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The Internet of Things: Definitions, Key Concepts, and Reference Architectures

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• Theo Lynn 7 ,
• Patricia Takako Endo 8 , 9 ,
• Andrea Maria N. C. Ribeiro 10 ,
• Gibson B. N. Barbosa 10 &
• Pierangelo Rosati 7  

Part of the book series: Palgrave Studies in Digital Business & Enabling Technologies ((PSDBET))

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This chapter introduces the Internet of Things (IoT) and presents definitions and a general framework for conceptualising IoT. Key concepts and enabling technologies are summarised followed by a synthesis and discussion of the current state-of-the-art in IoT Reference Architectures.

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Internet of Things

• Internet of things
• IoT Reference Architecture

1.1 Introduction

The Internet has evolved in a series of waves (Cisco 2012 ). The first three waves were device-centric. In the first wave, we went to a device, typically a desktop PC, to access the Internet. As mobile computing evolved, soon we brought our own devices with us and could access the Internet anywhere anytime. Today, we are in the midst of the so-called Internet of Things (IoT) where devices (things) are connected to the Internet and each other. These things comprise a multitude of heterogeneous devices ranging from consumer devices, such as mobile phones and wearables, to industrial sensors and actuators. Gartner ( 2017 ) estimated only 8.4 billion things were connected in 2017 representing just over 0.5% of the total estimated connectable physical objects worldwide.

This objective of this chapter is to introduce readers to the Internet of Things. The remainder of the chapter is organised as follows. First, we will explore perspectives on the definition of the Internet of Things (IoT) followed by key constructs and concepts underlying IoT including a general research framework for conceptualising IoT. Then, we will delve into a further level of granularity and present a selection of IoT Reference Architectures before concluding.

1.2 Defining the Internet of Things

The Internet of Things (IoT) has rapidly grown in prominence in the last ten years and, yet, it means different things to different people. Indeed Whitmore et al. ( 2015 ) note that there is no universal definition of IoT. Two main conceptualisations exist—the technical and socio-technical perspectives. The first, the pure technical perspective, views IoT as an assemblage and ecosystem of technical artefacts. It is defined by reference to these artefacts and their capabilities. These range in detail. For example, Weyrich and Ebert 2016 , p. 1) define IoT as being “ […] about innovative functionality and better productivity by seamlessly connecting devices. ” In contrast, Tarkoma and Katasonov ( 2011 , p. 2) is significantly more detailed defining IoT as a “ global network and service infrastructure of variable density and connectivity with self-configuring capabilities based on standard and interoperable protocols and formats [which] consists of heterogeneous things that have identities, physical and virtual attributes, and are seamlessly and securely integrated into the Internet. ” Similarly, Whitmore et al. ( 2015 , p. 1) define the IoT as “ a paradigm where everyday objects can be equipped with identifying, sensing, networking and processing capabilities that will allow them to communicate with one another and with other devices and services over the Internet to achieve some objective. ” Unsurprisingly, given the nature of these definitions, they dominate Computer Science literature.

The socio-technical perspective of IoT recognises not only the technical artefacts but also the associate actors and processes with which the IoT interacts. For example, Haller et al. ( 2009 ) recognises the role of the connected objects as active participants in business processes. They define the IoT as “ a world where physical objects are seamlessly integrated into the information network, and where the physical objects can become active participants in business processes. Services are available to interact with these ‘smart objects‘ over the Internet, query their state and any information associated with them, taking into account security and privacy issues ” (Haller et al. 2009 , p. 15). Shin ( 2014 , p. 25) argues that the IoT is part of “ wider, socio-technical systems, comprising humans, human activity, spaces, artefacts, tools and technologies. ” Indeed, Shin et al. note that in some instances, a biological entity may, in fact, be considered the connected thing, for example a human with a heart monitor implant or a farm animal with a biochip transponder.

