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IEEE/CAA Journal of Automatica Sinica

• JCR Impact Factor: 11.8 , Top 4% (SCI Q1) CiteScore: 17.6 , Top 3% (Q1) Google Scholar h5-index: 77， TOP 5

Internet of Things for the Future of Smart Agriculture: A Comprehensive Survey of Emerging Technologies

Doi:  10.1109/jas.2021.1003925.

• Othmane Friha 1 ,  , 
• Mohamed Amine Ferrag 2 ,  , 
• Lei Shu 3, 4 ,  ,  , 
• Leandros Maglaras 5 ,  , 
• Xiaochan Wang 6 , 

Networks and Systems Laboratory, University of Badji Mokhtar-Annaba, Annaba 23000, Algeria

Department of Computer Science, Guelma University, Gulema 24000, Algeria

College of Engineering, Nanjing Agricultural University, Nanjing 210095, China

School of Engineering, University of Lincoln, Lincoln LN67TS, UK

School of Computer Science and Informatics, De Montfort University, Leicester LE1 9BH, UK

Department of Electrical Engineering, Nanjing Agricultural University, Nanjing 210095, China

Othmane Friha received the master degree in computer science from Badji Mokhtar-Annaba University, Algeria, in 2018. He is currently working toward the Ph.D. degree in the University of Badji Mokhtar-Annaba, Algeria. His current research interests include network and computer security, internet of things (IoT), and applied cryptography

Mohamed Amine Ferrag received the bachelor degree (June, 2008), master degree (June, 2010), Ph.D. degree (June, 2014), HDR degree (April, 2019) from Badji Mokhtar-Annaba University, Algeria, all in computer science. Since October 2014, he is a Senior Lecturer at the Department of Computer Science, Guelma University, Algeria. Since July 2019, he is a Visiting Senior Researcher, NAULincoln Joint Research Center of Intelligent Engineering, Nanjing Agricultural University. His research interests include wireless network security, network coding security, and applied cryptography. He is featured in Stanford University’s list of the world’s Top 2% Scientists for the year 2019. He has been conducting several research projects with international collaborations on these topics. He has published more than 60 papers in international journals and conferences in the above areas. Some of his research findings are published in top-cited journals, such as the IEEE Communications Surveys and Tutorials , IEEE Internet of Things Journal , IEEE Transactions on Engineering Management , IEEE Access , Journal of Information Security and Applications (Elsevier), Transactions on Emerging Telecommunications Technologies (Wiley), Telecommunication Systems (Springer), International Journal of Communication Systems (Wiley), Sustainable Cities and Society (Elsevier), Security and Communication Networks (Wiley), and Journal of Network and Computer Applications (Elsevier). He has participated in many international conferences worldwide, and has been granted short-term research visitor internships to many renowned universities including, De Montfort University, UK, and Istanbul Technical University, Turkey. He is currently serving on various editorial positions such as Editorial Board Member in Journals (Indexed SCI and Scopus) such as, IET Networks and International Journal of Internet Technology and Secured Transactions (Inderscience Publishers)

Lei Shu (M’07–SM’15) received the B.S. degree in computer science from South Central University for Nationalities in 2002, and the M.S. degree in computer engineering from Kyung Hee University, South Korea, in 2005, and the Ph.D. degree from the Digital Enterprise Research Institute, National University of Ireland, Ireland, in 2010. Until 2012, he was a Specially Assigned Researcher with the Department of Multimedia Engineering, Graduate School of Information Science and Technology, Osaka University, Japan. He is currently a Distinguished Professor with Nanjing Agricultural University and a Lincoln Professor with the University of Lincoln, U.K. He is also the Director of the NAU-Lincoln Joint Research Center of Intelligent Engineering. He has published over 400 papers in related conferences, journals, and books in the areas of sensor networks and internet of things (IoT). His current H-index is 54 and i10-index is 197 in Google Scholar Citation. His current research interests include wireless sensor networks and IoT. He has also served as a TPC Member for more than 150 conferences, such as ICDCS, DCOSS, MASS, ICC, GLOBECOM, ICCCN, WCNC, and ISCC. He was a Recipient of the 2014 Top Level Talents in Sailing Plan of Guangdong Province, China, the 2015 Outstanding Young Professor of Guangdong Province, and the GLOBECOM 2010, ICC 2013, ComManTel 2014, WICON 2016, SigTelCom 2017 Best Paper Awards, the 2017 and 2018 IEEE Systems Journal Best Paper Awards, the 2017 Journal of Network and Computer Applications Best Research Paper Award, and the Outstanding Associate Editor Award of 2017, and the 2018 IEEE ACCESS. He has also served over 50 various Co-Chair for international conferences/workshops, such as IWCMC, ICC, ISCC, ICNC, Chinacom, especially the Symposium Co-Chair for IWCMC 2012, ICC 2012, the General Co-Chair for Chinacom 2014, Qshine 2015, Collaboratecom 2017, DependSys 2018, and SCI 2019, the TPC Chair for InisCom 2015, NCCA 2015, WICON 2016, NCCA 2016, Chinacom 2017, InisCom 2017, WMNC 2017, and NCCA 2018

Leandros Maglaras (SM’15) received the B.Sc. degree from Aristotle University of Thessaloniki, Greece, in 1998, M.Sc. in industrial production and management from University of Thessaly in 2004, and M.Sc. and Ph.D. degrees in electrical & computer engineering from University of Volos in 2008 and 2014, respectively. He is the Head of the National Cyber Security Authority of Greece and a Visiting Lecturer in the School of Computer Science and Informatics at the De Montfort University, U.K. He serves on the Editorial Board of several International peer-reviewed journals such as IEEE Access , Wiley Journal on Security & Communication Networks , EAI Transactions on e-Learning and EAI Transactions on Industrial Networks and Intelligent Systems . He is an author of more than 80 papers in scientific magazines and conferences and is a Senior Member of IEEE. His research interests include wireless sensor networks and vehicular ad hoc networks

Xiaochan Wang is currently a Professor in the Department of Electrical Engineering at Nanjing Agricultural University. His main research fields include intelligent equipment for horticulture and intelligent measurement and control. He is an ASABE Member, and the Vice Director of CSAM (Chinese Society for Agricultural Machinery), and also the Senior Member of Chinese Society of Agricultural Engineering. He was awarded the Second Prize of Science and Technology Invention by the Ministry of Education (2016) and the Advanced Worker for Chinese Society of Agricultural Engineering (2012), and he also gotten the “Blue Project” in Jiangsu province young and middle-aged academic leaders (2010)

• Corresponding author: Lei Shu, e-mail: [email protected]
• Revised Date: 2020-11-25
• Accepted Date: 2020-12-30
• Agricultural internet of things (IoT) , 
• internet of things (IoT) , 
• smart agriculture , 
• smart farming , 
• sustainable agriculture

Proportional views

通讯作者: 陈斌, [email protected].

沈阳化工大学材料科学与工程学院 沈阳 110142

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• We review the emerging technologies used by the Internet of Things for the future of smart agriculture.
• We provide a classification of IoT applications for smart agriculture into seven categories, including, smart monitoring, smart water management, agrochemicals applications, disease management, smart harvesting, supply chain management, and smart agricultural practices.
• We provide a taxonomy and a side-by-side comparison of the state-of-the-art methods toward supply chain management based on the blockchain technology for agricultural IoTs.
• We highlight open research challenges and discuss possible future research directions for agricultural IoTs.
• Copyright © 2022 IEEE/CAA Journal of Automatica Sinica
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• Figure 1. The four agricultural revolutions
• Figure 2. Survey structure
• Figure 3. IoT-connected smart agriculture sensors enable the IoT
• Figure 4. The architecture of a typical IoT sensor node
• Figure 5. Fog computing-based agricultural IoT
• Figure 6. SDN/NFV architecture for smart agriculture
• Figure 7. Classification of IoT applications for smart agriculture
• Figure 8. Greenhouse system [ 101 ]
• Figure 9. Aerial-ground robotics system [ 67 ]
• Figure 10. Photovoltaic agri-IoT schematic diagram [ 251 ]
• Figure 11. Smart dairy farming system [ 254 ]
• Figure 12. IoT-based solar insecticidal lamp [ 256 ], [ 257 ]

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• IEEE Standards
• IEEE Spectrum

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Tutorial papers, review papers.

IoT reliability: a review leading to 5 key research directions

• Review Paper
• Open access
• Published: 07 August 2020
• Volume 2 , pages 147–163, ( 2020 )

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• Samuel J. Moore   ORCID: orcid.org/0000-0003-3205-3310 1 ,
• Chris D. Nugent 1 ,
• Shuai Zhang 1 &
• Ian Cleland 1  

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The Internet of Things (IoT) is rapidly changing the way in which we engage with technology on a daily basis. The IoT paradigm enables low-resource devices to intercommunicate in a fully flexible and pervasive manner, and the data from these devices is used for decision-making in critical applications such as; traffic infrastructure, health-care and home security, to name but a few. Due to the scarce resources available in these IoT devices, being able to quantify the reliability of them is a critical function. This report presents a detailed evolution of the area of reliability measurement, followed by an in-depth review of the state-of-the-art for quantification of reliability in the IoT, revealing the many challenges associated with this task. From this in-depth review, a set of key research directions for IoT reliability is determined. Despite the critical nature of the research area, at this current moment, this study is the first detailed review available in the area of assessing IoT reliability.

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Avoid common mistakes on your manuscript.

1 Introduction

Computing has been the fastest growing field of the last century. Computing systems now pervade the fabric of our everyday lives. We cannot make a purchase from a store, withdraw money from our bank accounts or visit a hospital without interacting with a computing system. Computing systems are now relied upon for many mission-critical systems, such as aircraft control systems, military systems and nuclear power plants. With the criticality of our computing systems in mind, it is vital that there are methods in place to ascertain the reliability of such systems. One of the fastest-growing fields within computing is the Internet of Things (IoT). The IoT is expected to grow to an immense size over the next number of years. In 2011 Cisco predicted that there would be 50 billion devices connected to the IoT by 2020 (Evans 2011 ). These huge claims have also triggered predictions of monetary investments reaching into the trillions by 2020 (Rayes and Salam 2016 ). While these numbers suggest a truly rapid growth in IoT, there are still many research challenges which must be solved for IoT to become fully integrated into our day-to-day lives. These barriers include trust, security, interoperability, reliability, scalability, performance, availability and mobility (Al-Fuqaha et al. 2015 ; Wang 2018 ; Ahmed et al. 2017 ; Saini 2016 ). These areas represent significant research challenges that must be addressed if we are to allow IoT to become the ubiquitous technology that it has set out to become (Sicari et al. 2016 ). If the vision of IoT is to be fully implemented in our homes, cities and workplaces then we will be trusting intelligent systems to make thousands of decisions daily that will have profound impact on our lives, through applications such as; home security (Ghorbani and Ahmadzadegan 2017 ), providing healthcare services to patients (Da Li et al. 2014 ) and monitoring critical traffic infrastructure (Singh et al. 2014 ).

The IoT is tasked with considering devices which may be extremely constrained by nature (Chiang and Zhang 2016 ). Considering that the IoT will be responsible for managing key infrastructure such as traffic lights, critical health systems and home security, it is easy to appreciate how the impact of unreliable IoT infrastructure may affect the decision-making of the system in a potentially severe or fatal manner (Fekade et al. 2017 ). The reliability issue does not end at the device and hardware layer either, there is also the consideration of the reliability of the network layer. This can often be difficult to determine due to the heterogenous nature of the devices connected to it and how they transmit data, often wirelessly over lossy links. Beyond the data transmission, there is also the issue of actuation to be considered. This raises an important question, how can we be assured that decisions are taken by the system based on robust information, given the challenges at the lower layers of the architecture? There must also be mechanisms in place to determine the accuracy of the decision-making models that determine the actuation of the system. Incorrect decision-making at this level could potentially be life-threatening for end-users, making this a key research issue (Sato et al. 2016 ). The vulnerabilities of IoT devices are becoming a prominent issue in the consumer and government industries. In October 2018, the UK government issued a set of guidelines describing minimum standards for smart-home devices, in order to protect the consumer (DDCMS 2018 ). This is demonstrative that IoT system reliability is something that will need to be addressed comprehensively in order for the technology to fully mature. If we are able to successfully quantify the reliability of our IoT infrastructure and which applications can avail of its service, this will then allow us to use the quantified reliability metric to reason about the fitness for purpose of our critical IoT infrastructure.

This report represents a novel in-depth study of reliability in relation to IoT, from first examining the fundamentals of reliability in engineering; these principles are then applied to reliability in computing and IoT. An exhaustive literature review, which to the best of the authors’ knowledge is the first of it’s kind, is also included in this report. This is followed by a summary of the current state-of-the-art research into reliability of IoT. Finally, this report proposes a novel set of research directions which are crucial in the advancement of IoT reliability, based upon the findings of the in-depth study

The rest of this paper is organised as follows: Section  2 describes the method and process used to perform this research. Section  3 is an overview of the meaning and origins of reliability engineering and how it applies to computing. Section  4 is a detailed view of reliability in IoT, and the challenges that make the quantification of reliability difficult in IoT. Section  5 is a detailed literature review of current research into quantifying reliability in IoT systems. Section  6 then discloses the five key research directions gleamed from the in-depth study conducted in this work, followed by the conclusion of the work in Sect.  7 .

2 Methods and processes

At the time of writing, to the best of the authors’ knowledge, this work represents the first work to review and summarise the practice of quantifying and measuring reliability in the IoT. This indicates that, in general, the area of reliability quantification in the IoT is as yet under-researched, and would benefit from an in-depth review. Moreover, in order to guide the efforts of future researchers who may apply the knowledge herein to their own research projects, it would be of benefit for this in-depth review to summarise the key knowledge points, and then synthesise them into a list of key research directions. Crucially, for a piece of work such as this, there must be a process in place in order to ensure that the research is carried out in a robust and reliable manner. As such, this section details the three-stage methodology that was used to perform this research. This methodology aims to support the research in achieving the following contributions to knowledge:

Define reliability in the IoT through researching state-of-the art in other well-established disciplines, such as biomedical engineering and engineering.

Using this reliability information from contribution 1, apply it to the scope of the IoT, detailing the key requirements for IoT reliability

Perform an in-depth literature review surveying the state-of-the-art in IoT reliability quantification

Analyse the current state-of-the-art research and derive key research directions for the field.

2.1 Defining reliability in the IoT

Given the nascent nature of this field, as described earlier in this section, it becomes necessary to formulate a definition for the IoT. While reliability is a mature field in the related disciplines of engineering (Bradley 2016 ) and software engineering (Xie et al. 2004 ), there is no agreed upon definition for the IoT. As such, it becomes necessary to review the core principles of reliability in these other domains. Highly cited and relied upon publications were selected from the industries of engineering, biomedical engineering and computing which described the practice of reliability definition and quantification. From these works, a clear definition of reliability is then developed. This core definition of reliability is then taken and applied against the backdrop of the IoT. This ensures that the application of any reliability definition is firmly grounded in highly cited academic and scientific articles.

The definition of IoT reliability is then further expanded, and viewed in an end-to-end sense, using the core physical architecture of the IoT as a structure to discuss the applications of reliability in the IoT.

2.2 Selection of works

With reliability then firmly defined in a wider sense, then narrowed into an IoT-specific scope and definition, works were then selected to demonstrate the current state of the art in performing reliability analysis in the IoT. In order to ensure that the works included in this review were of significant scientific standard, the following steps were applied in sourcing and including the literature:

Wide searches were conducted in the following databases; IEEE Xplore, ACM Digital Library and Google Scholar (including Springer and Elsevier).

Search terms used were “IoT”, “Reliability”, “Reliable”, “Trust”, “Dependability”, “Quality” (in any combination) in the title of the work.

Prior to 2010, IoT was a seldom-used term in research literature, as such, works before 2010 were excluded before to ensure currency.

Works were then excluded which did not address some method of quantifying, measuring or directly aiming to assess or improve reliability in the IoT.

