blockchain iot research papers

Advances, Systems and Applications

• Open access
• Published: 14 July 2023

A conceptual architecture for simulating blockchain-based IoT ecosystems

• Adel Albshri 1 , 2 ,
• Ali Alzubaidi 3 ,
• Maher Alharby 4 ,
• Bakri Awaji 5 ,
• Karan Mitra 6 &
• Ellis Solaiman 1  

Journal of Cloud Computing volume  12 , Article number:  103 ( 2023 ) Cite this article

Recently, the convergence between Blockchain and IoT has been appealing in many domains including, but not limited to, healthcare, supply chain, agriculture, and telecommunication. Both Blockchain and IoT are sophisticated technologies whose feasibility and performance in large-scale environments are difficult to evaluate. Consequently, a trustworthy Blockchain-based IoT simulator presents an alternative to costly and complicated actual implementation. Our primary analysis finds that there has not been so far a satisfactory simulator for the creation and assessment of blockchain-based IoT applications, which is the principal impetus for our effort. Therefore, this study gathers the thoughts of experts about the development of a simulation environment for blockchain-based IoT applications. To do this, we conducted two different investigations. First, a questionnaire is created to determine whether the development of such a simulator would be of substantial use. Second, interviews are conducted to obtain participants’ opinions on the most pressing challenges they encounter with blockchain-based IoT applications. The outcome is a conceptual architecture for simulating blockchain-based IoT applications that we evaluate using two research methods; a questionnaire and a focus group with experts. All in all, we find that the proposed architecture is generally well-received due to its comprehensive range of key features and capabilities for blockchain-based IoT purposes.

Introduction

The Internet of Things (IoT) has enabled the interconnection and management of various types of computing and intelligent objects, including sensors, actuators, edge devices, networks and clouds, enabling the communication and processing of vast quantities of data for many applications [ 1 ]. In a typical IoT architecture, things are equipped with sensors that gather data about their environment and transmit it to edge devices or cloud servers for processing [ 2 ]. Then, collected data is transmitted through gateways, which are the second essential component of the IoT. Gateways serve as intermediaries between things , edge devices, and the cloud, facilitating the necessary connectivity, security, and data flow for the Internet of Things [ 3 ]. The third and fourth components of the Internet of Things are local edge devices for local data processing and/or cloud infrastructure, which consists of large virtualized resources such as storage and processing devices with high processing and analytical power. These resources enable large-scale data processing and analysis [ 4 ].

There have been security, privacy, and trust challenges associated with the IoT, which impair the smooth transition to IoT-enabled applications. One specific concern with IoT is that central IoT servers, which manage users’ sensitive data, may pose a risk to privacy and potentially lead to privacy violations [ 5 ]. IoT has a wide range of applications, including health, environmental, and geospatial applications, which often involve the exchange of sensitive and private data. Given the sensitivity of the data being exchanged in many IoT applications, it is important to consider how to ensure that this data has not been modified, tampered with, or misused. This is especially relevant in the context of centralized IoT architectures, which are vulnerable to single points of failure. In addition, the centralized architecture of some IoT applications, which may involve the exchange of large volumes of data, can negatively impact the performance of these applications, potentially slowing them down to dangerous levels. For example, a hospital may not receive critical patient data in a timely manner if the system is slowed down [ 6 ]. Therefore, it is necessary to consider decentralized models for implementing IoT applications to address these performance and security concerns. The peer-to-peer (P2P) model, which enables large exchanges between IoT devices, has the potential to significantly reduce the cost of employing servers [ 7 ]. The peer-to-peer model for the Internet of Things also involves distributing processing tasks over a larger number of devices. As a result of this distributed processing, the failure of a single device in the network will not cause the entire system to fail, which meets the requirement for fault tolerance. In addition, using a peer-to-peer network can help to reduce the significant costs associated with servers, their operating systems, and maintenance  [ 8 ]. However, it is important to note that the peer-to-peer model for the Internet of Things also has well-known security issues [ 9 ]. In this context, blockchain technology, which is an extended, secure peer-to-peer network [ 10 ], may be an effective solution.