This perspective taken in this book is not particularly concerned with a specific IoT-related definition or problem. Figure 1.1 below presents a general research framework for conceptualising IoT research. It is general in that it is capable of being used to understand IoT related problems and research questions in conjunction with widely accepted levels of generalisation (abstraction) in both the social sciences (nano, micro, meso, macro) and computer sciences (computation, algorithmic/representational, physical/implementation). Furthermore, it provides a sufficiently general abstraction of the IoT in that it facilitates sense making without getting in to a non-generalisable level of granularity.

A general framework for conceptualising big data research in the Internet of Things

In this framework, five core entities are identified and defined—social actors, things, data, networks, and events. Each of these entities has a myriad of characteristics that may change and evolve over time and inflect our understanding of how value can be generated and captured at different units of analysis:

Social Actors (S) , while typically human, need not be; the framework is flexible enough to accommodate the emerging concept of computers as social actors (Lynn et al. 2015 ; Zhao 2003 ).

Things (T) are primarily physical however they may also be virtual and exist in augmented and/or virtual reality. Two key functional requirements of things in IoT and IoE are data sensing (collating data) and network connectivity.

Data (D) here are discrete artefacts that can connect to other entities including other data and may be sourced from first party, second party, or third party sources. It recognises the existence of an IoT data chain. For example, Radio frequency identification (RFID) enables the tracking of objects through an electronic product code (EPC) serving serves as a link to data about the object that can queried over the Internet (Haller et al. 2009 ).

Networks (N) are systems of interconnected entities and are both conduits and entities in themselves. Our framework accommodates networks between different types of IoT entities and those of the same type, for example machine-to-machine (M2M) networks.

Events (E) are occurrences of interest at given time and/or physical or virtual space.

Processes (P) are obviously critical to how entities interoperate in the IoT and comprise general (e.g. communication) and domain-specific processes. They are essential to how value is created, captured, and delivered in the IoT.

All entities and processes take place in an infrastructural setting and the framework recognises that in the IoT, additional data and metadata is created and collated at the infrastructural level. For example, depending on the networking, processing, and storage capabilities of a given device, these activities may be centralised (in the cloud), at the edge (at the device), or in an intermediary layer (the fog) and not only store or process this data but also may extract other hardware, software, functional use, or other ambient data that can provide different and/or new insights. Finally, each IoT use case is situated in space (physical or virtual) and time and it is against this context that different types of events occur and impact the IoT.

As the IoT can be explored from numerous perspectives, we argue that such a research framework can play an important role for researchers to make sense of a complex and dynamic environment and isolate the major constituents of the IoT experience. In addition, the proposed framework can be used as a general-purpose scaffold for crafting research agendas on the IoT and avoiding duplicated and unfocussed research endeavours.

1.3 Key Concepts and Constructs

IoT revolves around a number key concepts and enabling technologies including object (thing) identification (e.g. IPv6), information sensing (e.g. RFID, sensors, GPS, etc.), communications technologies for data exchange, and network integration technologies (Shin 2014 ).

It is important to note that legacy computing and telecommunications architectures were not designed with the IoT in mind. The scale of heterogeneous devices and an unprecedented volume, variety and velocity of data combined with an extreme variation in use context require new paradigms in computing. Depending on the use case and service level requirements, IoT devices may require processing and storage locally, in the cloud or somewhere in between. In addition cloud computing, edge, fog, and dew computing are three new computing paradigms designed to support IoT. While beyond the scope of this chapter, it is useful to be aware of these concepts and technologies when consider the architectures in Sect. 1.4 . Table 1.1 provides a brief definition for technology.

1.4 IoT Reference Architectures

IoT devices are being used in a wide range of domains such as health, agriculture, smart cities, and process automation. The ‘things’ used can be characterised by their heterogeneity in terms of computing resources (processing, memory, and storage), network connectivity (communication protocols and standards) and software development (high degree of distribution, parallelisation, dynamicity). While such heterogeneity enables the depth and breadth of applications and use cases, it also introduces complexity, particularly with respect to expected service level requirements, for example, user and device mobility, software dependability, high availability, scenario dynamicity, and scalability. As such, an abstraction layer to promote interoperability amongst IoT devices is needed. However, lack of standardisation means that such interoperability is lacking (Cavalcante et al. 2015 ). Reference Architectures can help IoT software developers to understand, compare, and evaluate different IoT solutions following a uniform practice.