2.3 Analysis and research directions

Once the works were selected, each of them were reviewed and a detailed summary presented detailing their contributions to the field of IoT reliability research. This information is then synthesised into benefits and shortcomings, which are analysed and presented in a summary table.

Finally, the analysis of each of these surveyed works was used to derive a set of research directions for the research area. Given that, at the time of writing, this review paper is the first to specifically study reliability in the IoT, this research output represents a trusted and evidenced roadmap to improving reliability in the IoT.

3 The science of reliability

Reliability, at a fundamental level, is concerned with the study of failures (Fries 2006 ). More specifically, it is concerned with how failures are caused, how they can be addressed and how they can be prevented. There are many misconceptions regarding what reliability actually represents. It is not as simple as testing and re-testing a device until relative satisfaction is reached. Reliability can be represented by a formal definition which includes four key requirements. Fries ( 2006 ) defines these requirements of reliability by stating that devices must be able to; perform a required function, perform without failure, perform under stated conditions and operate for a specific period of time. Therefore, the specification for reliability requires that we fully identify the expected conditions of use, what constitutes proper function and what constitutes a failure (Mavrogiorgou et al. 2018 ). The remainder of this section will cover the key areas in establishing reliability engineering. First, a definition is presented to illustrate the difference between two often misused terms; quality and reliability. Then basic failure patterns are discussed, and how they impact new services. Finally, a description of reliability as it applies to the field of computing is presented, alongside standard metrics used to quantify reliability in computing.

3.1 Quality versus reliability

The terms “quality” and “reliability” are often misunderstood. Sometimes, these terms are used interchangeably, however, there are important distinctions between the two terms. Both terms exist to describe a characteristic of a product or system. Fries ( 2006 ) determines that the main difference between these two terms as being the temporal nature of quality. The term “quality”, as defined in ISO 9000 (ISO 2015 ) is the “ability to consistently provide products and services conforming to their requirements”. In this definition of quality, there is no stipulation of a time period for which these requirements must be met or continue to be met in the future. A quality test, therefore, reflects only a snapshot of a particular time at which quality requirements are either met or not met. Reliability, however, refers to the performance of a system or product over a specific window of time. This is an important distinction between the two terms, especially when it comes to assuring continued adequate performance of a device or system over time. In essence, we can evaluate quality in the IoT, however, this will not offer us any assurance in the continued successful operation of the deployment. A product or system can be designed and released to a very high standard of quality, however, this will not provide any information with regards to how often the product fails. Moreover, we cannot use quality to ascertain the probability that the system or product will be operating without fault at a given time. Quantified reliability measures, on the other hand, allow us to ascertain vital pieces of information regarding the up-to-date operational state of the IoT deployment. These pieces of information can include, how often a device fails, the average interval between failures, the average time taken to repair a component and the probability that a component will need to be replaced by a certain date.

The three common failure patterns; infant mortality, constant (steady) failure rate and wearout

3.2 Common failure patterns

There are three main patterns of failure which are defined in the field of reliability engineering; infant mortality, constant failure rate and wearout failure (Bradley 2016 ). Infant mortality refers to failures that occur predominantly early in the lifecycle of a product and gradually are neutralised over time. Wearout failure patterns are observed when a device begins to exhibit an exponentially higher number of errors compared to a previously consistently low number of errors. This failure pattern indicates that a device is nearing the end of its useful life period (Fries 2006 ). The constant failure rate describes a pattern where the number of errors within a given period of time remains constant. For example, for a device to have a constant failure rate we would expect that the same total count of errors to occur in each calendar month, though these do not necessarily need to occur at the same times each month. These three error patterns are characterised in Fig.  1 , the dotted vertical lines mark the beginning and the end of the useful life period where the failure pattern is at a constant rate.

3.3 Reliability in computing

There are many ways of assessing reliability in computing. The most appropriate method may depend upon the nature and function of the system being assessed. Reliability should be a quantitative measure which broadly represents the ability for a computer system to perform its intended function (Xie et al. 2004 ). Xie et al. ( 2004 ) outline several key metrics that help to define reliability in computing. Mean Time to Failure (MTTF), closely related to Mean Time Between Failures (MTBF) is the expected lifetime that the system will operate normally before a failure occurs. The failure rate function, also known as the hazard function, is a metric that helps to define the rate of system aging. The failure rate is the probability that a device will fail within a specified window of time. The failure rate function when used to evaluate hardware would be expected to follow an exponential distribution, which thereby allows us to reason about the aging and deterioration of the hardware. When used in software, however, the failure rate would remain constant because software does not age or deteriorate physically. Maintainability, according to Xie et al. ( 2004 ), is a metric that represents the probability that a failed system can be returned to normal operation within a given period of time. Availability is a metric representing the probability that a system will be operating as normal in a given period of time. Availability and maintainability are closely related, however, they differ in one key aspect: availability concerns the period of time in which a system is expected to be operating normally, whereas maintainability concerns the period of time in which a fault has occurred. Of course, beyond this technical definition, maintainability also is concerned with the continuing and ongoing operation of a system - this may relate to such tasks as meeting new requirements, refactoring and restructuring code and other maintenance tasks which contribute towards maximising the useful life period of a system.

Within the study of computing reliability there are four key areas which have different approaches in establishing reliability; hardware, software, network and system (Xie et al. 2004 ). These different areas each have reliability analysis methods that are uniquely suited to the requirements and issues in the area. Hardware reliability concerns the reliability over time for the physical components of a computer system, such as CPU, disk and sensors. These components are prone to wear and tear, and therefore we would expect the reliability to reduce over time for these components. Software components, on the other hand, should not be subject to physical wear and tear, therefore we would not expect to see a decay in reliability over time (Mavrogiorgou et al. 2018 ). Network reliability concerns the network performance over a given period of time, which is determined by a blend of hardware and software. Systems reliability is a combination of all the components combined, and there are specialised techniques to analyse this.

Reliability quantification methods in computing are well established and understood, as presented in this section. With the onset of the novel IoT paradigm, however, it is important that we formulate IoT-centric reliability quantification methods. These methods must suit the unique nature and constraints of IoT, which is discussed in the following section.

The four layers of the IoT architecture; cloud layer, service management layer, fog layer, and device layer

4 Reliability in the Internet of Things

The definition for IoT is fundamental in understanding the problem of reliability within the paradigm. The definition of IoT is often under-represented and ill-defined (Atzori et al. 2017 ). Often, the IoT is crudely defined as being able to add internet connectivity to every-day devices, in effect allowing “your toaster to talk to your fridge” . While this statement is true for some part of the IoT, it does not encompass the whole paradigm of the IoT. A useful starting point in defining the IoT paradigm is by considering the key components of IoT. These components are; sensing, actuating, communication, services and applications (Rayes and Salam 2016 ). These four components can then be mapped to an architecture for the IoT, as presented in Fig.  2 . Sensing and actuating are carried out at the lowest layer of the architecture, also referred to as the device layer. The next layer up, the edge layer, enables the communication between the devices and the application layer. Typically, this communication is enabled by semi-capable devices behaving as hubs, collecting data from the sensors and relaying it into the cloud and sending commands to the actuators as necessary.

With the key components of IoT in mind, we can now form a full definition of IoT, which is a paradigm which enables interconnectivity in anything and everything to create monitoring and control infrastructure which can be used in applications to enrich everyday user experience (Rayes and Salam 2016 ).

To begin understanding the problem space of reliability in IoT, it is best to use the architecture, as presented in Fig.  2 , as a reference point. We can then observe reliability issues in each of the layers of the architecture and understand how they contribute to the problem.

4.1 Device reliability

From a device perspective, that is the sensors and actuators, the first problem we can observe is the highly constrained nature of these devices (Al-Fuqaha et al. 2015 ). These constraints concern battery, memory and computational capacity (Kouicem et al. 2018 ). Battery is a concern for IoT applications, because often the application layer is unaware of the remaining battery left on the device thereby making it difficult to determine when the device requires a battery replacement (Shi et al. 2016 ). This battery life concern is further compounded when we consider that devices may be located in places that are physically difficult or dangerous to reach to replace. The memory and CPU constraints on the devices limit the device’s ability to store complex encryption methods, meaning that IoT devices must rely on lightweight encryption to protect the data being transmitted by the device (Rayes and Salam 2016 ; Alaba et al. 2017 ).

Another issue evolves from the constrained nature of the devices when it comes to updating the limited firmware of these low-powered sensors. It is impractical, due to the lack of power and implications on battery life for the device, to connect to a cloud service routinely and check if new firmware needs to be downloaded and installed on the device (Chiang and Zhang 2016 ; Yaqoob et al. 2017 ; Allhoff and Henschke 2018 ). This leads to a scenario where devices could potentially be operating with outdated firmware, thereby leaving them vulnerable to security breaches.

The sensors and actuators that are used in the IoT are often deployed in remote and distant locations, and can often be subject to harsh environmental conditions such as heat, freezing temperatures, mechanical wear, vibration, and moisture (Rayes and Salam 2016 ). As discussed in Sect.  2 in this report, there is a need to determine the “useful life” period of a device, so that we can determine when the device needs to be retired. This useful life will shorten if the device is employed in a harsh environment, therefore, we could expect to see great variances of device lifetime for identical devices deployed in different environments, which results in the system reliability being difficult to manage.

Another concerning aspect with regard to device reliability in IoT, is the propensity for sensors to “fail-dirty” (Karkouch et al. 2016 , 2017 ). This phenomenon concerns a scenario where a sensor continues to send erroneous readings after having suffered a failure. This is a well-known, yet little understood, problem that is pervasive in IoT environments. In particular, this issue is hard to diagnose because the sensor appears to be operating normally. The impact of a false reading being sent in an IoT environment can be critical, when we consider that actuation often has physical impact on human lives.

4.2 Communication and network reliability

Mobility is one of the key expectations of an IoT network (Al-Fuqaha et al. 2015 ), whereby users of the network can dynamically move between applications while the device onboarding and identification happens seamlessly in the background. Global addressing, however, is a difficulty in IoT (Rafferty et al. 2018 ), given that manufacturers do not co-ordinate to provide globally unique identifiers for all IoT devices. This means that the responsibility of assigning unique identification resides within the IoT network itself. When we consider that IoT devices are expected to be mobile, this creates a problem given that the device ID might differ across different networks, meaning that we might lose traceability of the device. This then introduces a reliability concern when it comes to tracking or auditing the device as it moves through different IoT applications.

Internet Protocol (IP) is the current de-facto standard for communication and identification in traditional networks. IP in its current state is, however, not well suited to the IoT (Tsai et al. 2014 ). Introducing new protocols into this problem space will require these new protocols to mature quickly, which is not always easy. This problem is exacerbated further when we consider the implications of unique addressing. IPv4 has a 32-bit length address, which creates room for 4.3 billion addresses, keeping in mind the predictions of 50 billion devices discussed previously in this paper, it becomes clear that IPv4 is not suitable to fulfil the vision of IoT. This problem is further compounded by the fact that IPv4 ran out of addresses in 2010 (Evans 2011 ). As such, it becomes necessary to implement a protocol with suitable addressing space, such as IPv6, which boasts an address space of 128 bits, allowing space for

addresses. This new addressing space, however, creates problems for constrained devices, not all of which are capable of handling the overheads required for the address (Al-Fuqaha et al. 2015 ).

A remedy to this large address overhead is offered by the 6LoWPAN protocol (Kushalnagar et al. 2007 ). 6LoWPAN is able to compress the header size of the IPv6 packets in order to make them compatible with the IEEE 802.15.4 standard (Al-Fuqaha et al. 2015 ), and thus better suited to the IoT. These new and emerging standards to cope with the new requirements of the IoT contributes to the creation of a landscape of disparate standards and protocols among IoT devices and deployments targeted for communication for constrained devices in IoT networks. Given the lightweight and constrained nature of some of these protocols, not all of them feature quality of service (QoS) guarantees, meaning that the reliability of the network connection becomes harder to assess.

Karkouch et al. ( 2016 ) indicate in their study that due to the scarce resources and intermittent communication, the network is liable to drop readings, or produce unreliable readings. This notion that readings can be dropped due to the inherent nature of IoT networks is an area for concern, especially given that IoT infrastructure is often responsible for managing mission-critical applications (Fekade et al. 2017 ).

4.3 Application layer reliability

The application layer of the IoT paradigm is not subject to the same constraints of either the network or the device layer of the architecture. It is important to note that in many cases the reliability of the application layer is a function of how reliable the lower layers of the architecture are. If anomalous data is sent from the device through the network into the application layer, this will reduce the reliability of the application. In this regard, it is important that the application layer has sufficient anomaly detection techniques to eradicate errors and maintain the reliability of the application. Given that IoT networks feature a heterogeneous range of constrained devices transmitting many pieces of information in different formats, this task can be difficult (Abeshu and Chilamkurti 2018 ).

While the application layer doesn’t suffer from the physical constraints of the device layer, there is still a need to manage the reliability of the applications that are being deployed. A study Moore et al. ( 2019 ) which observed the impact of anomalous data on classification in the IoT application of human activity recognition found that some classifiers were considerably more vulnerable to errors than others, and that the preparation method of the data can also make the application more vulnerable to failure. With this in mind, developers must make a conscious effort to establish and understand the reliability of applications being hosted in IoT infrastructure, in order to prevent critical errors from creeping into the system.

4.4 Toward an effective solution for reliable IoT systems

The issues of reliability at the three levels of the architecture in IoT combine to create a vulnerable landscape for IoT, which often leads to anomalous data being generated and sent through the network. This notion demonstrates a strong need for effective, quantifiable reliability measures that will allow us to reason about the fitness for purpose of our IoT systems. The issue of anomalous data is highly problematic to the IoT vision, given that actuations will be made on the basis of this data which could, in the most severe cases, threaten human lives (Fekade et al. 2017 ). Therefore, it is essential that any framework that aims to assess and quantify reliability in IoT must be able to detect the presence of anomalies in the system. Once reliability has been quantified, this opens up a new opportunity to further enhance the robustness of the system by placing a human in the loop (HITL). HITL is an essential ingredient for the future of reliability in IoT, and was identified as a key future research area in Stankovic ( 2014 ). The HITL paradigm opens up opportunities for detecting and resolving reliability issues in critical IoT infrastructure. The use of a human observer brings an element of domain expert knowledge to the application, which allows the human to synthesise information presented by the system with their own expert knowledge to come to an informed conclusion about the reliability of the system. Furthermore, humans enable the assessment of ground truth, such as a true temperature value, which can help to verify a machine reading. This concept is relatively novel in IoT research and, to date, no reliability studies have opted to use a HITL method to assist in reliability assessment in combination with classical reliability models. This novel combination would be a step towards a new and effective solution.

This section reviewed the key concepts and requirements for IoT reliability. The vulnerable landscape which the IoT occupies presents a clear requirement for the research community to design and implement frameworks and solutions which might aid in assessing and understanding reliability for our key IoT infrastructure.

5 Current research toward IoT reliability quantification

The definition of reliability, as discussed in the previous sections, has a strong element of quantification associated with it. Reliability, as defined in Sect.  3 , is not a subjective science, and therefore mechanisms aiming to assess reliability should be objective and quantifiable in their nature. There is also a heavy focus within reliability in defining and using metrics to assess the reliability of components and systems. Research in the area of IoT reliability has been conducted to enhance reliability at various levels of the IoT architecture. This section summarises the research available in the areas of device reliability, data quality, network reliability and anomaly detection, all of which represent key areas for improvement of IoT reliability.