More recently, Blockchain technology emerges with the potential to address the issues associated with centralised systems, such as a single point of failure and security vulnerabilities [ 11 ]. That is, Blockchain technology is widely recognized as a tamper-proof and secure technology. The first well-established blockchain-based application is Bitcoin [ 12 ], which is a decentralized digital currency based on a peer-to-peer network. Typically, blockchain technology is often used to secure data by providing a tamper-proof, decentralized, and transparent way of storing and managing data. The decentralized, distributed structure of the blockchain makes it difficult for any single entity to tamper with the data, as any changes to the data would have to be agreed upon by multiple network participants.

The transparency of the blockchain also helps to ensure the integrity of the data, as all network participants can see and verify the data stored on the network. In addition, the use of cryptographic techniques helps to ensure the confidentiality of the data. These features make blockchain technology a powerful tool for securing data in a variety of applications. Therefore, there has been growing interest in integrating blockchain technology into the IoT to address the security and scalability challenges that have emerged in traditional IoT architectures. By using blockchain technology, it is possible to create decentralized, secure, and transparent networks for exchanging data between IoT devices. This can help to ensure the integrity and confidentiality of the data being exchanged, as well as provide a tamper-proof record of the data. In addition, the decentralized nature of blockchain networks can help to improve the fault tolerance and scalability of IoT systems, as the failure of a single device or network participant will not compromise the integrity of the network. These features make blockchain technology a promising solution for securing and scaling IoT networks [ 13 ]. However, both the IoT and blockchain technologies are complex and have many potential applications, making it important to have accurate and effective simulation tools that can model and evaluate these applications before they are deployed in the real world.

Simulation tools can help to identify potential issues and optimize the performance of these systems, making it possible to test and refine the design of these systems before they are deployed. In the case of IoT and blockchain applications, simulation tools can help to evaluate the scalability, security, and reliability of these systems, as well as the performance of various protocols and algorithms. This can help to ensure that these systems are robust and fit for their intended purpose and can save time and resources by identifying and addressing issues before they arise in the real world. Simulation tools are used to study the behavior and performance of systems by examining various parameters and variables [ 14 ]. Thus, simulation tools are particularly useful for studying complex systems that are difficult to analyze or test in the real world [ 15 , 16 ]. Simulation studies can be a cost-effective way to study the behavior and performance of systems, particularly complex systems. In addition, simulation tools can be used to study the performance of a system under different configurations, helping to identify the optimal configuration for a particular application. By using simulation tools, it is possible to analyze and optimize the performance of a system in a controlled and repeatable manner, providing valuable insights into how the system will behave in the real world.

The paper aims to gather the thoughts and insights of experts on developing a simulation environment for blockchain-based IoT applications. Based on these thoughts and insights, a conceptual model is proposed for the simulation environment. To evaluate this, a questionnaire and a focus group method are conducted to evaluate the effectiveness of the conceptual model. The following contributions have been made in this paper:

We conducted mixed methods research to gather the opinions and insights of experts to understand the primary challenges experts face with these types of applications and to use this information to inform the design of a creation simulation environment for blockchain-based IoT applications. By utilizing a mixed methods approach, we were able to gather both qualitative and quantitative data from a diverse group of experts. This allowed us to gain a more comprehensive understanding of the issues and needs surrounding the creation of this simulation tool. Our findings confirm that the participants had a high level of confidence in the ability of blockchain to alleviate IoT issues but also highlighted the need for more tools to evaluate and test this concept.

We proposed a conceptual model for a creation simulation environment for blockchain-based IoT applications that includes various components, mechanisms, and processing elements. The purpose of the proposed conceptual model is to provide a foundation for creating a simulation environment that can be used to test and evaluate the performance of blockchain-based IoT applications.

We conducted a questionnaire and a focus group with experts to evaluate the conceptual model against a set of objectives. The result of the evaluation of the conceptual model showed that it is generally well-regarded. The reason underpinning this attitude is the inclusion of a wide range of key features and capabilities that make it a suitable foundation for creating a simulation environment for blockchain-based IoT applications.