Several Reference Architectures have been proposed in order to standardise concepts and implementation of IoT systems in different domains. Breivold ( 2017 ), for instance, conducted a comparative study with eleven different Reference Architectures. This chapter focuses on the those Reference Architectures that enable IoT integration with cloud computing and/or fog and edge computing i.e. across the cloud to thing (C2T) continuum. Figure 1.2 shows the timeline containing the main Reference Architectures that support IoT across the C2T continuum, namely IoT Architectural Reference Model (IoT ARM), IEEE P2413 (IEEE P2413 2014 ), Industrial Internet Reference Architecture (IIRA) (Lin et al. 2019 ), WSO2 IRA, Intel SAS, Azure IRA, and SAT-IoT.

Timeline of selected IoT Reference Architectures

Each of the architectures below can be explored through the lens of the framework presented in Sect. 1.2 and embodies the key concepts and constructs discussed in Sect. 1.3 .

1.4.1 Internet of Things Architectural Reference Model (IoT ARM)

The IoT-A project (IoT-A 2019 ) groups the specificities of IoT functionalities and defines the IoT Architectural Reference Model (IoT ARM) to support the usage, the development and the analysis of different IoT systems, from communication to service level.

According to Bauer et al. ( 2013 ), the main contributions of the IoT ARM are twofold: (a) the Reference Model itself, which contains a common understanding of the IoT domain and definitions of the main IoT entities and their basic relationships and interactions; and (b) the Reference Architecture per se , which provides views and perspectives to generate IoT architectures adapted to one’s specific requirements. This way, the Reference Model and the Reference Architecture provide abstraction levels (models, views and perspectives) to derive concrete IoT solutions (i.e. IoT ARM compliant IoT architectures and systems) (Fig. 1.3 ).

Derivation from each IOT ARM step

The Reference Architecture is independent from a specific use-case or application and includes three views: (a) functional, (b) information, and (c) deployment and operation. The functional view describes the function components of a system; these include components’ responsibilities, default functions, interfaces, and interactions. The architecture is composed of five longitudinal functionality groups (FGs), namely service organisation, IoT process management, virtual entity, IoT services, communication, and two transversal FGs, namely management and security.

The information view covers the information life cycle in the IoT system, providing an overview of the information structures and flows (i.e. how information is defined, structured, exchanged, processed, and stored), and the list of the components involved in the process.

Lastly, the deployment and operation view has an important role in the realisation of IoT systems as they are bringing together a number of devices, each of which has different resources and connection interfaces, which can be interconnected in numerous ways. The deployment and operation view provides a set of guidelines for system design, covering different aspects of technologies, communication protocols, services, resources, and information storage.

According to Bauer et al. ( 2013 ), evolution and interoperability, availability and resilience, trust, security and privacy, and performance and scalability are the most important for perspectives for IoT systems.

Bauer et al. ( 2013 ) also present a reverse mapping to demonstrate how the concepts of the IoT ARM can be presented to existing architectures and to validate their proposal. One of the use cases was based on the use of RFID for tracing the towels before, during, and after the surgery to avoid towels being left on the patient’s abdomen. This use case was also based on the use of a cloud infrastructure for data storing. Even though the authors argue that the IoT ARM mapping was successfully done, there is no way to say that it can be applied to any existing concrete architecture.

1.4.2 IEEE Standard for an Architectural Framework for the Internet of Things (P2413)

To avoid silos in domain-specific standards, P2413 is a unified architectural framework for IoT. As well as defining the framework, it includes descriptions of various IoT domains, definitions of IoT domain abstractions, and identification of commonalities between different IoT domains (energy, media, home, transport etc.). It provides a reference model that defines relationships among various IoT verticals and common architecture elements. In this way it has similar design principles to IoT ARM. The Reference Architecture covers the definition of basic architectural building blocks and their ability to be integrated into multi-tiered systems. The Reference Architecture also addresses how to document and mitigate architecture divergence. P2413 also includes a blueprint for data abstraction and addresses the need for trust through protection, security, privacy, and safety. Applying P2413, the architectural transparency of IoT systems can be improved to provide benchmarking, safety, and security assessments.