5.1 Device reliability

Several authors researching IoT device reliability integrated classical reliability metrics into IoT-centric solutions. Reliability, failure rate, availability, and MTTR were quantified by Zin et al. ( 2016 ). The work proposed a probabilistic model for measuring reliability in connected IoT devices positing that the failure structures of IoT devices adhere to a certain probability distribution. The authors define the reliability measure R(t) as being the probability that the device is operating correctly at time interval [0, t]. This probabilistic function allows estimation of the expected time to failure, availability and reliability for a given IoT device. Meanwhile, Mavrogiorgou et al. ( 2018 ), included Mean Time to Repair (MTTR), MTTF, MTBF, and availability metrics in their work, which proposed a mechanism for capturing the reliability of heterogeneous IoT devices. This mechanism considered both known and unknown device types and sought to differentiate between which devices were reliable and which were not, with the goal of collecting data from the reliable ones and discarding data from unreliable devices. The mechanism consisted of four stages: devices recognition, specifications classification, reliability estimation and reliability validation. Using this mechanism, the authors were able to build a ranking of connected fitness devices based upon their reliability results from known reliability metrics. Lastly, Kim ( 2016 ) used reliability, failure rate and recoverability in their study which proposed a weighted model to quantifying reliability in the IoT. The model consisted of four quality criteria; functionality, reliability, efficiency and portability. Metrics were defined within these criteria which were assigned weights so that the model could provide a total score for the quality of the IoT application. The model was then evaluated in a virtual environment and scores were produced for each of the metrics. This model provides weighting, however, each criterion was weighted evenly in this experiment. These classical metrics provide a useful starting point in the quantification of IoT reliability, but have not yet matured in capability and cannot attest to reliability across all levels of the IoT architecture.

Moving away from the classic well-defined reliability metrics, some non-standard reliability metrics have been designed and implemented in recent studies. Saini ( 2016 ) presented a model to evaluate trust factor and reliability over a period of time (ROPT) for IoT systems. Due to the notion that identical IoT sensors might be deployed in drastically different environments (i.e., exposed to varying levels of humidity, temperature, wind) these identical IoT sensors might exhibit different expected lifetimes. The author proposed that ROPT is calculated for every individual device and gateway in the IoT system in order to gain a full understanding of how reliable the system is. The author also presented a trust factor rating scale allowing us to reason on how some IoT applications require higher levels of trust, i.e. defence systems, and therefore higher levels of availability. This study only uses one metric to determine the reliability of the system, and cannot represent the entire picture of reliability in the IoT. Li et al. ( 2012 ) also proposed three non-standard reliability metric definitions to observe real time quality of data collected from devices in IoT environments. The study validates the implementation of these metrics by applying them to two real-world open source datasets. The three metrics defined were; currency, availability and validity. Implementing the metrics onto real-world datasets validated that it was possible to calculate these metrics in real-time, but this was not able to attest to the effectiveness of the applied metrics in identifying data quality issues in IoT.

A more complete framework for managing quality and reliability is proposed by Sicari et al. ( 2016 ) and Sicari et al. ( 2014 ). This architecture is designed to quantify the security and quality of individual devices in IoT applications. The model used NOS (Networked Smart Objects) to extract metadata from IoT nodes in a network (Rizzardi et al. 2016 ). The parameters extracted from a security perspective were confidentiality, integrity, privacy and authentication. The collected parameters for quality were accuracy, precision, timeliness and completeness. Each parameter was attributed an index score ranging from zero to one, which reflected the effectiveness of the node with regard to that parameter. The model was tested using Raspberry Pis and sensors from a meteorological station and it was successfully able to calculate the specified parameters. This model concerns the data quality characteristics of IoT nodes though the quality metadata, which is not sufficient in describing the holistic reliability of an IoT system. The security metadata provides some insight into how secure a given node is in an IoT system, this could be enhanced by adding anomaly detection.

The research presented in this section is valuable in aiding the understanding of how reliable and prone to failure the devices in our IoT infrastructure are. These pieces of research help form an understanding of how some of this information can be quantified, using metrics like availability, MTBF and MTTR. Nevertheless, the quantification of hardware reliability is only one step in the solution to overall IoT reliability. These research studies are unable to attest to reliability at the network level or make an assessment about the likelihood of the system providing anomalous data or falling victim to a spreading threat.

5.2 Network reliability

Beyond being able to reason about the fitness of our IoT devices, we must also be able to attest to the reliability of the network infrastructure that forms the backbone of IoT communication. Generally speaking, there are two forms of network reliability studies which are discussed in this section; studies for enhancing QoS in networks, and studies aimed at quantifying reliability metrics for networks. This section presents the current state-of-the-art research in IoT networks reliability.

A novel IoT network QoS metric was proposed by Maalel et al. ( 2013 ) in their work, which designed a lightweight and energy efficient routing protocol to enhance and measure reliability in IoT applications, specifically emergency applications. Emergency applications in the IoT require a rapid response for alarms that have been raised. The work proposed a mechanism called AJIA (Adaptive Joint Protocol based on Implicit ACK) for packet loss and route quality evaluation. The mechanism relies upon the broadcast nature of the protocol, where messages are broadcast to all nearby nodes. The nearby nodes can therefore “overhear” the message being sent. This overhearing function is used rather than traditional ACK messages to ensure reliability of the message being sent. The links between nodes are then evaluated with a metric called Link Quality Indicator (LQI), which uses the history of packet loss in the link to determine the reliability of that particular path. Other QoS metrics, such as delay throughput, and packet loss, were quantified by Kamyod ( 2018 ). This work employed Riverbed’s Optimized Network Engineering Tools (OPNET) to observe these network reliability parameters in a smart agriculture scenario. These parameters were monitored so that they might provide some information as to how reliable the overall end-to-end IoT system was. The study found that increasing the number of nodes in the network saw longer packet delays and significantly longer transmission times and packet loss. Brogi and Forti ( 2017 ) proposed a general model for a QoS-aware IoT infrastructure, based on the fog computing paradigm. The model allows IoT applications to generate QoS profiles in order to request certain QoS characteristics from the Things it interacts with. Each communication link in the IoT system has an associated QoS profile, which allows the model to determine the potential latency and bandwidth for an application to things communication. The model only considers latency and bandwidth, which is a limited subset of QoS characteristics which would not fully represent the reliability of the network at a given point in time.

Further IoT network QoS metrics, embedded in a management framework, were examined in a study by Al-Masri ( 2018 ), which presented a microservices QoS management framework (mQoSM) for use in Industrial IoT (IIoT), which is a QoS-aware middleware that monitors the behavior of microservices in order to determine the “best” microservice amongst all discovered microservices. This information can then be used by IoT architects to decide if they wish to integrate the microservice. This framework monitors the following parameters; response time, throughput, availability, reliability and cost. The model presents a useful step towards generating a situational awareness of the IoT system with regards to reliability and performance, however, it has not been scaled up beyond microservices in an IoT environment.

An approach of reliability modelling using Generalised Stochastic Petri Net (GSPN) was proposed by Li and Huang ( 2017 ). This approach theorised mathematical models at edge nodes to provide statistics on the performance of IoT devices. The metrics calculated were time consumption, response time, failure rate and repair times. These metrics only speak to the performance of the device to edge layer and offer a very limited view of network performance which does not present a holistic view of IoT reliability. A gateway redundancy model was proposed by Sinche et al. ( 2018 ). This work made use of redundancy at both the ISP (Internet Service Provider) level and Gateway (edge node) level. This model tested three cases, an IoT infrastructure with no redundancy, an IoT with gateway redundancy and an IoT with gateway and ISP and gateway redundancy. The model was tested using a physical IoT testbed, wherein the devices were communicating using the I2C bus protocol. RTT (return trip time) was used as the performance metric to determine the effectiveness of the model. The results shown in the study found that the model which did not use the redundancy approach saw the RTT increase by 14% during fault conditions, whereas the redundancy models resulted in only a 1% increase in RTT. This study considers reliability at the network and cloud level only. Therefore, it does not consider the reliability of the physical devices, or their propensity to fail at any given time. This study also does not consider the heterogenous nature of IoT communication protocols. Alam ( 2018 ) presented a framework to handle reliability issues in IoT based on the TCP (Transmission Control Protocol). There are three components to the framework; the reliability calculator, the reliability controller and the reliability handler. The framework uses delay to determine the failure-state of the IoT system. If high levels of delay are observed by the reliability calculator, the reliability controller will attempt retransmission and the reliability handler will initiate a broadcasting mode and enter a power-saving state. This framework only deals with the delay QoS metric in IoT, thus it cannot represent the full state of reliability in the network.

The research presented in this section shows that while some attempts have been made to enhance reliability in IoT networks, both by enhancing the network’s QoS and by monitoring and quantifying network reliability, there is currently not a research approach which successfully combines device and network reliability into one framework.

5.3 System reliability

Some research has also been conducted to evaluate IoT reliability at a system level. These approaches are at a high level, and do not capture the individual detail for reliability, such as which devices are responsible for failures, or which parts of the network are responsible for traffic problems.

Behera et al. ( 2015 ) proposed a method of modelling reliability in a service oriented IoT. Specifically, algorithms were proposed to evaluate reliability in a Centralised Heterogeneous IoT Service System (CHISS). The authors proposed that reliability could be measured by modelling the availability of the program to run the service, the availability of input required for the service to run and the service reliability of subsystems associated with the system. The algorithms were tested on a case study of a fire alarm system, which was running under normal operation at the time. The algorithms were able to determine if the program and file was available for each component in the IoT system. This methodology did not, however, consider the notion that the IoT components could fail at any moment and begin sending anomalous data, or that the network could fall victim to a spreading threat or virus. In order to present a true reflection of reliability, it is necessary to have a mechanism which can alert the user to failures in the system before critical actuations are made.

Kharchenko et al. ( 2017 ) proposed the use of a Markov model to predict the reliability requirements of an IoT system. The Markov model considered that the application could be in a range of 15 states, from normal condition to complete failure. The probabilistic nature of the Markov model facilitates prediction that the system will move from one state to the next and can establish the probability of a failure at a given point in time. This model only considers the states specified in the design of the model and is not capable of reacting to new situations that were not catered for in the design of the model.

5.4 Anomaly detection

With the vulnerable state of IoT networks, given their constrained devices and highly mobile nature, it is essential that any framework which intends to quantify the reliability of an IoT infrastructure must have knowledge of the potential presence of anomalous data in its applications. This anomalous data could have severe consequences if left undiagnosed to be sent to the application layer and used in critical actuation situations. This section presents the current research on IoT anomaly detection. IoT-specific anomaly detection is a challenging area, because the solutions must be lightweight and capable of handling the heterogeneous range of IoT devices.

Spanos et al. ( 2019 ) proposed a smart-home anomaly detection method which combines statistical and machine learning techniques according the network behaviour of the device. During training, features are extracted from the network packet data, these features are then standardised and passed into a clustering algorithm. These clustered labels are then passed into ensemble classification methods, which determine the final result from soft-voting. The authors were able to detect mechanical exhaustion and physical damage to the devices. Nevertheless, more data and performance metrics are required here to determine if the model works at scale and with a wider set of devices.

Gonzalez-Vidal et al. ( 2019 ) examined methods to detect anomalies in IoT time-series data. Their process consisted of two steps; extract outliers and abnormal patterns using the individual time-series properties of the data, and then use the features extracted from these models to classify them from the annotated classes. For the time series anomaly detection model, the ARIMA and HOT-SAX frameworks were used, while Random Forest and Association Rule Mining methods were used in the classification component. The authors saw accuracies of up to 90% using their methods. This work is a valuable contribution in the area of sensor data-level anomaly detection, however, it is limited in that it requires time-series data to operate.

Stiawan et al. ( 2017 ) proposed a technique for early anomaly detection using network traffic analysis. This technique used the SNMP (Simple Network Mapping Protocol) to collect traffic from a heterogeneous range of IoT devices. This traffic was then visualised in graphs for further analyses. Thresholds could then be set based upon CPU and memory usage which can determine the presence of an anomalous communication in the network. This approach is lightweight and suited to the IoT, however, the solution does not include a method to automatically or statistically determine a threshold for failures, which could generate a high volume of false alarms.

Sedjelmaci et al. ( 2016 ) proposed an energy-efficient anomaly detection technique which caters for low-resource IoT devices. The technique uses a game theoretic methodology in order to reach the optimal energy efficiency by combining two known techniques for intrusion detection in IoT; signature-based detection and anomaly detection. The anomaly detection component learns activity and builds a classification rule, which is then passed to the signature detection component so that the next time the anomaly occurs it can be recognised by its signature rather than having to rerun the classifier to detect it. Game theory was then applied to this hybrid technique to create further energy savings, which opposes two “players” against each other, one being the attacker launching the new attack signatures and the other running the algorithm to detect anomalous new signatures. When the game finishes the historical data can be examined to determine the probability of a new signature and thus can state a time at which anomaly detection should be run to build new rules. The study compared the proposed lightweight game-theoretic technique to other known hybrid techniques in the research literature. The study found that accuracy was reduced in the game-theoretic technique, which was to be expected given the predictive nature of the technique. When comparing energy consumption, however, the study found that it was possible to save up to 6000 mJ of energy when running the lightweight technique, which represents a worthwhile energy saving given the low-resource nature of IoT.

Desnitsky et al. ( 2015 ) proposed a method for detecting anomalies in IoT applications using domain-specific knowledge to create a list of constraints for the application. For example, the temperature in a home should not exceed 30 degrees Celsius, or the constraints could be drawn from the history of the data, for example a motion sensor in an office stops providing data. If one of these constraints is exceeded this indicates the presence of an anomalous situation. This model is useful for detecting simple anomalous scenarios, however, it is entirely dependent upon the rule base which is designed by the domain-expert. This limitation means that if an anomaly is not accounted for in the constraints then it will not be detected.

Abeshu and Chilamkurti ( 2018 ) proposed a deep learning approach for detecting attacks based upon the fog-computing paradigm in IoT. Using the fog-computing paradigm can significantly reduce delays versus the traditional cloud centric paradigm, which is useful in mission-critical IoT scenarios. The study compared the performance of a deep learning model which used a pre-trained stacked autoencoder for feature engineering and SoftMax for classification against a shallow learning model. The study found that the deep model was consistently more accurate than the shallow model, on average this accuracy gap was 4% which is a large gap in a mission-critical application. Furthermore, the study revealed that the deep model coped with a scaling number of nodes much more comfortably than the shallow model, as when the shallow model was exposed to more than 80 fog nodes the accuracy fell by 2%.

Thanigaivelan et al. ( 2016 ) proposed an anomaly detection system for IoT where each node monitors the behaviour of its one-hop neighbours. The proposed system has three main components; the MGSS (Metrics and Grading Subsystem), the RSS (Reporting Subsystem) and the ISS (Isolation Subsystem). The MGSS is the component responsible for grading the neighbouring nodes, these nodes are graded based upon packet size and data rate. The RSS is responsible for reporting any nodes which are confirmed to be anomalous, which the ISS component will then isolate to remove the threat from the network. Further research is required within this solution in order to derive a more comprehensive list of network parameters to monitor, and a statistical method is needed to determine if a node is anomalous or not.

Nomm et al. ( 2019 ) proposed a method of detecting botnet attacks in IoT deployments. The method evaluated feature selection techniques to reduce the dimensionality of the data before passing it into a classifier. The dataset used in the experiment was a genuine dataset from a Mirai botnet attack, containing 115 discrete numerical features generated by 9 IoT devices. The features described various network characteristics, such as source and destination IP, jitter and socket information. The author used three different techniques to reduce the dimensionality of the data; entropy, variance and Hopkins statistics. Three classifiers were then used to classify the data; LOF (local outlier factor), one-class SVM (support vector machine) and an IF (Isolation Forest). The study found that feature reduction by entropy combined with the IF classifier was able to achieve accuracy results of 90% by using 5 features. This feature reduction is well suited to the IoT given that it is a much greener approach to machine learning, as opposed to a classifier having to train and test on 115 features. This anomaly detection technique is successfully able to detect anomalies at the network level but does not consider the anomalies that may occur in the payload of the packet being sent by the IoT devices themselves.

The papers reviewed here with regard to IoT anomaly detection represent a clear drive in the research community to create a more reliable IoT ecosystem. With this in mind, it should be stated that anomaly detection is an extremely large field, with application in IoT, network security and a vast array of other computing disciplines. Within the scope of this work, it is not possible to review all available anomaly detection methods, and as such, only the pertinent IoT examples are reviewed here in detail. A more detailed review of anomaly detection methods can be found within the literature (Zarpelão et al. 2017 ; Moustafa et al. 2019 ; Cook et al. 2020 ; da Costa et al. 2019 ).