Paper organisation

The structure of this paper is as follows: Section Related work provides an overview of related work in the field. The research methodology of the study is outlined in Section Research methodology . The research methods used to gather insights and perspectives on the design of an appropriate simulator for the study are described in Section Utilized methods to gather requirements . The results of the study are presented in Section Findings . Recommendations based on the findings of the study are presented in Section Recommendation . Section Motivating blockchain-based IoT scenario introduces a motivating example of a blockchain-based IoT scenario. The proposed conceptual architecture for the modelling blockchain for IoT application is presented in Section Conceptual architecture , and the results of evaluating this architecture are discussed in Section Evaluation . The paper concludes with a summary of the main findings and future work in Section Conclusion and future work .

Related work

This section describes the prior works that have been done on simulating blockchain and Internet of Things (IoT) systems. In recent years, there has been a significant amount of research on both blockchain and IoT, and many efforts have been made to develop simulators for these technologies. In the literature, there are several examples of simulators for blockchain systems [ 17 ] and IoT applications [ 18 ].

Blockchain simulators

There have been several efforts to develop simulators for blockchain systems. One of these, VIBES (Visualisations of Interactive Blockchain Extended Simulations) [ 19 ], was proposed as a configurable blockchain simulator to enable end-users to perceive empirical insights and analytics about blockchain networks. VIBES can simulate blockchain systems and mimics the effect of specific parameter changes on the system. The merits of VIBES are twofold. First, VIBES is a scalable simulator as it can simulate systems with thousands of interacting nodes. Second, VIBES is a fast simulator able to provide fast simulation results. Faria and Correia [ 20 ] proposed a discrete-event blockchain simulator referred to as BlockSim that is a framework assisting in designing, implementing and evaluating blockchains. It can evaluate the implementation of different blockchains that are rapidly modelled and simulated. Therefore, BlockSim is characterised as a dynamic simulator able to simulate systems over a certain time interval. Yet another attempt referred to as BlockSim is proposed by Alharby and van Moorsel [ 21 ] that implements proof of work (PoW) as a consensus algorithm for making agreements about the blockchain’s state. Moreover, as a discrete-event simulator, BlockSim can test the effect of different parameter configurations on the system’s performance. Another simulator BlockSIM [ 22 ] is a resilient open-source blockchain simulator that enables blockchain designers to evaluate the performance of their designed private blockchains. The contribution of BlockSIM is twofold. First, it accurately models the stability of the system. Second, it accurately simulates the transaction throughput concerning a given scenario. It can optimise the system’s parameters which, in turn, allows for testing various scenarios needed for the build-up of its chains.

IoT simulators

There have been several efforts to simulate Internet of Things (IoT) ecosystems and the applications that operate within these ecosystems. These simulators can be used to study the behavior and performance of IoT systems, as well as to optimize the design and deployment of these systems in the real world. A cloud layer is normally significant for a wide range of IoT applications; therefore, cloud simulators are widely used for simulating IoT applications. The most popular and widely used is the CloudSim toolkit [ 23 ], in which tasks are created in the form of cloudlets to be processed using virtual machines in the cloud environment. Moreover, it is mainly designed to simulate discrete-event scenarios while implementing a five-layer structure. An interesting aspect of CloudSim is its ability to model CPU power consumption to shed light on bandwidth and delay parameters. Due to its success, an improved extended version has been introduced and referred to as CloudAnalyst [ 24 ]. CloudAnalyst extends the core of CloudSim while adding a set of features to investigate the effect of different configurations on the system’s performance. A prominent simulator for modelling applications on the Edge of IoT networks is iFogSim [ 25 ] which is the extension of the CloudSim simulator. As an edge layer-dependent simulator, it can simulate real systems that consider the different aspects ranging from sensing to processing the data. The main contribution of this simulator is the simulation of the physical layer. In particular, it can model the physical component of systems.

To our knowledge, none of the simulators mentioned above focuses on simulating IoT scenarios (IoT applications that run on multiple IoT layers, including sensors, edge devices, communication networks, and the cloud). This motivated the development of IoTSim [ 26 ]. IoTSim is built over the core of CloudSim to support the task of IoT and big data simulation. IoTSim follows a three-layer architecture (perception, network and application layer). These layers are integrated with the three layers of CloudSim (storage, big data processing and user code). An important point in this simulator is using the MapReduce approach, one of the big data handling approaches. From the practical viewpoint, this is done through two separate functions: MapCloudlet and ReduceCloudLet. Finally, IoTSim-Osmosis [ 27 ] is a framework that supports testing and validating IoT applications using the principle of osmotic computing. It is mainly designed to simulate complex IoT applications while being deployed on heterogeneous edge, cloud and SDN environments.