The P2413.1 is the Standard for a Reference Architecture for Smart City (RASC) (P2413.1 2019 ). The RASC provides an architectural design for the implementation of a smart city, enabling interaction and interoperability between domains and system components. The smart city applications may include water management, waste management, street lighting, smart parking, environmental monitoring, smart community, smart campus, smart buildings, e-health, e-government, etc. The RASC includes the Intelligent Operations Center (IoC) and IoT.

The P2413.2 is the Standard for a Reference Architecture for Power Distribution IoT (PDIoT) (P2413.2 2019 ). Following a similar idea of RASC, the PDIoT also provides an architectural design but for implementing power distribution systems, covering different domains, such as legacy grid systems, IoT and cloud computing. This standard defines a cloud based power distribution which supports microservices and migration from legacy systems to IoT based platforms.

1.4.3 Industrial Internet Reference Architecture (IIRA)

The term ‘Industrial Internet’ is largely attributed to General Electric (GE). In a joint report, Accenture and GE (2014, p. 7) define the industrial internet as an architecture that:

[…] enables companies to use sensors, software, machine-to-machine learning and other technologies to gather and analyse data from physical objects or other large data streams—and then use those analyses to manage operations and in some cases to offer new, value-added services.

Today, the Industrial Internet has evolved in to the Industrial Internet of Things (IIoT). IIoT is defined Boyes et al. ( 2018 , p. 3) as:

A system comprising networked smart objects, cyber-physical assets, associated generic information technologies and optional cloud or edge computing platforms, which enable real-time, intelligent, and autonomous access, collection, analysis, communications, and exchange of process, product and/or service information, within the industrial environment, so as to optimise overall production value.

Somewhat like IoT ARM and P2413, the Industrial Internet Reference Architecture (IIRA) (Lin et al. 2019 ) is an architecture framework to develop interoperable IIoT systems for diverse applications across industrials verticals.

IIRA is composed of one frame and different representations (Fig. 1.4 ). According to (Lin et al. 2019 ), a frame is a collection of concepts represented by stakeholders (individual, team, organisation having interest in a system), concerns (any topic of interest pertaining to the system), and viewpoints (conventions framing the description and analysis of specific system concerns). Representations are defined as views and models, which are collections of the results obtained through the application of the architecture frame to abstracted or concrete systems. These models and views are chosen for addressing a specific concern at an appropriate level of abstraction (Lin et al. 2019 ).

Industrial internet Reference Architecture. (Adapted from Lin et al. 2019 )

The IIRA identifies the main architectural concerns found in IIoT systems and classifies them into viewpoints related to their respective stakeholders. Viewpoints are critical components in the IIRA; there are four different viewpoints (Fig. 1.5 ). Firstly, the Business Viewpoint is responsible for inserting the vision, values, and objectives of business stakeholders in the commercial and regulatory context. Secondly, the Usage Viewpoint describes how an IIoT system realises its key capabilities, by providing the sequence of activities that coordinates the system components. Thirdly, the Functional Viewpoint relates the functional and structural capabilities of an IIoT system and its components. It is decomposed into five main functional domains: control domain, operation domain, information domain, application domain and business domain. Finally, the Implementation Viewpoint provides (1) a description the general architecture of an IIoT system, (2) a technical description of its components, (3) an implementation map of the activities identified in the Usage Viewpoint; and (4) an implementation map for the key system characteristics (Lin et al. 2019 ).

IIRA viewpoints. (Adapted from Lin et al. 2019 )

By adopting IIRA, industries can integrate best practices into their processes, use a generic architecture and common framework and as a result reduce operation expenditure. It should be noted that IIRA provides architectural patterns for both cloud and edge computing.