Many methods were discussed in this section which provide accurate and varied mechanisms for detecting anomalies in IoT systems. Nevertheless, further research is required to determine how anomalies actually affect the reliability of an IoT system, given that the presence of an anomaly does not necessarily need to hinder or prevent IoT services from operating. This being said, the presence of anomalies is a clear indicator that the IoT system is not performing optimally.

5.5 Discussion of surveyed work

The range of research presented in this section demonstrates a growing demand for quantifying reliability in IoT networks. This is not a straightforward task, given that we must be able to assess reliability at both a device and at a network level whilst also being able to detect anomalies as they occur in the system. The research studies presented in this paper all only tackle one facet of the problem, as is evidenced in Table 1 which summarises the contributions of these works. A complete solution would need to be able to integrate all of this valuable IoT reliability information into one reliability framework. The research presented in this paper presents a clear gap in the knowledge and understanding of IoT: there is currently not a solution available capable of, in an end-to-end sense, assessing the reliability of IoT infrastructure.

From the works aimed at quantifying device reliability, there are several different contributions made. Some works, such as Mavrogiorgou et al. ( 2018 ), Zin et al. ( 2016 ) and Kim ( 2016 ) use standard reliability metrics to quantify the state of reliability in IoT devices. These standard metrics include MTTF, MTTR, Availability, Maintainability and Failure Rate (Fries 2006 ). When given enough device data, these metrics can be used to mathematically reason about the reliability of IoT devices. Some works, such as Saini ( 2016 ) and Li et al. ( 2012 ) proposed non-standard metrics, like ROPT, Trust Factor and Maturity. Again, these metrics can provide some view of how reliable an IoT device or set of devices is.

The device reliability metrics, regardless of being standard or non-standard, offer up several opportunities for expansion and further research. Firstly, perhaps these metrics could also be extended to include network infrastructure and communications protocols. Doing so would enable the solution to be a more holistic one and bring it closer to managing reliability for the full end-to-end stack. Secondly, these metrics are able to attest to reliability of IoT devices at a certain point in time—could these metrics then be extended to allow the systems to predict and preempt failure? Doing this would be a valuable step towards a more reliable IoT, especially in scenarios where the IoT is supporting mission critical applications. This leads on to the third area for expansion here—while these metrics are valuable at solving reliability for a given set of sensors in a given environment, there is research required to understand how this generalises into other applications. Importantly, do different thresholds need to be applied when considering one IoT vertical over another? Some research is also required to understand how these reliability metrics might react as new and previously unseen devices are added to the applications. One would expect that new devices may carry a significantly different failure profile, and thus may influence the reliability metrics in different ways. The research on IoT device reliability, therefore, should be extended where possible to include the scenario in which the IoT is capable of handling new and unseen devices, operating over a wide range of communication protocols. Lastly, there is an interplay between IoT device reliability and anomaly detection which was not fully exploited in the works surveyed. Given that we know IoT devices are prone to both spontaneous failure and attack from malicious users, this notion will have a strong influence on the reliability of IoT devices. Therefore, research is required to understand the impact of anomalies on IoT device reliability. For example, some applications may be highly sensitive to noise and anomalies, while other applications may fail completely with the presence of a single anomaly. As such, anomaly detection methods provide a valuable insight into the current state of reliability for IoT devices. A potential research question exists here in trying to understand if reliability information can be synthesised from anomaly detection models.

With regard to the works researching network reliability, again we can observe that some metrics were proposed, both standard (Al-Masri 2018 ; Li and Huang 2017 ; Alam 2018 ) and non-standard (Sinche et al. 2018 ). We can also observe that some new communication protocols were proposed for enabling a more reliable IoT. Some research was also conducted to help address the need for IoT solutions to be considerate of the various vertical markets, for example emergency IoT applications (Maalel et al. 2013 ). Methods were also introduced to profile devices before they joined the IoT deployment, using reliability data as the decision factor (Brogi and Forti 2017 ).

The research conducted on network reliability opens up several areas for future research to enable a more reliable IoT. Firstly, while some research has been conducted to understand the sensitivity of different IoT verticals, there is still a growing need for research in this area to help in understanding the impact that these vertical markets have on reliability engineering in the IoT. Given the large predictions for growth in IoT services, we can only expect demand to increase and diversify in terms of the applications being offered. Therefore, in order to be fully reliable, the IoT must be cognisant of these vertical markets, and measure reliability in a tailored fashion. For example, do faults need to be reported in real-time, such as with emergency applications? Or perhaps we may be able to tolerate faults being reported in larger time windows, such as a day, as with smart home applications.

One of the main issues with the studies aimed at assessing network reliability is that they do not have an awareness of the reliability of the devices themselves. Therefore, it is pertinent that some research is conducted to help tie these two facets together in order to enable reliability across the full IoT stack.

As with device reliability, we can also speculate about the importance of anomalies and intrusions in network traffic. It is important that we understand the impact that these anomalies have on the reliability of a particular application. Moreover, if we are able to leverage intrusion detection methods and anomaly detection methods for networks and use them to ascertain reliability information then this represents a step towards a more reliable IoT. Also similar to the device case, some research would be pertinent to understand if it were possible to predict faults before they occur at the network level. The ability to perform this prediction would enable IoT architects to preemptively manage failure, resulting in a more reliable IoT—especially in the case of mission-critical IoT applications.

The system reliability modelling works reviewed in this paper were not specific to either the device or network component of the IoT architecture. Nevertheless, the methods in these works are at an early stage of development and lack the complexity required to deal with a complex IoT environment.

Referring to the works reviewed for anomaly detection, it is clear from these works that anomaly detection is a growing field within the IoT and computing in general. While the anomaly detection methods included were capable of detecting anomalies, there is still a lack of research and knowledge on how we might leverage this anomaly information to quantify the reliability of an IoT deployment. A key area of future research here will be to take these anomaly detection methods and to try to synthesise reliability information from them.

6 The five research directions for IoT reliability

Having catalogued and analysed the combined efforts made by the IoT reliability research community, some assessments can be drawn as to what the ideal reliability solution should look like. While none of the works surveyed in this paper fully satisfy end-to-end reliability in IoT, they each add a piece of the puzzle towards this goal. As such, we can derive from these works five crucial elements that an end-to-end reliability management system for the IoT must adhere to.

6.1 Direction 1: Vertical and real-time measurement

If the IoT is set to manage critical infrastructure, such as security and critical traffic systems, then we must be able to attest to the reliability of the system in real-time, or as close to real-time as possible. As shown in the study by Maalel et al. ( 2013 ), it is necessary that we pay particular attention to those applications which operate emergency services and require a rapid and reliable response. Moreover, there is a need to define reliability requirements in each individual domain. For example, a smart-building solution may have a delay tolerance of up to a few seconds. An industrial process, on the other hand, will likely only be able to tolerate delays of microseconds. As such, research is required to categorise these requirements and design effective solutions to handle reliability in each of these vertical domains.

6.2 Direction 2: All devices, all protocols

This survey has demonstrated the very wide array of protocols and devices which are set to connect to and consume services from the IoT. Standards for communication protocols are continuing to evolve daily with efforts from many research groups aiming to design more lightweight and efficient communication protocols. Moreover, new IoT devices and hardware continue to emerge in the consumer market daily. Therefore, the ideal reliability solution must be both hardware, software and communication protocol agnostic.

6.3 Direction 3: Full stack awareness

One of the conclusions drawn from the literature review was that, while many researchers had successfully solved a particular problem, or subset of problems, in IoT reliability research, no study has been undertaken which had full awareness of end-to-end reliability. Given the scale and complexity of emerging IoT deployments, this is no easy task. This is not to say, however, that researchers should aim to design a “one size fits all” reliability approach, as this would contradict the first research direction outlined in this work. Rather, individual reliability solutions should be proposed for each IoT vertical that encompass the full IoT architecture. Nevertheless, designing an end-to-end reliability solution for the IoT would be a significant and novel research finding with the potential to greatly enhance IoT end-user experience.

6.4 Direction 4: Synthesising reliability information from anomalies

Much work has gone into detecting and reporting anomalies when they appear in IoT services. While this work is both useful and necessary, it does not necessarily aid reliability without an extra step. Knowledge of an anomaly does not necessarily tell the user if the IoT system has become less reliable. Therefore, there is a need to research how we can synthesise information about emergent anomalies in IoT systems into information on how the reliability has been affected. For example, if a sensor breaks in a smart home which is monitoring an assisted living scenario, there may not necessarily be an immediate risk to life. Whereas, if a thermal sensor begins sending erroneous readings in a smart factory, there is potential for dangerous machinery to malfunction.

6.5 Direction 5: Predict and preemptively manage failure

Measuring reliability is the task discussed at length in this work. If the research is to move a step beyond this goal, then the task of predictive maintenance can be considered. If we are able to reason about the quantified reliability of a system, can we then extrapolate this into an accurate maintenance date? Moreover, can this be further classified at a component level and be a dynamic process which determines results based on real-time reliability data, rather than using a history of past failures to estimate a future failure date? Solving this research question would represent a valuable step in the research of IoT reliability.

7 Conclusion

To the best of our knowledge, this study represents the first review or survey studying the topic of IoT reliability. A detailed history of the evolution of reliability was given, starting from the fundamentals of reliability engineering, moving into reliability in computing and then finally a detailed discussion on the arena of IoT-specific reliability. IoT reliability was defined and discussed across the four main layers of the architecture. A detailed literature review was presented, which looked at research in device, network and system reliability, while also reviewing the current state of the art anomaly detection methods for the IoT. Lastly, the findings and outputs of this detailed survey have been used to formulate five key research directions for the area of reliability in the Internet of Things. This finding now presents a need for the IoT research community to design and implement solutions according to the directions identified in this paper. These solutions will serve to strengthen and support the reliability of our IoT infrastructure, resulting in a safer and more stable paradigm for its users.

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This research is supported by the BTIIC (British Telecom Ireland Innovation Centre) project, funded by BT and Invest Northern Ireland.

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Moore, S.J., Nugent, C.D., Zhang, S. et al. IoT reliability: a review leading to 5 key research directions. CCF Trans. Pervasive Comp. Interact. 2 , 147–163 (2020). https://doi.org/10.1007/s42486-020-00037-z

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Internet of Things is a revolutionary approach for future technology enhancement: a review

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Internet of Things (IoT) is a new paradigm that has changed the traditional way of living into a high tech life style. Smart city, smart homes, pollution control, energy saving, smart transportation, smart industries are such transformations due to IoT. A lot of crucial research studies and investigations have been done in order to enhance the technology through IoT. However, there are still a lot of challenges and issues that need to be addressed to achieve the full potential of IoT. These challenges and issues must be considered from various aspects of IoT such as applications, challenges, enabling technologies, social and environmental impacts etc. The main goal of this review article is to provide a detailed discussion from both technological and social perspective. The article discusses different challenges and key issues of IoT, architecture and important application domains. Also, the article bring into light the existing literature and illustrated their contribution in different aspects of IoT. Moreover, the importance of big data and its analysis with respect to IoT has been discussed. This article would help the readers and researcher to understand the IoT and its applicability to the real world.

Introduction

The Internet of Things (IoT) is an emerging paradigm that enables the communication between electronic devices and sensors through the internet in order to facilitate our lives. IoT use smart devices and internet to provide innovative solutions to various challenges and issues related to various business, governmental and public/private industries across the world [ 1 ]. IoT is progressively becoming an important aspect of our life that can be sensed everywhere around us. In whole, IoT is an innovation that puts together extensive variety of smart systems, frameworks and intelligent devices and sensors (Fig.  1 ). Moreover, it takes advantage of quantum and nanotechnology in terms of storage, sensing and processing speed which were not conceivable beforehand [ 2 ]. Extensive research studies have been done and available in terms of scientific articles, press reports both on internet and in the form of printed materials to illustrate the potential effectiveness and applicability of IoT transformations. It could be utilized as a preparatory work before making novel innovative business plans while considering the security, assurance and interoperability.

General architecture of IoT

A great transformation can be observed in our daily routine life along with the increasing involvement of IoT devices and technology. One such development of IoT is the concept of Smart Home Systems (SHS) and appliances that consist of internet based devices, automation system for homes and reliable energy management system [ 3 ]. Besides, another important achievement of IoT is Smart Health Sensing system (SHSS). SHSS incorporates small intelligent equipment and devices to support the health of the human being. These devices can be used both indoors and outdoors to check and monitor the different health issues and fitness level or the amount of calories burned in the fitness center etc. Also, it is being used to monitor the critical health conditions in the hospitals and trauma centers as well. Hence, it has changed the entire scenario of the medical domain by facilitating it with high technology and smart devices [ 4 , 5 ]. Moreover, IoT developers and researchers are actively involved to uplift the life style of the disabled and senior age group people. IoT has shown a drastic performance in this area and has provided a new direction for the normal life of such people. As these devices and equipment are very cost effective in terms of development cost and easily available within a normal price range, hence most of the people are availing them [ 6 ]. Thanks to IoT, as they can live a normal life. Another important aspect of our life is transportation. IoT has brought up some new advancements to make it more efficient, comfortable and reliable. Intelligent sensors, drone devices are now controlling the traffic at different signalized intersections across major cities. In addition, vehicles are being launched in markets with pre-installed sensing devices that are able to sense the upcoming heavy traffic congestions on the map and may suggest you another route with low traffic congestion [ 7 ]. Therefore IoT has a lot to serve in various aspects of life and technology. We may conclude that IoT has a lot of scope both in terms of technology enhancement and facilitate the humankind.

IoT has also shown its importance and potential in the economic and industrial growth of a developing region. Also, in trade and stock exchange market, it is being considered as a revolutionary step. However, security of data and information is an important concern and highly desirable, which is a major challenging issue to deal with [ 5 ]. Internet being a largest source of security threats and cyber-attacks has opened the various doors for hackers and thus made the data and information insecure. However, IoT is committed to provide the best possible solutions to deal with security issues of data and information. Hence, the most important concern of IoT in trade and economy is security. Therefore, the development of a secure path for collaboration between social networks and privacy concerns is a hot topic in IoT and IoT developers are working hard for this.

The remaining part of the article is organized as follows: “ Literature survey ” section will provide state of art on important studies that addressed various challenges and issues in IoT. “ IoT architecture and technologies ” section discussed the IoT functional blocks, architecture in detail. In “ Major key issues and challenges of IoT ” section, important key issues and challenges of IoT is discussed. “ Major IoT applications ” section provides emerging application domains of IoT. In “ Importance of big data analytics in IoT ” section, the role and importance of big data and its analysis is discussed. Finally, the article concluded in “ Conclusions ” section.

Literature survey

IoT has a multidisciplinary vision to provide its benefit to several domains such as environmental, industrial, public/private, medical, transportation etc. Different researchers have explained the IoT differently with respect to specific interests and aspects. The potential and power of IoT can be seen in several application domains. Figure  2 illustrates few of the application domains of IoTs potentials.

Some of the potential application domains of IoT

Various important IoT projects have taken charge over the market in last few years. Some of the important IoT projects that have captured most of the market are shown in Fig.  3 . In Fig.  3 , a global distribution of these IoT projects is shown among American, European and Asia/Pacific region. It can be seen that American continent are contributing more in the health care and smart supply chain projects whereas contribution of European continent is more in the smart city projects [ 8 ].

Global distribution of IoT projects among America (USA, South America and Canada), Europe and APAC (Asia and Pacific region) [ 8 ]

Figure  4 , illustrates the global market share of IoT projects worldwide [ 8 ]. It is evident that industry, smart city, smart energy and smart vehicle based IoT projects have a big market share in comparison to others.