It appears that, despite the many efforts that have been made to develop simulators for blockchain and IoT systems, there are currently no simulators that focus specifically on simulating the integration of these technologies. Table  1 summarizes the previous work in this area and highlights the lack of focus on simulating the integration of blockchain and IoT systems. This lack of focus on simulating integrated blockchain and IoT systems highlights the need for further research in this area and the potential value of a simulator that can model and evaluate the performance of such systems.

Research methodology

This section presents the research methodology steps to design the conceptual model for creating a simulation environment for blockchain-based IoT applications, which contains several steps as shown in Fig. 1 .

Steps of Research Methodology

Survey of blockchain simulators : The first step in this research methodology is to survey existing blockchain simulators in order to understand the current state of the field and identify potential gaps or areas for improvement. To do this, we carried out a systematic mapping study for existing blockchain simulators [ 17 ] to provide a systemic mapping review of blockchain simulators. This study is done with respect to several quality factors in which we shed light on the configuration parameters (inputs) and produced metrics (outputs) by each simulator. For a deep technical review, a code quality comparison is carried out to assess the source code of the covered simulators. The results reveal that blockchain simulation is still in its infancy stages, and further research must be undertaken in this direction. No simulator fully covers the wide operational range of features and capabilities of existing blockchain technologies. Moreover, existing blockchain simulators have little viability for being integrated with other technologies, such as cloud and IoT.

Utilize a mixed-method approach to gather requirements : The second step of this study is to gather the opinions of experts on the development of a simulation environment for blockchain-based IoT applications using a mixed-method approach. This includes conducting interviews and a questionnaire with experts in the field to understand the needs and preferences of potential users of the simulator. To do this, we conducted this approach including interviews and a questionnaire with domain experts [ 28 ]. These methods allowed us to gain insights into the potential contributions and challenges of blockchain-based IoT applications and to formulate the proposed simulation’s requirements and mechanisms. This process is outlined in relation to several objectives, as follows:

To gather the required information from experts in the field regarding:

The usage of IoT in our daily life.

The most commonly used blockchain types.

The IoT data that should be stored on blockchain.

The consensus algorithms required for the simulator.

The users’ needs as regards the blockchain log.

The possibility of using IoT nodes as blockchain nodes.

To provide analytical information regarding:

Participants’ opinions about having an integrated blockchain IoT simulator.

Participants’ opinions on modelling various types of blockchain in the simulator.

To design a simulator to validate the integrated blockchain IoT systems.

Analysis and recommendation : The third step is to analyze the data collected in the previous steps and make recommendations for designing and implementing a simulation environment for blockchain-based IoT applications. This could involve identifying key features or capabilities that should be included in the simulator, identifying potential challenges or limitations, and suggesting ways to overcome these challenges.

Motivation scenario : The fourth step of the research methodology involves outlining the potential uses and benefits of a simulation environment for blockchain-based IoT applications through a motivation scenario. The purpose of the motivation scenario is to provide a clear understanding of the potential applications and benefits of the proposed simulator. It helps to guide the development and implementation of the simulator by highlighting the specific needs and goals that the simulator should aim to address.

Designing a conceptual model : The fifth step is to propose a conceptual model as a foundation for creating a simulation environment for blockchain-based IoT applications. This involves providing a high-level design in order to represent the fundamental principles (e.g., the main components) and relationships of a system or concept.

Evaluate the conceptual model : The final step is to evaluate the conceptual model using a focus group of experts in the field. This could involve presenting the model to a group of experts and gathering feedback and insights on its strengths, weaknesses, and potential improvements. This feedback can then be used to refine and improve the conceptual model before proceeding with the development of the simulation environment. To do this, it is important to have clear objectives in mind when conducting this evaluation. Therefore, we have applied the SMART criteria [ 29 ] to evaluate the feasibility and effectiveness of our objectives. This helps to ensure that our objectives are specific, measurable, attainable, relevant, and time-bound. To ensure that our conceptual model meets the needs and expectations of stakeholders, we conducted a questionnaire and a focus group with ten experts in the field of blockchain and IoT, as outlined in the Evaluation section. The purpose of both approaches was to gather feedback and insights that would inform the evaluation of the conceptual model. By engaging with experts and gathering their feedback and insights, we aimed to ensure that the conceptual model adequately addresses the needs of all relevant parties. This process is described in relation to several objectives, as follows:

Objective 1 : To evaluate the generalizability and quality of the conceptual model .