1.4.4 WSO2 IoT Reference Architecture (WSO2 IRA)

WSO2 is a US-based open source integration vendor. The WSO2 IoT Reference Architecture (WSO2 IRA) is illustrated in Fig. 1.6 and supports IoT device monitoring, management, and interaction, covering the communication process between the IoT and the cloud (Fremantle 2015 ). The WSO2 IRA comprises five horizontal layers (client/external communication, event processing and analytics, aggregation layer, transports, and devices) and two cross-cutting layers (device management and identity and access management). Table 1.2 provides a brief definition of each layer.

WSO2 IoT Reference Architecture. (Adapted from Fremantle 2015 )

1.5 Intel System Architecture Specifications (Intel SAS)

The purpose of the Intel System Architecture Specifications (SAS) is to connect any type of device to the cloud considering five key items: (1) C2T management, (2) real time analytics, (3) interoperability, (4) service and device discovery and provisioning, and (5) security (Intel 2015 ). Intel SAS has two distinct versions that co-exist in order to cover different infrastructure maturity levels: version 1.0 for connecting the unconnected and version 2.0 for smart and connected things. Version 1.0 specifies how legacy devices that were not originally designed to be connected to the cloud can use an IoT gateway to be online. Version 2.0 specifies how to integrate heterogeneous smart things focusing on security, manageability and real time data sharing between things and cloud (Fig. 1.7 ).

Intel system architecture specifications. (Adapted from Intel 2015 )

Intel SAS recommends a layered architecture that encompasses horizontal layers (users, runtime, and developers) and vertical layers (business and security). The data flow involves through eleven steps including analogue-to-digital conversion (ADC), gateways and reaching the cloud. Intel also recommends software components and interfaces to connect legacy devices with no connectivity functionality. The software components are located at endpoint devices and in the cloud. Basically, the cloud software components receive data collected by on-premise components and are responsible for analysis, storage, and service orchestration.

1.5.1 Azure IoT Reference Architecture (Azure IRA)

The Azure IoT Reference Architecture (Azure IRA) represented in Fig. 1.8 relies on Microsoft Azure platform to connect sensors to intelligent services at the cloud. The main goal of Azure IRA is to take actions on business insights that are generated through gathering data from IoT applications (‘things’) (Microsoft 2018 ). The reference document proposes a recommended IoT architecture, describing foundational concepts and principals, IoT subsystems details and solution design considerations. Azure IRA is focused on flexibility. As such, IoT solutions are cloud native and microservice-based. As deployable services are independent of each other, they suggest that it is better for scaling, updating individual IoT subsystems, and flexibility in the selection of technologies per IoT subsystem.

Azure IoT Reference Architecture. (Adapted from Microsoft 2018 )

Figure 1.8 shows the recommended Azure IRA covering both hybrid cloud and edge solution integration. In orange, one can see the core IoT subsystems: IoT devices, cloud gateway (IoT Hub), stream processing, and user interface. The IoT device should be able to register with the cloud gateway, which is responsible for managing the devices. The stream processor consumes and stores the data, and integrates with the business process. For each subsystem, the Azure IRA recommends a specific technology based on Azure services. There is also a set of optional IoT subsystems (in blue): IoT edge devices, data transformation, machine learning, and user management. The edge devices are able to aggregate and/or transform and process the data on premise, while the data transformation (at the cloud) can manipulate and translate telemetry data. The machine learning subsystem allow the IoT system to learn from past data and act properly, such as firing alert to predictive maintenance. Finally, the user management subsystem provides functionality for users to manage the devices.

1.5.2 SAT-IoT

SAT IoT is a platform (Fig. 1.9 ) developed by Spanish company, SATEC, as part of the Horizon 2020 RECAP project. Footnote 1 Smart cities is a primary use case for SAT IoT. As such it needed an architecture that could (1) manage the smart city data network topology at run time, (2) use optimisation techniques that support processing aggregated data by geographical zones, and (3) monitor the IoT system and the optimisation process in run time (Peña and Fernández 2019 ).