Global share of IoT projects across the world [ 8 ]

Smart city is one of the trendy application areas of IoT that incorporates smart homes as well. Smart home consists of IoT enabled home appliances, air-conditioning/heating system, television, audio/video streaming devices, and security systems which are communicating with each other in order to provide best comfort, security and reduced energy consumption. All this communication takes place through IoT based central control unit using Internet. The concept of smart city gained popularity in the last decade and attracted a lot of research activities [ 9 ]. The smart home business economy is about to cross the 100 billion dollars by 2022 [ 10 ]. Smart home does not only provide the in-house comfort but also benefits the house owner in cost cutting in several aspects i.e. low energy consumption will results in comparatively lower electricity bill. Besides smart homes, another category that comes within smart city is smart vehicles. Modern cars are equipped with intelligent devices and sensors that control most of the components from the headlights of the car to the engine [ 11 ]. The IoT is committed towards developing a new smart car systems that incorporates wireless communication between car-to-car and car-to-driver to ensure predictive maintenance with comfortable and safe driving experience [ 12 ].

Khajenasiri et al. [ 10 ] performed a survey on the IoT solutions for smart energy control to benefit the smart city applications. They stated that at present IoT has been deployed in very few application areas to serve the technology and people. The scope of IoT is very wide and in near future IoT is able to capture almost all application areas. They mentioned that energy saving is one of the important part of the society and IoT can assist in developing a smart energy control system that will save both energy and money. They described an IoT architecture with respect to smart city concept. The authors also discussed that one of the challenging task in achieving this is the immaturity of IoT hardware and software. They suggested that these issues must be resolved to ensure a reliable, efficient and user friendly IoT system.

Alavi et al. [ 13 ] addressed the urbanization issue in the cities. The movement of people from rural to urban atmosphere resulting in growing population of the cities. Therefore, there is a need to provide smart solutions for mobility, energy, healthcare and infrastructure. Smart city is one of the important application areas for IoT developers. It explores several issues such as traffic management, air quality management, public safety solutions, smart parking, smart lightning and smart waste collection (Fig.  5 ). They mentioned that IoT is working hard to tackle these challenging issues. The need for improved smart city infrastructure with growing urbanization has opened the doors for entrepreneurs in the field of smart city technologies. The authors concluded that IoT enabled technology is very important for the development of sustainable smart cities.

Potential IoT application areas for smart cities

Another important issue of IoT that requires attention and a lot of research is security and privacy. Weber [ 14 ] focused on these issues and suggested that a private organization availing IoT must incorporate data authentication, access control, resilience to attacks and client privacy into their business activities that would be an additional advantage. Weber suggested that in order to define global security and privacy issues, IoT developers must take into account the geographical limitations of the different countries. A generic framework needs to be designed to fit the global needs in terms of privacy and security. It is highly recommended to investigate and recognize the issues and challenges in privacy and security before developing the full fledge working IoT framework.

Later, Heer et al. [ 15 ] came up with a security issue in IP based IoT system. They mentioned that internet is backbone for the communication among devices that takes place in an IoT system. Therefore, security issues in IP based IoT systems are an important concern. In addition, security architecture should be designed considering the life cycle and capabilities of any object in the IoT system. It also includes the involvement of the trusted third party and the security protocols. The security architecture with scalability potential to serve the small-scale to large-scale things in IoT is highly desirable. The study pointed out that IoT gave rise to a new way of communication among several things across the network therefore traditional end to end internet protocol are not able to provide required support to this communication. Therefore, new protocols must be designed considering the translations at the gateways to ensure end-to-end security. Moreover, all the layers responsible for communication has their own security issues and requirements. Therefore, satisfying the requirements for one particular layers will leave the system into a vulnerable state and security should be ensured for all the layers.

Authentication and access control is another issue in IoT that needs promising solutions to strengthen the security. Liu et al. [ 16 ] brought up a solution to handle authentication and access control. Authentication is very important to verify the communicating parties to prevent the loss of confidential information. Liu et al. [ 16 ] provided an authentication scheme based on Elliptic Curve Cryptosystem and verified it on different security threats i.e. eavesdropping, man-in-the-middle attack, key control and replay attack. They claimed that there proposed schemes are able to provide better authentication and access control in IoT based communication. Later, Kothmayr et al. [ 17 ] proposed a two-way authentication scheme based of datagram transport layer security (DTLS) for IoT. The attackers over the internet are always active to steal the secured information. The proposed approach are able to provide message security, integrity, authenticity and confidentiality, memory overhead and end-to-end latency in the IoT based communication network.

Li et al. [ 18 ] proposed a dynamic approach for data centric IoT applications with respect to cloud platforms. The need of an appropriate device, software configuration and infrastructure requires efficient solutions to support massive amount of IoT applications that are running on cloud platforms. IoT developers and researchers are actively engaged in developing solutions considering both massive platforms and heterogeneous nature of IoT objects and devices. Olivier et al. [ 19 ] explained the concept of software defined networking (SDN) based architecture that performs well even if a well-defined architecture is not available. They proposed that SDN based security architecture is more flexible and efficient for IoT.

Luk et al. [ 20 ] stated that the main task of a secure sensor network (SSN) is to provide data privacy, protection from replay attacks and authentication. They discussed two popular SSN services namely TinySec [ 21 ] and ZigBee [ 22 ]. They mentioned that although both the SSN services are efficient and reliable, however, ZigBee is comparatively provides higher security but consumes high energy whereas TinySec consumes low energy but not as highly secured as ZigBee. They proposed another architecture MiniSec to support high security and low energy consumption and demonstrated its performance for the Telos platform. Yan et al. [ 23 ] stated that trust management is an important issue in IoT. Trust management helps people to understand and trust IoT services and applications without worrying about uncertainty issues and risks [ 24 ]. They investigated different issues in trust management and discussed its importance with respect to IoT developers and users.

Noura et al. [ 25 ] stated the importance of interoperability in IoT as it allows integration of devices, services from different heterogeneous platforms to provide the efficient and reliable service. Several other studies focused on the importance of interoperability and discussed several challenges that interoperability issue is facing in IoT [ 26 , 27 , 28 ]. Kim et al. [ 29 ] addressed the issue of climate change and proposed an IoT based ecological monitoring system. They mentioned that existing approaches are time consuming and required a lot of human intervention. Also, a routine visit is required to collect the information from the sensors installed at the site under investigation. Also, some information remained missing which leads to not highly accurate analysis. Therefore, IoT based framework is able to solve this problem and can provide high accuracy in analysis and prediction. Later, Wang et al. [ 30 ] shows their concern for domestic waste water treatment. They discussed several deficiencies in the process of waste water treatment and dynamic monitoring system and suggested effective solutions based on IoT. They stated that IoT can be very effective in the waste water treatment and process monitoring.

Agriculture is one of the important domain around the world. Agriculture depends on several factors i.e. geographical, ecological etc. Qiu et al. [ 31 ] stated that technology that is being used for ecosystem control is immature with low intelligence level. They mentioned that it could be a good application area for IoT developers and researchers.

Qiu et al. [ 31 ] proposed an intelligent monitoring platform framework for facility agriculture ecosystem based on IoT that consists of four layer mechanism to manage the agriculture ecosystem. Each layer is responsible for specific task and together the framework is able to achieve a better ecosystem with reduced human intervention.

Another important concern around the world is climate change due to global warming. Fang et al. [ 32 ] introduced an integrated information system (IIS) that integrates IoT, geo-informatics, cloud computing, global positioning system (GPS), geographical information system (GIS) and e-science in order to provide an effective environmental monitoring and control system. They mentioned that the proposed IIS provides improved data collection, analysis and decision making for climate control. Air pollution is another important concern worldwide. Various tools and techniques are available to air quality measures and control. Cheng et al. [ 33 ] proposed AirCloud which is a cloud based air quality and monitoring system. They deployed AirCloud and evaluated its performance using 5 months data for the continuous duration of 2 months.

Temglit et al. [ 34 ] considered Quality of Service (QoS) as an important challenge and a complex task in evaluation and selection of IoT devices, protocols and services. QoS is very important criteria to attract and gain trust of users towards IoT services and devices. They came up with an interesting distributed QoS selection approach. This approach was based on distributed constraint optimization problem and multi-agent paradigm. Further, the approach was evaluated based on several experiments under realistic distributed environments. Another important aspect of IoT is its applicability to the environmental and agriculture standards. Talavera et al. [ 35 ] focused in this direction and presented the fundamental efforts of IoT for agro-industrial and environmental aspects in a survey study. They mentioned that the efforts of IoT in these areas are noticeable. IoT is strengthening the current technology and benefiting the farmers and society. Jara et al. [ 36 ] discussed the importance of IoT based monitoring of patients health. They suggested that IoT devices and sensors with the help of internet can assist health monitoring of patients. They also proposed a framework and protocol to achieve their objective. Table 1 provides a summary of the important studies and the direction of research with a comparison of studies on certain evaluation parameters.

IoT architecture and technologies

The IoT architecture consists of five important layers that defines all the functionalities of IoT systems. These layers are perception layer, network layer, middleware layer, application layer, business layer. At the bottom of IoT architecture, perception layer exists that consists of physical devices i.e. sensors, RFID chips, barcodes etc. and other physical objects connected in IoT network. These devices collects information in order to deliver it to the network layer. Network layer works as a transmission medium to deliver the information from perception layer to the information processing system. This transmission of information may use any wired/wireless medium along with 3G/4G, Wi-Fi, Bluetooth etc. Next level layer is known as middleware layer. The main task of this layer is to process the information received from the network layer and make decisions based on the results achieved from ubiquitous computing. Next, this processed information is used by application layer for global device management. On the top of the architecture, there is a business layer which control the overall IoT system, its applications and services. The business layer visualizes the information and statistics received from the application layer and further used this knowledge to plan future targets and strategies. Furthermore, the IoT architectures can be modified according to the need and application domain [ 19 , 20 , 37 ]. Besides layered framework, IoT system consists of several functional blocks that supports various IoT activities such as sensing mechanism, authentication and identification, control and management [ 38 ]. Figure  6 illustrates such functional blocks of IoT architecture.

A generic function module of IoT system

There are several important functional blocks responsible for I/O operations, connectivity issues, processing, audio/video monitoring and storage management. All these functional block together incorporates an efficient IoT system which are important for optimum performance. Although, there are several reference architectures proposed with the technical specifications, but these are still far from the standard architecture that is suitable for global IoT [ 39 ]. Therefore, a suitable architecture is still needsvk to be designed that could satisfy the global IoT needs. The generic working structure of IoT system is shown in Fig.  7 . Figure  7 shows a dependency of IoT on particular application parameters. IoT gateways have an important role in IoT communication as it allows connectivity between IoT servers and IoT devices related to several applications [ 40 ].

Working structure of IoT

Scalability, modularity, interoperability and openness are the key design issues for an efficient IoT architecture in a heterogenous environment. The IoT architecture must be designed with an objective to fulfil the requirements of cross domain interactions, multi-system integration with the potential of simple and scalable management functionalities, big data analytics and storage, and user friendly applications. Also, the architecture should be able to scaleup the functionality and add some intelligence and automation among the IoT devices in the system.

Moreover, increasing amount of massive data being generated through the communication between IoT sensors and devices is a new challenge. Therefore, an efficient architecture is required to deal with massive amount of streaming data in IoT system. Two popular IoT system architectures are cloud and fog/edge computing that supports with the handling, monitoring and analysis of huge amount of data in IoT systems. Therefore, a modern IoT architecture can be defined as a 4 stage architecture as shown in Fig.  8 .

Four stage IoT architecture to deal with massive data

In stage 1 of the architecture, sensors and actuators plays an important role. Real world is comprised of environment, humans, animals, electronic gadgets, smart vehicles, and buildings etc. Sensors detect the signals and data flow from these real world entities and transforms into data which could further be used for analysis. Moreover, actuators is able to intervene the reality i.e. to control the temperature of the room, to slow down the vehicle speed, to turn off the music and light etc. Therefore, stage 1 assist in collecting data from real world which could be useful for further analysis. Stage 2 is responsible to collaborate with sensors and actuators along with gateways and data acquisition systems. In this stage, massive amount of data generated in stage 1 is aggregated and optimized in a structured way suitable for processing. Once the massive amount of data is aggregated and structured then it is ready to be passed to stage 3 which is edge computing. Edge computing can be defined as an open architecture in distributed fashion which allows use of IoT technologies and massive computing power from different locations worldwide. It is very powerful approach for streaming data processing and thus suitable for IoT systems. In stage 3, edge computing technologies deals with massive amount of data and provides various functionalities such as visualization, integration of data from other sources, analysis using machine learning methods etc. The last stage comprises of several important activities such as in depth processing and analysis, sending feedback to improve the precision and accuracy of the entire system. Everything at this stage will be performed on cloud server or data centre. Big data framework such as Hadoop and Spark may be utilized to handle this large streaming data and machine learning approaches can be used to develop better prediction models which could help in a more accurate and reliable IoT system to meet the demand of present time.

Major key issues and challenges of IoT

The involvement of IoT based systems in all aspects of human lives and various technologies involved in data transfer between embedded devices made it complex and gave rise to several issues and challenges. These issues are also a challenge for the IoT developers in the advanced smart tech society. As technology is growing, challenges and need for advanced IoT system is also growing. Therefore, IoT developers need to think of new issues arising and should provide solutions for them.

Security and privacy issues

One of the most important and challenging issues in the IoT is the security and privacy due to several threats, cyber attacks, risks and vulnerabilities [ 41 ]. The issues that give rise to device level privacy are insufficient authorization and authentication, insecure software, firmware, web interface and poor transport layer encryption [ 42 ]. Security and privacy issues are very important parameters to develop confidence in IoT Systems with respect to various aspects [ 43 ]. Security mechanisms must be embedded at every layer of IoT architecture to prevent security threats and attacks [ 23 ]. Several protocols are developed and efficiently deployed on every layer of communication channel to ensure the security and privacy in IoT based systems [ 44 , 45 ]. Secure Socket Layer (SSL) and Datagram Transport Layer Security (DTLS) are one of the cryptographic protocols that are implemented between transport and application layer to provide security solutions in various IoT systems [ 44 ]. However, some IoT applications require different methods to ensure the security in communication between IoT devices. Besides this, if communication takes place using wireless technologies within the IoT system, it becomes more vulnerable to security risks. Therefore, certain methods should be deployed to detect malicious actions and for self healing or recovery. Privacy on the other hand is another important concern which allows users to feel secure and comfortable while using IoT solutions. Therefore, it is required to maintain the authorization and authentication over a secure network to establish the communication between trusted parties [ 46 ]. Another issue is the different privacy policies for different objects communicating within the IoT system. Therefore, each object should be able to verify the privacy policies of other objects in IoT system before transmitting the data.

Interoperability/standard issues

Interoperability is the feasibility to exchange the information among different IoT devices and systems. This exchange of information does not rely on the deployed software and hardware. The interoperability issue arises due to the heterogeneous nature of different technology and solutions used for IoT development. The four interoperability levels are technical, semantic, syntactic and organizational [ 47 ]. Various functionalities are being provided by IoT systems to improve the interoperability that ensures communication between different objects in a heterogeneous environment. Additionally, it is possible to merge different IoT platforms based on their functionalities to provide various solutions for IoT users [ 48 ]. Considering interoperability an important issue, researchers approved several solutions that are also know as interoperability handling approaches [ 49 ]. These solutions could be adapaters/gateways based, virtual networks/overlay based, service oriented architecture based etc. Although interoperability handling approaches ease some pressure on IoT systems but there are still certain challenges remain with interoperability that could be a scope for future studies [ 25 ].

Ethics, law and regulatory rights

Another issue for IoT developers is the ethics, law and regulatory rights. There are certain rules and regulations to maintain the standard, moral values and to prevent the people from violating them. Ethics and law are very similar term with the only difference is that ethics are standards that people believes and laws are certain restrictions decided by the government. However, both ethics and laws are designed to maintain the standard, quality and prevent people from illegal use. With the development of IoT, several real life problems are solved but it has also given rise to critical ethical and legal challenges [ 50 ]. Data security, privacy protection, trust and safety, data usability are some of those challenges. It has also been observed that majority of IoT users are supporting government norms and regulations with respect to data protection, privacy and safety due to the lack of trust in IoT devices. Therefore, this issue must be taken into consideration to maintain and improve the trust among people for the use of IoT devices and systems.