IoTSim-Osmosis [ 27 ] is a framework for simulating the behavior and performance of IoT systems across an edge-cloud continuum. It enables users to test different configurations and scenarios in a simulated environment, providing a valuable tool for understanding how different factors may impact the behavior of an IoT system. The IoTSim-Osmosis framework is designed to be flexible and extensible, allowing users to simulate a wide range of IoT systems and scenarios. It offers a set of tools and libraries for building and running simulations, as well as visualizing and analyzing the results. Assuming that IoTSim-Osmosis is the base IoT simulator in the conceptual model, it is important to gather feedback and insights from a diverse group of experts in the fields of blockchain and IoT. This will help us to determine whether the IoTSim-Osmosis simulator meets the needs and expectations of these experts.

Objective 2 : To determine the extent to which the IoTOsmosis simulator meets the requirements of participants .

The conceptual model consists of two main components: the IoT side and the blockchain side. The IoT side focuses on modelling and simulating the devices, sensors, and other components that make up an IoT system. The blockchain side, on the other hand, involves modelling and simulating the nodes, consensus mechanisms, and other elements of a blockchain system. Therefore, It is essential to evaluate the effectiveness of the blockchain component of the simulator in meeting the needs of participants.

Objective 3 : To evaluate the effectiveness of the blockchain component of the conceptual model in meeting the needs of the participants .

Obtaining feedback, criticism, and recommendations from experts can be a useful way to improve the conceptual model for modelling blockchain for IoT applications. Experts in the fields of blockchain and IoT can offer valuable insights and perspectives on the model, which can help identify areas where it may be lacking or where it could be improved.

Objective 4 : To identify areas of the conceptual model that may need improvement .

Utilized methods to gather requirements

Participants.

This paper employed a sequential explanatory design methodology [ 30 ] comprising a questionnaire and interviews. Overall, 25 participants represented the target sample of individuals with knowledge of computer science, with a specific specialisation in IoT and/or blockchain.

Research tools

An online questionnaire with nine closed-ended questions was created using the SurveyMonkey website and distributed to the participants. This was followed by online interviews using the Zoom app with a set of participants who consented to participate. At the end of the interview, participants had the opportunity to complete a form with open-ended questions, which enabled the collection of qualitative data for a high level of analysis. To assess the reliability and consistency of the gathered information, we calculated Cronbach’s Alpha [ 31 ] using SPSS for the 9 questions, resulting in a value of 0.796. This value exceeds 0.5, indicating a high level of reliability and consistency of the gathered data.

Research procedures

First, it was necessary to gather quantitative numerical data through a questionnaire [ 32 ], to develop robust conclusions. Second, qualitative data was gathered through interviews with various participants, using a set of open questions [ 33 ]. The first approach, the questionnaire, was disseminated to approximately 25 participants, all of whom were IoT and blockchain, developers/researchers. With 25 active participants, the statistical analysis was undertaken using SPSS to understand the participants’ attitudes regarding blockchain features. To more effectively communicate the idea, the data analysis results as numeric values are presented in descriptive graphical format. The question responses were provided on a Likert scale from 1 (‘strongly disagree’) to 5 (‘strongly agree’). The findings, presented in the figures, are displayed in the Questionnaire . Regarding the second data collection approach of the interviews, these were undertaken online with six participants who responded to a set of open questions. An in-depth description of this process is presented in  Interviews  section.