The SAT-IoT Architecture. (Adapted from Peña and Fernández 2019 )

Edge/cloud computing location transparency is a core feature of the platform allowing data to be shared between different zones (geographically and from the cloud to the edge), and thus to be processed at any of the edge nodes, mid nodes, or cloud nodes. This is realised by two of the entities in the SaT IoT architecture—the IoT Data Flow Dynamic Routing Entity and the Topology Management Entity. Together, they enable SAT IoT to manage the network topology at run time while also providing the necessary monitoring capabilities to understand the usage pattern and capacity limitations of the infrastructure. The IoT Data Flow Dynamic Routing Entity and the Topology Management Entity are augmented by the integration of the RECAP Application Optimiser in to SAT IoT, which derive the best possible placement of the data processing logic. Figure 1.9 shows the SAT-IoT architecture composed of Physical Layer, Smart Device Entity, IoT Data Flow Collector Entity, IoT Data Flow Dynamic Routing Entity, IoT Topology Management Entity, IoT Visualisation Entity, IoT Cloud Entity, Platform Access Entity, Security and Privacy, and Embedded IoT Applications.

1.5.3 Summary of Architectural Features

Table 1.3 summarises the key functional features addressed in each IoT Reference Architecture, that is interoperability, scalability, security and privacy, data management, analytics, data visualisation and user interface, and supported computing paradigms.

By system interoperability, we mean that the architecture should address connectivity, data management and automatic integration in a transparent way for the end user. Scalability refers to the architecture’s ability to handle increases in the number of IoT devices and endpoints. Security and privacy capability ensures that the information be where it should be and prevents data leakage to unauthorised persons. Data management refers to both the management and exchange of data between architectural components. Analytics refers to the ability of the architecture to capture useful data from the deluge of data that travels on the network. Data visualisation and user interface is related to whether the architecture provides a human interface. Finally, computing paradigm refers to whether the architecture addresses support for new computing paradigms and specifically cloud, fog, edge, and dew computing.

Table 1.3 summarises the key features of different IoT Reference Architectures. It clearly emerges that only two functionalities are met by all Reference Architecture proposals—interoperability and security and privacy. Another common area of focus, unsurprisingly, is data management. Obviously, the primary value driver in the IoT is data and systems are required to manage the volume, velocity and variety of this data, not least where its stored and processes. The IEEE P2413 Reference Architecture presents less functionality; however this is due to the nature of such a standard. It is however the basis for a related smart cities standard (RASC).

When considering the IoT from a business, technical, or research perspective, each of these architecture features should be considered and addressed.

1.5.4 Conclusion

The chapter introduced two perspectives of the Internet of Things—a purely technical and a socio-technical perspective. The Internet of Things is not merely a technical phenomenon. It has the potential to transform how society operates and interacts. As such, it is critical to have a sufficiently general abstraction of the Internet of Things that facilitates sense making without getting in to a non-generalisable level of granularity. We present such an abstraction organised around five entities—social actors, things, data, networks, and events—and the processes that occur between them, all situated in time and space. We provided a brief overview of some of the key enabling technologies and new computing paradigms. Section 1.4 presented seven Reference Architectures for the Internet of Things and compared them across seven dimensions. This provides a further lens with which to consider the Internet of Things.

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Lynn, T., Endo, P.T., Ribeiro, A.M.N.C., Barbosa, G.B.N., Rosati, P. (2020). The Internet of Things: Definitions, Key Concepts, and Reference Architectures. In: Lynn, T., Mooney, J., Lee, B., Endo, P. (eds) The Cloud-to-Thing Continuum. Palgrave Studies in Digital Business & Enabling Technologies. Palgrave Macmillan, Cham. https://doi.org/10.1007/978-3-030-41110-7_1

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

• Sachin Kumar   ORCID: orcid.org/0000-0003-3949-0302 1 ,
• Prayag Tiwari 2 &
• Mikhail Zymbler 1  

Journal of Big Data volume  6 , Article number:  111 ( 2019 ) Cite this article

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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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DOI : https://doi.org/10.1186/s40537-019-0268-2

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