Scalability, availability and reliability

A system is scalable if it is possible to add new services, equipments and devices without degrading its performance. The main issue with IoT is to support a large number of devices with different memory, processing, storage power and bandwidth [ 28 ]. Another important issue that must be taken into consideration is the availability. Scalability and availability both should be deployed together in the layered framework of IoT. A great example of scalability is cloud based IoT systems which provide sufficient support to scale the IoT network by adding up new devices, storage and processing power as required.

However, this global distributed IoT network gives rise to a new research paradigm to develop a smooth IoT framework that satisfy global needs [ 51 ]. Another key challenge is the availability of resources to the authentic objects regardless of their location and time of the requirement. In a distributed fashion, several small IoT networks are timely attached to the global IoT platforms to utilize their resources and services. Therefore, availability is an important concern [ 52 ]. Due to the use of different data transmission channels i.e. satellite communication, some services and availability of resources may be interrupted. Therefore, an independent and reliable data transmission channel is required for uninterrupted availability of resources and services.

Quality of Service (QoS)

Quality of Service (QoS) is another important factor for IoT. QoS can be defined as a measure to evaluate the quality, efficiency and performance of IoT devices, systems and architecture [ 34 ]. The important and required QoS metrics for IoT applications are reliability, cost, energy consumption, security, availability and service time [ 53 ]. A smarter IoT ecosystem must fulfill the requirements of QoS standards. Also, to ensure the reliability of any IoT service and device, its QoS metrics must be defined first. Further, users may also be able to specifiy their needs and requirements accordingly. Several approaches can be deployed for QoS assessment, however as mentioned by White et al. [ 54 ] there is a trade-off between quality factors and approaches. Therefore, good quality models must be deployed to overcome this trade-off. There are certain good quality models available in literature such as ISO/IEC25010 [ 55 ] and OASIS-WSQM [ 56 ] which can be used to evaluate the approaches used for QoS assessment. These models provides a wide range of quality factors that is quite sufficient for QoS assessment for IoT services. Table  2 summarizes the different studies with respect to IoT key challenges and issues discussed above.

Major IoT applications

Emerging economy, environmental and health-care.

IoT is completely devoted to provide emerging public and financial benefits and development to the society and people. This includes a wide range of public facilities i.e. economic development, water quality maintenance, well-being, industrialization etc. Overall, IoT is working hard to accomplish the social, health and economic goals of United Nations advancement step. Environmental sustainability is another important concern. IoT developers must be concerned about environmental impact of the IoT systems and devices to overcome the negative impact [ 48 ]. Energy consumption by IoT devices is one of the challenges related to environmental impact. Energy consumption is increasing at a high rate due to internet enabled services and edge cutting devices. This area needs research for the development of high quality materials in order to create new IoT devices with lower energy consumption rate. Also, green technologies can be adopted to create efficient energy efficient devices for future use. It is not only environmental friendly but also advantageous for human health. Researchers and engineers are engaged in developing highly efficient IoT devices to monitor several health issues such as diabetes, obesity or depression [ 57 ]. Several issues related to environment, energy and healthcare are considered by several studies.

Smart city, transport and vehicles

IoT is transforming the traditional civil structure of the society into high tech structure with the concept of smart city, smart home and smart vehicles and transport. Rapid improvements are being done with the help of supporting technologies such as machine learning, natural language processing to understand the need and use of technology at home [ 58 ]. Various technologies such as cloud server technology, wireless sensor networks that must be used with IoT servers to provide an efficient smart city. Another important issue is to think about environmental aspect of smart city. Therefore, energy efficient technologies and Green technologies should also be considered for the design and planning of smart city infrastructure. Further, smart devices which are being incorporated into newly launched vehicles are able to detect traffic congestions on the road and thus can suggest an optimum alternate route to the driver. This can help to lower down the congestion in the city. Furthermore, smart devices with optimum cost should be designed to be incorporated in all range vehicles to monitor the activity of engine. IoT is also very effective in maintaining the vehicle’s health. Self driving cars have the potential to communicate with other self driving vehicles by the means of intelligent sensors. This would make the traffic flow smoother than human-driven cars who used to drive in a stop and go manner. This procedure will take time to be implemented all over the world. Till the time, IoT devices can help by sensing traffic congestion ahead and can take appropriate actions. Therefore, a transport manufacturing company should incorporate IoT devices into their manufactured vehicles to provide its advantage to the society.

Agriculture and industry automation

The world’s growing population is estimated to reach approximate 10 billion by 2050. Agriculture plays an important role in our lives. In order to feed such a massive population, we need to advance the current agriculture approaches. Therefore, there is a need to combine agriculture with technology so that the production can be improved in an efficient way. Greenhouse technology is one of the possible approaches in this direction. It provides a way to control the environmental parameters in order to improve the production. However, manual control of this technology is less effective, need manual efforts and cost, and results in energy loss and less production. With the advancement of IoT, smart devices and sensors makes it easier to control the climate inside the chamber and monitor the process which results in energy saving and improved production (Fig.  9 ). Automatization of industries is another advantage of IoT. IoT has been providing game changing solutions for factory digitalization, inventory management, quality control, logistics and supply chain optimization and management.

A working structure of IoT system in agriculture production

Importance of big data analytics in IoT

An IoT system comprises of a huge number of devices and sensors that communicates with each other. With the extensive growth and expansion of IoT network, the number of these sensors and devices are increasing rapidly. These devices communicate with each other and transfer a massive amount of data over internet. This data is very huge and streaming every second and thus qualified to be called as big data. Continuous expansion of IoT based networks gives rise to complex issue such as management and collection of data, storage and processing and analytics. IoT big data framework for smart buildings is very useful to deal with several issues of smart buildings such as managing oxygen level, to measure the smoke/hazardous gases and luminosity [ 59 ]. Such framework is capable to collect the data from the sensors installed in the buildings and performs data analytics for decision making. Moreover, industrial production can be improved using an IoT based cyber physical system that is equipped with an information analysis and knowledge acquisition techniques [ 60 ]. Traffic congestion is an important issue with smart cities. The real time traffic information can be collected through IoT devices and sensors installed in traffic signals and this information can be analyzed in an IoT based traffic management system [ 61 ]. In healthcare analysis, the IoT sensors used with patients generate a lot of information about the health condition of patients every second. This large amount of information needs to be integrated at one database and must be processed in real time to take quick decision with high accuracy and big data technology is the best solution for this job [ 62 ]. IoT along with big data analytics can also help to transform the traditional approaches used in manufacturing industries into the modern one [ 63 ]. The sensing devices generates information which can be analyzed using big data approaches and may help in various decision making tasks. Furthermore, use of cloud computing and analytics can benefit the energy development and conservation with reduced cost and customer satisfaction [ 64 ]. IoT devices generate a huge amount of streaming data which needs to be stored effectively and needs further analysis for decision making in real time. Deep learning is very effective to deal with such a large information and can provide results with high accuracy [ 65 ]. Therefore, IoT, Big data analytics and Deep learning together is very important to develop a high tech society.

Conclusions

Recent advancements in IoT have drawn attention of researchers and developers worldwide. IoT developers and researchers are working together to extend the technology on large scale and to benefit the society to the highest possible level. However, improvements are possible only if we consider the various issues and shortcomings in the present technical approaches. In this survey article, we presented several issues and challenges that IoT developer must take into account to develop an improved model. Also, important application areas of IoT is also discussed where IoT developers and researchers are engaged. As IoT is not only providing services but also generates a huge amount of data. Hence, the importance of big data analytics is also discussed which can provide accurate decisions that could be utilized to develop an improved IoT system.

Availability of data and materials

Not applicable.

Abbreviations

Internet of Things

Quality of Service

Web of Things

Cloud of Things

Smart Home System

Smart Health Sensing System

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This work was financially supported by the Ministry of Education and Science of Russian Federation (government order 2.7905.2017/8.9).

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Kumar, S., Tiwari, P. & Zymbler, M. Internet of Things is a revolutionary approach for future technology enhancement: a review. J Big Data 6 , 111 (2019). https://doi.org/10.1186/s40537-019-0268-2

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LoRa-Based IoT Network Assessment in Rural and Urban Scenarios

Aikaterini i. griva.

1 ELEDIA@AUTH, School of Physics, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece

Achilles D. Boursianis

Shaohua wan.

2 Shenzhen Institute for Advanced Study, University of Electronic Science and Technology of China, Shenzhen 518110, China

Panagiotis Sarigiannidis

3 Department of Electrical and Computer Engineering, University of Western Macedonia, 50131 Kozani, Greece

Konstantinos E. Psannis

4 Department of Applied Informatics, School of Information Sciences, University of Macedonia, 54636 Thessaloniki, Greece

George Karagiannidis

5 School of Electrical and Computer Engineering, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece

Sotirios K. Goudos

Associated data.

Data sharing not applicable.

The implementation of smart networks has made great progress due to the development of the Internet of Things (IoT). LoRa is one of the most prominent technologies in the Internet of Things industry, primarily due to its ability to achieve long-distance transmission while consuming less power. In this work, we modeled different environments and assessed the performances of networks by observing the effects of various factors and network parameters. The path loss model, the deployment area size, the transmission power, the spreading factor, the number of nodes and gateways, and the antenna gain have a significant effect on the main performance metrics such as the energy consumption and the data extraction rate of a LoRa network. In order to examine these parameters, we performed simulations in OMNeT++ using the open source framework FLoRa. The scenarios which were investigated in this work include the simulation of rural and urban environments and a parking area model. The results indicate that the optimization of the key parameters could have a huge impact on the deployment of smart networks.

1. Introduction

In recent years, Internet of Things (IoT) technologies and techniques have been developed to cope with modern requirements. Smart cities [ 1 ], smart homes and buildings [ 2 ], healthcare [ 3 ], manufacturing [ 4 ], and smart agriculture [ 5 ] are some of the most notable areas where IoT technologies are being adopted to address many challenges and improve the way we live. Everyday life devices are being equipped with sensors that communicate through the Internet. Based on Cisco, it is predicted that there will be 500 billion Internet-connected devices by 2030 [ 6 ].

The key requirement for data access between devices is to cover large distances and consume less power. The low-power wide-area network (LPWAN) architecture is one of the most prominent in that field, and long-range (LoRa) technology supported by the low-power wide-area networking (LoRaWAN) specification has already been adopted in numerous systems in wireless communication. A LoRa network consists of two distinct parts: LoRa and LoRaWAN. Each component corresponds to a different layer of the protocol stack.

LoRa’s design favors long-range applications that require a low rate of transmission and low energy consumption [ 7 ]. LoRa operates in the industrial, scientific, and medical (ISM) bands (from 863 to 870 MHz in Europe, from 902 to 928 MHz in the USA, and from 470 to 510 MHz in Asia).

LoRaWAN is a communication protocol [ 8 ] that uses an ALOHA-based MAC protocol. That way, the LoRa end-devices comply with the practical requirement for low complexity. LoRa end-node devices and gateways communicate over the physical layer, but there is no association between nodes and a specific gateway. Data from end nodes can be received by any gateway within a definite communication range, and the messages are forwarded toward the network server using the Internet protocol. Finally, the messages are delivered to the application server.

In our preliminary work [ 9 ], we simulated an open-field cultivation scenario using FLoRa and evaluated the performance of the network. The motivation for our work stems from the above-mentioned discussion and from the fact that we want to thoroughly investigate the most important parameters that affect the operation of LoRa networks in various simulations using the common path loss models in different environments. As such, we can evaluate the performance of LoRa networks under different conditions and draw a generalized conclusion about the efficiency of the network in urban and rural environments.

In this work, we focus on the assessment of performance metrics in LoRa networks under different propagation and environmental scenarios. We selected the two most common path loss models that are used to simulate various LoRa networks in the rural environment, and we explored how the selection of the technical parameters affects the performance of each scenario network. Moreover, we decided to extend the research to an urban area by modeling a wide variety of nodes. We also used the Oulu path loss model to compare different propagation scenarios. Finally, we modeled a dense network of nodes with the same technical characteristics to simulate a smart parking area that can be found in every modern city, and we compared the performance of this network to the previous ones.

The remainder of the paper is structured as follows. Section 2 presents the work directly related to this paper. The problem definition and the simulation scheme are provided in Section 3 . In Section 4 , the authors analyzed the simulation scenarios and evaluated the results. Finally, the conclusions of this study are summarized in Section 5 .

2. Related Work

According to the relevant literature, there have been various studies on LoRa networks. Some of these have focused on LoRa network scalability. In [ 10 ], the authors released the LoRaSim simulator to study the scalability and performance of LoRa networks through simulation. They developed models that describe LoRa communication behavior, and they determined that LoRa networks can scale well by adding more gateways and/or by selecting dynamic transmission parameters. This was further extended in [ 11 ], in which the authors presented LoRaWANSim, a tool that employs bidirectional communication. Georgiou and Raza in [ 12 ] proposed a stochastic geometry framework to evaluate the behavior of a single gateway LoRa network. They found that, with an increasing number of end devices, the coverage probability decreased exponentially due to interfering signals that used the same spreading factor. In a similar study, the authors in [ 13 ] highlighted that the network scalability is more precise under the combined impact of co-SF and inter-SF interference. In [ 14 ], in the case of a single-gateway LoRaWAN deployment, the scalability was examined in terms of the number of nodes per gateway. The authors developed a simulation model that measures the impact of interference to determine the scalability of a single gateway. Furthermore, the authors in [ 15 ] used the ns-3 module to analyze the scalability. The results show that the allocation of the network parameters to nodes has a huge impact on the performance of LoRaWAN networks. Moreover, this work examines the capacity for various traffic types. In [ 16 ], the scalability of the network increases by using a new medium access protocol in a MATLAB simulator. In [ 17 ], a literature overview was presented, and various performance determinants were analyzed on LoRa-based networks.

Various experimental tests have been performed in real-world environments. LoRa networks have been studied in cases ranging from indoor [ 18 , 19 ] and urban/suburban [ 20 , 21 ] scenarios to rural [ 22 ] and mountain [ 23 , 24 ] environments. In [ 25 ], the researchers provided a comprehensive evaluation of LoRa networks in urban, suburban, and rural environments, considering both static and dynamic conditions. The authors in [ 26 ] evaluated the coverage and simulated the path loss model in urban, forest, and coastal environments. In [ 27 ], a path loss model based on experimental scenarios was proposed to assess the efficiency of LoRa networks in urban and rural environments in terms of coverage. The work presented in [ 28 ] focused on the evaluation of the transmission performance and the link quality of a network considering the deployment scenario and the parameter configuration. In [ 29 ], a smart building scenario was implemented to appraise the communication performance of LoRa networks without considering the power consumption. The authors in [ 30 ] presented a theoretical study and an experimental evaluation of a LoRa network. Moreover, the impact of the coding rate of the communication link is discussed in this work. In [ 31 ], the authors developed the FLoRa simulation tool to implement and evaluate the adaptive data rate (ADR) mechanism in LoRa networks. In [ 32 ], the authors evaluated the impact of SF on performance. Another method to improve the performance based on SF network clustering was presented in [ 33 ], and the authors in [ 34 ] developed an algorithm to further improve the efficiency of the LoRa network compared to the ADR algorithm.

3. Problem Definition

3.1. evaluation metrics.

Based on the related work that was presented, we decided to assess the effectiveness of LoRa networks using two evaluation metrics:

• Data extraction rate (DER) is defined as the amount of messages which were received correctly divided by the number of messages that were sent to the server. DER is computed between 0 and 1. When the ratio is closer to 1, it means that the LoRa network is working more efficiently.
• Network energy consumption (NEC) is defined as the energy consumed by the network divided by the number of successfully received messages. A low value for NEC implies a more efficient network.

3.2. Path Loss Models

To model radio wave propagation, most researchers are using the log-normal shadowing model, which depends on empirical data [ 10 ]. The Oulu city model [ 18 ] and the Okumura–Hata (OH) model have been used to assess the coverage in LoRa networks [ 25 , 35 ]. In our research, we chose to look more closely at these three different path-loss models. A short summary of each model is given.