Questionnaire

The questionnaire began by asking questions to determine the participants’ familiarity with the IoT, specifically asking, “To what extent are you familiar with IoT?” We received 25 answers, as shown in Fig.  2 . The figure shows that the majority of participants (eight, 32%) are moderately aware of the IoT, while six participants (24%) have moderately low familiarity with the IoT. Additionally, four participants (16%) are highly aware of the IoT, and another five participants (20%) have a moderately high awareness of the IoT. On the other hand, the least number of participants (two, 8%) were completely unaware of the IoT. Overall, the selected participants were a good fit as the majority (moderate and higher) were aware of the IoT.

Participants’ familiarity with the IoT

In addition to examining participants’ familiarity with the IoT, we also looked at their familiarity with blockchain to gain more confidence in their answers. Thus, participants were asked, “To what extent are you familiar with blockchain?” We received 25 responses, shown in Fig.  3 . The figure suggests that the majority of participants (seven, 28%) have a moderately high awareness of blockchain, while six participants (24%) are very familiar with blockchain. The least number of participants (two, 8%) are completely unaware of blockchain, while five participants (20%) have a moderately low awareness of blockchain. Additionally, six participants (24%) are moderately aware of blockchain.

Participants’ familiarity with Blockchain

Similar to the participants’ familiarity with the IoT, the selected participants were a good fit for this question, as the majority are aware of blockchain. Therefore, participants were asked, “if they believe that there will be an expansion of blockchain with IoT in the future” All 25 participants responded, with their responses shown in Fig.  4 . It was found that the majority (eight, 32%) strongly agreed with this point. Additionally, six participants (24%) expressed a moderately high level of agreement. In total, 11 participants (44%) either moderately or strongly disagreed.

Participants’ thoughts about the IoT’s integration with blockchain

Following this, participants were asked, “What are your thoughts regarding the need to have an IoT blockchain simulator for helping developers adjust the system’s configurations?” All 25 participants provided their responses, summarized in Fig.  5 . As shown in the figure, nine participants (36%) strongly agreed with this idea, while eight participants (32%) agreed with it. In total, 10 participants (32%) were either neutral or completely disagreed with the concept

Participants’ thoughts about having an integrated IoT blockchain simulator

Given that the participants are domain experts, We took the opportunity to ask participants for their perspectives on storing IoT data in the blockchain, asking, “Do you agree that all IoT data should be stored in the blockchain?” The participants’ responses are shown in Fig.  6 . It is clear that the majority disagreed with this statement (13 participants either disagreed or strongly disagreed). This may be due to the various scenarios of using IoT with blockchain. On the other hand, the least number of participants agreed with this statement (eight participants either agreed or strongly agreed), while two participants were neutral.

Participants’ thoughts about storing all of the IoT data in the blockchain

Consensus algorithms are critical in blockchain because they are used to reach a common agreement (consensus) on the current state of ledger data and enable unknown peers to be trusted in a distributed computing environment. Therefore, we sought to understand participants’ needs related to this. Accordingly, participants were asked, “What are your thoughts on having multiple consensus algorithms in the simulator?” The participants’ responses to this question are summarized in Fig.  7 . Examining the data more closely, it is clear that the majority (eight, 32%) agreed with this idea, while five participants (20%) strongly agreed. In total, 11 participants (38%) either moderately or strongly disagreed.

Participants’ thoughts about having multiple consensus algorithms in the simulator

Considering blockchain in greater depth, it is essential to determine the participants’ perspectives regarding investigating the log. This is crucial because it provides the opportunity to compute system latency and throughput. Accordingly, the participants were asked for their opinions concerning investigating the log file. The participants’ responses to this question are presented in Fig.  8 . The significant point is that the majority (12 participants) either strongly agreed or agreed with this idea. Additionally, five participants expressed neutrality concerning the statement. Meanwhile, eight participants in total expressed either moderate or complete disagreement.

Participants’ thoughts about the ability to investigate the log

Subsequently, the participants were asked about using IoT devices as blockchain nodes. The participants’ responses to this question are presented in Fig.  9 , which presents their overall positive perspectives regarding this statement. Ultimately, most participants either strongly agreed (seven participants, 28%) or agreed (five, 20%) with the statement. In contrast, a total of nine participants (36%) either strongly disagreed or disagreed with this notion. Lastly, six participants (24%) expressed neutrality regarding this notion.