3.2.1. Log-Distance Path Loss Model with Shadowing

The log-distance path loss model with shadowing is widely used in wireless communication to model the obstruction on the propagation path between a base station (BS) and a mobile station (MS). The equation is given as follows:

where the P L ( d ) stands for the path loss, P L ( d 0 ) is the mean path loss measured in dB, n is used for the path loss exponent, X s is the loss due to shadow fading with a zero-mean Gaussian distribution, and σ denotes the standard deviation. In this work, n was set to 2 and s i g m a was set to 5 dB in order to simulate the rural environment, whilst n was selected equal to 2.08 and σ was set to 3.57 dB to simulate the urban environment.

3.2.2. The Oulu Path Loss Equation

Based on experimental data, the authors in [ 36 ] provided the Oulu path loss model using the following equation:

where E P L is the expected path loss, B is the path loss in dB, α describes the path loss exponent, and R is the distance between the node and the base station divided by the 1 km reference distance.

The standard deviation of shadow fading describes a deviation between the measured path loss and expected path loss and is computed as follows

The city of Oulu is a medium-sized city with high residential buildings in the center, located one the seashore and with a mainly flat terrain. The approach provided by this area can be used to model many similar urban environments all over the world. The measurements were conducted using a mobile node on the roof of a car moving over the ground and using a node on a boat over the water. The base station was on the roof of the University of Oulu, 24 meters above sea level.

3.2.3. Okumura–Hata Path Loss Model

The Okumura–Hata empirical model is a widely used radio propagation model for predicting path loss. The model is appropriate for linking distances from 1 to 20 km and a frequency range of 150–1500 MHz. The user antenna heights range from 1 to 10 m and the BS antenna height ranges from 30 to 200 m. The Okumura–Hata model is popular for its accuracy and simplicity. This model has the advantage of being adjusted in many different environments from open areas to large cities by selecting the appropriate variant, as is apparent from the following equations.

The Okumura–Hata path loss equations are modified as follows:

With regard to an urban environment, the two following Okumura–Hata variants can exist

For small or medium-sized cities,

and for large cities,

Under the sub-urban environment,

Under the free/open/rural environment,

where L u r b a n is the path loss in urban areas, L s u b u r b a n is the path loss in suburban areas, and L o p e n is the path loss in open-rural areas in dB. h b is the height of the base station antenna, and h m is the height of the mobile station antenna in m. Moreover, f c is the frequency of the transmission in MHz and R is the distance between the station and the mobile stations in km. Finally, the a ( h m ) is the correction factor for mobile antenna height.

3.3. Simulation Tools

As LoRa technology is widely used in IoT applications, various simulation environments were developed to study, evaluate, and optimize LoRa networks before their implementation. A short overview of the most commonly used simulators that have been developed over the years is presented in this section.

• LoRaSim is a discrete event simulator developed to study the scalability in LoRa networks and to model collisions. This tool is written in Python and allows modeling in a 2-dimensional grid. By selecting a number of parameters such as the number of nodes and base stations, the radio settings, and the simulation time, we can have information regarding the collisions, transmissions, and total energy spent [ 10 ].
• The Framework for LoRa (FLoRa) is an open source simulation tool based on the OMNeT++ (ver. 5.3, OpenSim Ltd., Budapest, Hungary) [ 37 ] discrete event simulator and the INET framework [ 38 ]. FLoRa provides a precise model of the physical layer taking into consideration the capture effect as well as the collisions and also implements the MAC layer. Moreover, FLoRa includes the various modules of a network such as nodes, gateway(s), and server(s), supports bi-directional communication and enables end-to-end simulations. One or more gateways can be simulated in the model and can receive transmissions from nodes on multiple channels. The gateways communicate with the server over the IP protocol. The tool is written in C++ and NED language and is used to simulate LoRa networks. Moreover, it provides the statistics for energy consumption in every node considering the three states (transmitting, receiving, and sleeping) of a LoRa radio [ 31 ].
• The NS-3 module is a discrete-event network simulator written in C++ and Python. This module is used to assess the performance of a network in various cases because it can simulate many aspects of a network such as the layers of the system and the protocols that are used [ 15 ].
• LoRaWANSim is a LoRaWAN simulator that is written in MATLAB. This simulator was developed to characterize the behavior of LoRaWAN networks. In LoRaWANSim, we can model and also modify parameters related to the PHY layer and the LoRaWAN protocol [ 11 ].

Considering the requirements of our study, the FLoRa open source simulation tool was selected. As shown in Figure 1 , the tool was used to model the LoRa physical layer and the LoRaWAN MAC protocol, as well as the network elements such as network servers, nodes, and gateways. The module describing the energy consumption was also used.

Modules in FLoRa and the corresponding protocol stack [ 31 ].

4. Numerical Results

In this part, we analyzed the various scenarios we implemented to assess the performance of LoRa Networks. In every simulation, we assumed that the nodes were uniformly distributed on a square deployment area, whereas the gateways were arbitrarily placed. Each scenario lasted 7 days, and the physical layer of the LoRa network was simulated in each simulation environment by choosing the right European regional parameters.

Five configuration parameters were selected to determine the energy consumption, the transmission range, the data rate, and the noise durability of a LoRa transmission [ 10 ].

• Transmission power (TP). TP can be set between −4 and 20 dBm. When the TP increases, the energy consumption of the network and the signal-to-noise ratio (SNR) are also increased.
• Bandwidth (BW). BW can be set to 125 kHz, 250 kHz, or 500 kHz. A bigger BW provides a higher data rate but decreases the radio sensitivity.
• Spreading factor (SF). SF can be in the range of 7–12. A higher SF improves the communication range but increases the energy consumption.
• Carrier frequency (CF). CF can be selected between 137 MHz and 1020 MHz according to the ISM band in the region of operation.
• Coding rate (CR). CR can be configured to 4/5, 4/6, 4/7, or 4/8 to provide security from interference. By choosing a higher CR, the network becomes more reliable, but the air time increases.

4.1. Case Study 1—Rural Environment

The rural environment was simulated by using the log-distance path loss model and the Okumura–Hata path loss model. In each scenario, the transmission power was set to 10 dBm, the bandwidth was fixed at 125 kHz, the code rate was selected as 4/8, the chosen spreading factor was 7, and the carrier frequency was selected as 868 MHz ( Table 1 ).

Simulation parameters in the rural environment.

4.1.1. Log-Distance Path Loss Model with Shadowing

Scenario 1.1 In the first scenario, we assess the effectiveness of a LoRa network in three squared areas. The first deployment area was set to 300 m × 300 m, the second deployment area was set to 500 m × 500 m, and the third area was set to 1000 m × 1000 m. The number of nodes was increased from 10 to 1000 in each area. Figure 2 a,b show that, with an increasing number of nodes, there is a decrease in the network performance, due to the more frequent incidents of collisions between the packets that were sent from nodes with identical characteristics. As we can obtain by increasing the nodes count from 10 to 1000 in the deployment area B, the collisions are increased by 64%. Moreover, the results of the simulations indicate that the performance metrics (DER, NEC) we obtained are affected by the dimensions of the deployment area. This occurs because, as the dimension of the deployment space is increased, the exact number of nodes must be spread across a larger space.

Log-normal shadowing model in rural environment. ( a ) DER as a function of the number of nodes; ( b ) NEC as a function of the number of nodes; ( c ) DER as a function of the EIRP; ( d ) NEC as a function of the EIRP; ( e ) DER as a function of the number of gateways; and ( f ) NEC as a function of the number of gateways.

Scenario 1.2 In the second scenario, the impact of the effective isotropic radiated power was explored. We modeled 100 uniformly distributed nodes in a 500 m × 500 m deployment area, and a single gateway was located at the point (x = 0, y = 0). We simulated various antennas, such as the isotropic antenna, the 0.173 m dipole antenna, and isotropic antennas with constant gain from 1 dBi to 6 dBi. Figure 2 c,d show that the obtained performance metrics of the network are improved as the EIRP increases. Moreover, we can detect an increase of 4.41% by replacing the antenna from dipole (EIRP = 12.15 dBm) to isotropic antenna with a constant of gain 6 dBi (EIRP = 16 dBm).

Scenario 1.3 In this series of simulations, we appraise the performance of 100 LoRa nodes deployed in the same 500 m × 500 m area using multiple gateways. Firstly, a single GW 0 was located at the point (x = 0, y = 0). Then, a second GW 1 was placed at (x = x max /2, y = y max /2). In the next set of simulations, we used the first GW 0 , a GW 2 located at (x = x max /2, 0), and a GW 3 located at (0, y = y max /2). For the last set, we used all of the previous gateways in the deployment area. This gateway layout was selected for simplicity. The performance of the network is increased by using more gateways, as shown in Figure 2 e,f. When the number of gateways is increased from 1 to 2, we obtained an increase of 12.75% in the network performance. This increase is affected by the location of the second gateway. We selected to place it at the point (x = x max /2, y = y max /2) to reduce the distance between the gateways and the nodes. The simulations show that, if we place the second gateway at the other side of the deployment area, for example, at the point (x = x max , y = y max ), the increase in the performance is changed to 11.2%.

4.1.2. Okumura–Hata Path Loss Model

Scenario 1.4 In the next set of simulations, six different deployment areas were selected. The smallest deployment area was set to 5 km × 5 km, the largest deployment area was set to 15 km × 15 km, and the rest of the deployment areas were selected between those boundaries. A gateway was located at (x = 0, y = 0) and 100 nodes were placed at each deployment area. Figure 3 a,b show that the efficiency of the network decreases by expanding the deployment area’s size. Regarding the percentage of the packets that arrive at the gateway having a power level below the minimum sensitivity level, we can divide the area into four zones, as shown in Figure 4 , namely 0–7 km, 7–11 km, 11–14 km, and more than 14 km. Within a 7 km range from the gateway, every node can successfully communicate with the gateway. In the 7–11 km range, 2.89% of the nodes cannot reach the gateway. In the third zone, 11–14 km, the figure rises from 2.89% to 24%. Finally, above 14 km, the number of nodes that cannot communicate with the gateway has significantly increased.

Okumura–Hata model in the rural environment. ( a ) DER as a function of the size of the deployment area; ( b ) NEC as a function of the size of the deployment area; ( c ) DER relative to the transmission power; ( d ) NEC relative to the transmission power; ( e ) DER relative to the spreading factor; and ( f ) NEC relative to the spreading factor.

The area divided into zones.

Scenario 1.5 In this scenario, we analyze the influence of the TP on the main performance metrics of a LoRa network. We placed a gateway at (x = 0, y = 0) and 100 nodes uniformly distributed at the deployment space that was selected to be 8 km × 8 km. Figure 3 c,d show that with a growing value from -4 dBm to 10 dBm, the performance of the network increases. For example, by increasing the TP from 2 dBm to 6 dBm, the performance is increased by 45.3%. A threshold is obtained at 11 dBm and that means that we can achieve the maximum performance of the network in the specific deployment area that we have simulated, by choosing between a big range of transmission power values.

Scenario 1.6 In this scenario, we selected the same layout as in Scenario 1.6. The TP was set to 10 dBm and we obtained the performance of the network by changing the value of SF. Figure 3 e,f show that, while we increase the value of the SF, the time on air is also increased. Consequently, we observe an increase in the energy consumption of the network. For example, by changing the SF from SF11 to SF12, the energy consumption increases by 50.76%.

4.2. Case Study 2—Urban Environment

The urban environment was simulated using the log-distance path loss model, the Okumura–Hata model, and the Oulu city model. To simulate the urban environment, the transmission power was chosen between 2 and 14 dBm, the code rate was set to 4/8, the bandwidth was set to 125 kHz, the carrier frequency was selected to 868 MHz, and the spreading factor was in the range of 7–12 ( Table 2 ).

Simulation parameters in urban environment.

4.2.1. Log-Distance Path Loss Model with Shadowing

Scenario 2.1 In this set of simulations, three different deployment areas were selected. Deployment area A was set to 30 m × 30 m, B was set to 50 m × 50 m, and C was set to 100 m × 100 m. The performance of a LoRa network was evaluated for an increasing number of nodes. Figure 5 a,b show that, while the number of nodes increases from 100 to 1000, the efficiency of the network is decreasing and comes as a result of the increase in the number of collisions between the packets. The number of this decrease compared to the rural environment is lower due to the range of the TP and SF values which were used to simulate the nodes in an urban environment. In the urban environment, the number of undetectable packages depends on the combination of the location, the TP, and the SF of the nodes. For example, by simulating 100 nodes in deployment area C, the percentage of the packages that reach the gateway with a power level lower than −100 dBm was almost 39%. With an increasing number of nodes, the percentage of undetectable packages was slightly reduced. As reflected by the previous simulations, the size of the deployment space affects the achieved performance.

Log-normal shadowing model in the urban environment: ( a ) DER as a function of the number of nodes; and ( b ) NEC as a function of the number of nodes.

4.2.2. Okumura–Hata Path Loss Model

Scenario 2.2 Six deployment areas were modeled to evaluate the performance of the network in this set of simulations. The smallest deployment area was set to 500 m × 500 m, and the largest deployment area was set to 3 km × 3 km. A gateway was modeled at a height of 30 m from the ground at (x = 0, y = 0), and 600 nodes were located uniformly at each deployment area at a height of 1 m. Figure 6 a,b show that the performance of the network slowly decreases by increasing the deployment area’s dimensions.

Okumura–Hata path loss model in an urban environment: ( a ) DER as a function of the size of the deployment area; ( b ) NEC as a function of the size of the deployment area; ( c ) DER relative to the height of gateway; and ( d ) NEC relative to the height of gateway.

Scenario 2.3 In this scenario, we changed the height of the gateway in deployment area B. When we raise the height of the gateway, the performance of the network is increased, as shown in Figure 6 c,d. For example, by changing the height from 30 m to 35 m, the performance is increased by 5% which is very important because we can deploy the same network in a wider area (1.5 km × 1.5 km) without changing any configuration parameters.

4.2.3. Oulu Path Loss Model

Scenario 2.4 At this setup, we used the Oulu city path loss model to evaluate the performance of a LoRa network ( Table 3 ). The deployment area was set to be 2 km × 2 km and a gateway was located at the point (x = 0, y = 0) at 24 m from sea level. Figure 7 a,b show the results for a growing number of nodes by simulating the different propagation models.

Oulu city path loss model in an urban environment: ( a ) DER as a function of the number of nodes; and ( b ) NEC as a function of the number of nodes.

Simulation parameters in the city of Oulu [ 36 ].

The performance of the network decreases when we increase the number of nodes due to the frequent incidents of collisions between the packets. As expected, the best results are obtained using the free space path loss model. On the other hand, when the car model was used, we detected the lowest value in the performance of the network. The car model was selected to simulate an area where buildings and other physical obstacles are blocking the path between the nodes and the gateway.

4.3. Case Study 3—Parking Model Environment

In the last series of simulations, we attempted to evaluate the performance of a parking model environment. The deployment area was set to 100 m × 100 m in every simulation set. Firstly, to model 600 nodes in the urban environment, we used the European regional parameters. Then, for the same number of nodes, we selected the TP to be fixed at 10 dBm and the SF at 7 to simulate a parking area with a large number of identical nodes. Finally, 50 nodes were simulated using the appropriate parameters to model the LoRa physical layer in the rural environment.

Figure 8 a,b show the results for the three different cases. Comparing the urban model to the parking area model, we can see that the data extraction rate of the network is decreased by 38.79% when we select identical nodes. This decrease is caused due to a large number of collisions between the packets. Regarding the energy consumption of the network, we observe that it slightly decreased by 2% in the parking area model. Finally, we can obtain that the performance in the rural environment is significantly high and the data extraction rate is very close to the highest value.

Parking model: ( a ) DER relative to the type of area; and ( b ) NEC relative to the type of area.

5. Conclusions

In this work, an assessment of LoRa networks was presented in three different use cases: the rural environment, the urban environment, and the parking model environment. Firstly, we explored the way in which the network density impacts performance in LoRa networks in both rural and urban environments. The results show that, while the amount of nodes increases, the network’s performance decreases. This is more obvious in the implementation of rural environments because we modeled nodes with identical technical characteristics. Moreover, we explored the impact of EIRP in our model and highlighted the importance of the number and the location of the gateways in the performance of LoRa networks by selecting the log-distance path loss model with shadowing. The impact of the transmission power and the spreading factor was explored using the Okumura–Hata path loss model. By increasing the transmission power, the number of the delivered packages was also increased until the stimulation upper threshold of 11 dBm was reached. While we increased the spreading factor from SF 11 to SF 12, the energy consumption of the network was increased by 50.76%. Furthermore, the results show that the selection of the dimensions of the deployment area and the height of the gateway are very important to implement more efficient networks. By choosing the Oulu path loss model, we managed to record the impact of the propagation model on the network performance. Finally, we simulated a parking model area to underline the network behavior by changing the environmental parameters of the modeled area. As future work, the current project can be extended in several directions, such as performing experimental evaluations in a real-world environment to assess the simulation results. To develop a full picture of the behavior of a LoRa network in specific environments, for example, in a smart agriculture scenario or/and in an urban area in Greece, we are planning to simulate and experimentally implement LoRa networks in order to define the numeric parameters of the path loss model taking into consideration the different type of crops in the agriculture scenario and the specifications of the urban area, respectively. Finally, further research should be undertaken to investigate the improvement of the performance of LoRa networks using optimization algorithms.

Funding Statement

This work has received funding from the European Union’s Horizon 2020 research and innovation program under grant agreement No. 957406 (TERMINET).

Author Contributions

Conceptualization, A.I.G., A.D.B. and S.K.G.; methodology, A.I.G., A.D.B. and S.K.G.; software, A.I.G. and A.D.B.; validation, A.I.G., A.D.B. and S.K.G.; formal analysis, A.I.G., A.D.B. and S.K.G.; investigation, S.K.G.; resources, P.S.; data curation, S.K.G.; writing—original draft preparation, A.I.G.; writing—review and editing, A.D.B. and S.K.G.; visualization, A.I.G., A.D.B. and S.K.G.; supervision, S.K.G., S.W., P.S., K.E.P. and G.K.; project administration, S.K.G. All authors have read and agreed to the published version of the manuscript.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Data availability statement, conflicts of interest.

The authors declare no conflict of interest.

Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

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Top 10+ IoT Research Topics for 2024 [With Source Code]

Home Blog others Top 10+ IoT Research Topics for 2024 [With Source Code]

With new applications being created every day, the Internet of Things (IoT) is one of the technologies that is expanding the fastest in the world right now. The Internet of Things (IoT) is a network of physical objects like cars, appliances, and other household things that are equipped with connectivity, software, and sensors to collect and share data. IoT is revolutionizing the way we live and work, creating new opportunities for businesses, governments, and individuals alike.

In this blog, we will discuss the top 10 Internet of Things research topics and ideas for 2024. We will also provide a comprehensive guide on how to choose the best IoT research topic and discuss some of the challenges and ethical considerations in IoT research.

Meanwhile, if you’ve always been fascinated by the world of coding and design and looking for the ideal place to get started, then Web Development Certificate online is one of the best certifications course you can consider.

IoT: Overview

IoT has numerous applications in various sectors such as healthcare, agriculture, transportation, manufacturing, and smart cities. The data collected from IoT devices can be used to improve decision-making, optimize processes, and enhance customer experiences. If you want to know more about IoT, check out online IoT training .

IoT Research Topics in 2024

Come let’s discuss the top X IoT-based research topics and ideas for 2024.

1. Smart Homes

The idea of a smart home is gaining popularity, and with IoT technology, it has become possible to control and automate various devices in a house. Some of the popular smart home projects include smart lighting, smart security, smart thermostat, and smart appliances.

• Smart Lighting: Smart lighting refers to the use of IoT technology to control the lighting of a house. This can be done by using sensors that detect the presence of people in a room and adjust the lighting accordingly. For example, when someone enters a room, the lights automatically turn on, and when the person leaves, the lights turn off. This can aid in energy conservation and lower electricity costs.
• Smart Security: Smart security refers to the use of IoT technology to enhance the security of a house. This can be done by using sensors and cameras that detect any suspicious activity and alert the homeowners. Smart security can also include features such as remote access control, automatic locking, and real-time monitoring.
• Smart Thermostat: Smart thermostat refers to the use of IoT technology to control the temperature of a house. This can be done by using sensors that detect the temperature of each room and adjust the thermostat accordingly. The ability to remotely operate a smart thermostat can aid in energy conservation and lower electricity costs.

2. Wearable Devices

Wearable devices such as smartwatches, fitness trackers, and medical devices are becoming increasingly popular. IoT technology can be used to develop wearable devices that can collect and analyze data, monitor health parameters, and provide real-time feedback to the user.

• Smartwatches: Smartwatches refer to the use of IoT technology to develop watches that can perform various functions such as making phone calls, sending messages, and tracking fitness. Smartwatches can also be integrated with other devices such as smartphones and laptops.
• Fitness Trackers: Fitness trackers refer to the use of IoT technology to develop devices that can track physical activity, monitor heart rate, and measure calories burned. Fitness trackers can be used to improve health and fitness and can also be integrated with other devices such as smartphones and laptops.
• Medical Devices: Medical devices refer to the use of IoT technology to develop devices that can monitor and track various health parameters such as blood pressure, glucose levels, and oxygen saturation. Medical devices can be used to improve patient care and can also be integrated with other devices such as smartphones and laptops.

3. Smart Agriculture

IoT technology can be used to develop smart agriculture solutions that can improve crop yields, reduce water consumption, and increase efficiency. Some of the popular smart agriculture projects include precision farming, soil monitoring, and crop monitoring.

• Precision Farming: Precision farming refers to the use of IoT technology to develop farming techniques that can help farmers optimize their crop yields. This can be done by using sensors that detect soil moisture, temperature, and nutrient levels, and adjusting the amount of water and fertilizer used accordingly.
• Soil Monitoring:  Soil monitoring refers to the use of IoT technology to develop devices that can monitor soil conditions such as pH levels, temperature, and moisture content. Soil monitoring can help farmers make informed decisions about crop management and reduce the amount of water and fertilizer used.
• Crop Monitoring: Crop monitoring refers to the use of IoT technology to develop devices that can monitor crop growth and health. This can be done by using sensors that detect the amount of sunlight, temperature, and humidity, and provide real-time feedback to farmers. Crop monitoring can help farmers identify and address any issues that may affect crop growth and yield.

4. Smart Cities

Smart cities refer to the use of IoT technology to develop cities that are more efficient, sustainable, and livable. Some of the popular smart city projects include smart transportation, smart energy, and smart waste management.

• Smart Transportation: Smart transportation refers to the use of IoT technology to develop transportation solutions that are more efficient and sustainable. This can include features such as real-time traffic monitoring, intelligent traffic routing, and smart parking.
• Smart Energy:  Smart energy refers to the use of IoT technology to develop energy solutions that are more efficient and sustainable. This can include features such as smart grids, renewable energy sources, and energy-efficient buildings.
• Smart Waste Management:  Smart waste management refers to the use of IoT technology to develop waste management solutions that are more efficient and sustainable. This can include features such as smart bins that detect when they are full and automatically alert waste collection services.

5. Industrial IoT

Industrial IoT refers to the use of IoT technology to develop solutions that can improve efficiency and productivity in industries such as manufacturing, transportation, and logistics. Some of the popular industrial IoT projects include predictive maintenance, asset tracking, and supply chain optimization.

• Predictive Maintenance: Predictive maintenance refers to the use of IoT technology to develop maintenance solutions that can detect and address issues before they become major problems. This can include features such as real-time monitoring of machinery and equipment, and predictive analytics that can identify potential issues.
• Asset Tracking: Asset tracking refers to the use of IoT technology to develop solutions that can track the location and status of assets such as machinery and vehicles. This can include features such as real-time tracking, geofencing, and alert notifications.
• Supply Chain Optimization: Supply chain optimization refers to the use of IoT technology to develop solutions that can optimize supply chain operations such as inventory management, logistics, and shipping. This can include features such as real-time tracking of shipments, predictive analytics, and automated inventory management.

6. Smart Health

Smart health refers to the use of IoT technology to develop solutions that can improve patient care, reduce costs, and enhance overall health outcomes. Some of the popular smart health projects include remote patient monitoring, medication management, and personalized health tracking.

• Remote Patient Monitoring: Remote patient monitoring refers to the use of IoT technology to monitor patients remotely and provide real-time feedback to healthcare providers. This can include features such as wearable devices that monitor vital signs and alert healthcare providers if any issues arise.
• Medication Management: Medication management refers to the use of IoT technology to develop solutions that can help patients manage their medications more effectively. This can include features such as smart pillboxes that remind patients to take their medications and alert healthcare providers if medications are missed.
• Personalized Health Tracking: Personalized health tracking refers to the use of IoT technology to develop solutions that can track and analyze individual health data such as activity levels, sleep patterns, and dietary habits. This can help individuals make informed decisions about their health and well-being.

7. Smart Retail

Smart retail is an emerging application of IoT technology that is changing the way we shop. The goal of smart retail is to provide customers with a more personalized and efficient shopping experience while also improving the efficiency and profitability of retailers. Here are some more details on some popular smart retail applications:

• Smart Shelves:  Smart shelves are shelves equipped with IoT sensors that detect when products are running low or out of stock. This data is sent to the retailer's inventory management system, which can then automatically order more inventory. Smart shelves can also be used to display product information, promotions, and customer recommendations.
• Smart Inventory Management:  Smart inventory management refers to the use of IoT technology to track inventory levels in real time. This can help retailers to optimise their inventory levels, reduce waste, and avoid stockouts. Smart inventory management can also help retailers to automate their ordering and fulfilment processes.
• Personalized Shopping Experiences: Personalized shopping experiences refer to the use of IoT technology to provide customers with tailored product recommendations and promotions. This can be done by analyzing customer data, such as purchase history and browsing behavior, and using machine learning algorithms to generate personalized recommendations.

8. Energy IoT

The energy industry is also poised for transformation through the use of IoT technology. Energy IoT solutions can help companies optimize energy usage, reduce waste, and improve sustainability. Some project ideas for energy IoT include:

• Smart Grids: A system that uses sensors and data analytics to optimize the distribution of energy, reducing waste and improving efficiency.
• Energy Management: A system that uses sensors to monitor energy usage in buildings, identifying areas where energy usage can be reduced and optimizing the energy usage of appliances and lighting.
• Renewable Energy Monitoring: A system that uses sensors to monitor the performance of renewable energy systems, optimizing energy production and reducing downtime.

9. Transportation IoT

IoT technology is also transforming the way we move people and goods. Transportation IoT solutions can help optimize transportation networks, reduce traffic congestion, and improve safety. Some project ideas for transportation IoT include:

• Connected Vehicles: Vehicles that are equipped with sensors and connectivity, allowing them to communicate with each other and with infrastructure to optimize traffic flow and improve safety.
• Intelligent Transportation Systems: A system that uses sensors and data analytics to optimize traffic flow, reducing congestion and improving safety.
• Smart Parking:  A system that uses sensors and data analytics to optimize parking availability, reducing search times and improving the parking experience for drivers.

10. Hospitality IoT

IoT technology can help hotels and other hospitality businesses improve the guest experience, increase efficiency, and reduce costs. Some project ideas for hospitality IoT include:

• Smart Room Controls: A system that uses sensors and connectivity to allow guests to control lighting, temperature, and other room features from their smartphones or other devices.
• Asset Tracking: A system that uses sensors to track the location and condition of hotel assets, improving supply chain visibility and reducing the risk of theft or loss.
• Guest Analytics: A system that uses sensors to track guest behavior and preferences, allowing hotels to offer personalized recommendations and improve the guest experience.

11. Aerospace IoT

IoT technology can help aerospace companies improve safety, increase efficiency, and reduce costs. Some project ideas for aerospace IoT include:

• Predictive Maintenance: A system that uses sensors and data analytics to predict when aircraft equipment is likely to fail, allowing for maintenance to be performed before a breakdown occurs.
• Fuel Optimization:  A system that uses sensors and data analytics to optimize fuel usage, reducing waste and increasing efficiency.
• Air Traffic Management:  A system that uses sensors and data analytics to optimize air traffic flow, reducing congestion and improving safety.

Top Futuristic IoT Research Ideas

• Human-Computer Interaction:  Develop interfaces that can interpret human behavior and emotions to enhance IoT systems' responsiveness and personalization.
• Augmented Reality and IoT:  Combine IoT with augmented reality to create immersive experiences in areas such as education, entertainment, and marketing.
• Quantum Computing and IoT:  Investigate how quantum computing can enhance IoT systems' performance, security, and scalability.
• Swarm Intelligence and IoT:  Explore how swarm intelligence can be applied to IoT systems to enable self-organizing and self-healing networks.
• IoT and 5G:  Investigate how 5G networks can enhance IoT systems' performance, reliability, and scalability.
• Smart Cities and IoT:  Develop smart city solutions that can improve urban planning, transportation, energy efficiency, and citizen engagement.

How to Choose the Best IoT Research Topic?

Choosing the best IoT research topic can be a challenging task. Here are some tips to help you choose the best IoT research topic:

• Think on how feasible and useful the research is:  Choose a topic that aligns with your interests and passions to stay motivated and engaged throughout the research process.
• Identify emerging trends and challenges:  Choose a topic that addresses emerging trends and challenges in the IoT industry to make a significant contribution to the field.
• Consider the feasibility and practicality of the research:  Choose a topic that is feasible and practical to research given the available resources, expertise, and time constraints.
• Seek input from experts and mentors:  Consult with experts and mentors in the field to get feedback and guidance on potential research topics.
• Evaluate the potential impact of the research:  Choose a topic that has the potential to make a significant impact on the IoT industry or society as a whole.

Things to Consider While Choosing IoT Research Topics

Here are some additional things to consider while choosing IoT topics for research:

•  Ethical considerations:  Consider the ethical implications of the research, such as data privacy, security, and transparency.
• Interdisciplinary nature:  Consider the interdisciplinary nature of IoT research and seek to collaborate with experts from different fields to broaden the scope of the research.
• Data management:  Consider how to manage the massive amount of data generated by IoT devices and ensure the accuracy, reliability, and integrity of the data.
• Scalability:  Consider how to design IoT systems that can scale up to accommodate the increasing number of devices and data.

IoT is a rapidly growing field that offers numerous opportunities for research and innovation. In this blog, we discussed the top 10 research topics on IoT for 2024, as well as some futuristic IoT research ideas. We also provided a comprehensive guide on how to choose the best IoT research topic and discussed some of the challenges and ethical considerations in IoT research. By choosing the right research topic and addressing emerging trends and challenges, you can make a significant contribution to the IoT industry and society as a whole. In addition to the project, you can also take advantage of KnowledgeHut Software Development Certification training to learn multiple programming languages and enhance your value in the job market.

Frequently Asked Questions (FAQs)

IoT research involves studying the technologies, applications, and challenges related to the Internet of Things (IoT) to develop new solutions and improve existing ones. 

Some current trends in IoT research include edge computing, machine learning and artificial intelligence (AI), security and privacy, and smart cities. 

IoT research can be used in industry to develop and improve products and services, optimize processes, and enhance customer experiences. It can also help companies to reduce costs, increase efficiency, and improve safety. 

Some ethical considerations in IoT research include privacy, data security, transparency, consent, and the potential for bias or discrimination. 

Some challenges in IoT research include interoperability, scalability, data management and analysis, energy efficiency, and the need for standardization and regulation. 

Geetika Mathur

Geetika Mathur is a recent Graduate with specialization in Computer Science Engineering having a keen interest in exploring entirety around. She have a strong passion for reading novels, writing and building web apps. She has published one review and one research paper in International Journal. She has also been declared as a topper in NPTEL examination by IIT – Kharagpur.

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