Participants’ thoughts about using IoT edge devices as blockchain nodes

Finally, given that there are numerous types of blockchain, there is a need to comprehend if it is essential to have a simulator that can model the diverse types. Accordingly, the participants were asked about this, with their responses to this question presented in Fig.  10 . According to the participants’ perspectives, the majority (nine participants, 36%) are neutral towards this. Alternatively, four participants (16%) agreed, while two participants (8%) strongly agreed. Finally, ten participants (40%) either strongly disagreed or disagreed with this notion.

Participants’ thoughts about modelling different blockchain types in the simulator

Overall, the results of a questionnaire were given to 25 participants to gauge their familiarity with the Internet of Things (IoT) and blockchain, and their opinions on various topics related to the potential expansion of blockchain with IoT in the future. The results show that the majority of participants are at least moderately familiar with both IoT and blockchain. When asked about the expansion of blockchain with IoT, the majority of participants agreed, with 44% either moderately or highly agreeing. The participants were also asked about the need for an IoT blockchain simulator to help developers adjust system configurations, with 36% strongly agreeing and 32% agreeing. When asked about storing all IoT data in the blockchain, the majority of participants disagreed, while a smaller number agreed. Additionally, the participants were asked about the inclusion of multiple consensus algorithms in a simulator, with the majority expressing agreement. Overall, the results of the questionnaire suggest that the participants are knowledgeable about both IoT and blockchain and see value in the potential expansion of the two technologies. For the picture to be complete, Table 2 matches the questionnaire questions to the predefined objectives.

Interviews were conducted with a set of participants to collect information concerning their opinions on using IoT-based Blockchain, as well as comprehending their requirements for the simulation software for assessing blockchain-based IoT. The participants’ responses were assessed from active, analytical, and critical perspectives, with their suggestions being clarified. Three questions were posed:

What are the major challenges you face when dealing with blockchain-based IoT for any evaluation purposes?

Which features make blockchain suitable for the IoT?

What are the anticipated outcomes of utilising blockchain within the IoT?

P1 stated that “There are many challenges based on the current proposed model. The obstacle lies in investigating the performance and cost of these technologies. Also, there are many proposed simulators for Blockchain and IoT in the literature; however, each simulator either focuses on IoT or blockchain. As a researcher, I prefer having a multi-discipline simulator that can simulate IoT devices in sensing and sending data to the edge/fog layer then to cloud, while using blockchain in different layers” . Regarding the second question, he remarked, “The majority of IoT applications such as healthcare data is of high importance and needs to be securely handled. I believe blockchain is a strong fit for this scenario because of its features (for example, decentralisation) that dispense a third party to manage data” . Lastly, for the third question, he suggested that “With the rapid development of IoT technology and the large number of devices expected to be connected, I believe blockchain would alleviate security issues. For example, identity management and access to the IoT should be more secure and trusted, using a reliable tool for controlling data access” .

P2 stated concerning the first question, “The main challenge I faced with the IoT and blockchain technologies is the difficulty of monitoring systems’ performance. “The challenge is that it does not cover all of my required features. I often use a cloud simulator to evaluate the system. Having a Blockchain simulator with IoT features that can track every transaction and system throughput will ease my tasks. This could become an efficient simulator, utilising both blockchain and IoT power” . Concerning the second question, the participant explained that “not all the IoT data are of high importance, but there is still a need to secure the sensitive data and enhance privacy” . Finally, he stated that “I believe blockchain can mitigate several of the IoT issues related to privacy. Also, blockchain can define a set of policies needed to control IoT data access” .

In reply to the first question, P3 mentioned that “One of the most important blockchain-based IoT challenges is system evaluation because of the heterogeneity and mobility of IoT devices. Personally, I prefer to assess the system from different viewpoints, ranging from general performance (computational time, transaction latency and throughput) to security, but there is no simulator permitting this” . Responding to the second question, he said that “Data storage is a crucial metric to determine the applicability of blockchain with the IoT. The IoT devices sense the environment and send data in real time. This implies that we have plenty of data per second. Accordingly, blockchain cannot be used as data storage for all data. Hence, I prefer storing only the most important data; I think that this can be reliable” . Concerning the third question, he asserted that “Every single device can be identified using a permissioned blockchain network that is used by all parties involved. This implies that data is generated by an identified device (trusted), in the sense that the generated data has a unique identification number, hence ensuring immutability. In this scenario, it could be appropriate to track the data in the supply chain context” .

P4 noted concerning the first question that “The challenge is how to obtain various statistics about the system, like the number of generated transactions, number of blocks and time of confirmation, both for the block and transaction. These metrics give me an indicator about the proposed system, which is essentially the same as the real world and enables me to make decisions” . Regarding question two, the participant stated that “IoT data can be immutable and distributed over time in the blockchain network. Participants in the blockchain network can ensure the data’s authenticity and that it will never be tampered with” . Finally, concerning question three, he remarked that “I advise using blockchain to keep sensitive IoT data where security is ensured. Also, as IoT devices are the data source, there is a need for reliable analysis which will not be carried out if there are no device management criteria. I believe this can be carried out by blockchain, for example, using smart contracts” .

P5 commented that “Assessing the system performance is very important. In the context of blockchain and the IoT, it is difficult to measure performance without simulation due to the complexity of both technologies. So, from my point of view, the simulator enables me to test the system from diverse aspects. Specifically, I can configure the number of IoT devices and protocols used, while at the same time determining the size of transactions, either for blockchain or the IoT (end to end)” . Regarding the second question, he suggested that “One of the advantages of blockchain is decentralisation, as it can prevent a single point of failure and bottlenecks from occurring. I see that blockchain benefits the IoT by ensuring reliable data transfer” . Finally, he stated “I believe that blockchain would provide the IoT developers with more secure solutions due to its features” .

Concerning the first question, P6 remarked that “The challenge lies in determining if the simulator supports more than one measure, such as latency, throughput, total time, along with the number of blocks created to analyse the overall performance of blockchain and the Internet of Things. Based on my experience, it is difficult to cover all aspects of the IoT and blockchain simultaneously, but the simulator can cover the general aspects of both technologies in different scenarios” . Concerning the second question, he stated that by and large “Due to the limited processing capabilities of IoT devices, third-party service providers are generally used to process additional data. By entrusting sensitive user data to third-party service providers, users must trust data protection and privacy. This trust coincides with the danger of breaking data privacy and policies. Blockchain’s traceability can help in these situations” . Finally, he expressed that “Blockchain is a promising choice when it comes to ensuring privacy and applying security” .

The findings of this summary are based on interviews conducted with participants to gather their opinions on using IoT-based blockchain and their requirements for simulation software for evaluating blockchain-based IoT. The participants identified several challenges in using blockchain-based IoT for evaluation purposes, including difficulties in monitoring and evaluating system performance, a lack of simulators with all required features, and the heterogeneity and mobility of IoT devices. They also emphasized the importance of data storage in determining the suitability of blockchain for the IoT, and the need to store only the most important data due to the large amount of data generated by IoT devices. One participant mentioned that blockchain has the potential to enhance security and privacy in the IoT, particularly through the use of permissioned blockchain networks to identify devices and ensure the immutability of data. Another participant highlighted the potential for blockchain to improve supply chain management by tracking data in the supply chain context.

In general, participants are seeking a multi-discipline simulator that can simulate both the IoT and blockchain aspects of a system and provide various metrics and statistics about system performance, security, and data storage. They also stressed the importance of securely handling sensitive data and enhancing privacy through the use of blockchain in the IoT.

Recommendations

The results presented in the previous sections have evidenced a broad belief that blockchain can benefit IoT applications and enhance its applicability by alleviating its limitations. Moreover, the majority of participants in our studies agreed that it is necessary to have an integrated blockchain IoT simulator to aid in the development and evaluation of applications that integrate blockchain and IoT technologies. On this basis, we recommend greater research and exploration of the design and development of an integrated blockchain IoT simulator. Considering the lack of such a simulator in the literature, this calls for greater research and the need to attract the attention of contemporary researchers.

Motivating blockchain-based IoT scenario

The need for a Blockchain-based IoT simulator is motivated by the difficulty of assessing the viability and performance of real deployment. To appreciate this difficulty, assume a blockchain-based IoT ecosystem scenario, presented by [ 34 ], where a firefighting station considers outsourcing the IoT infrastructure’s deployment and operation to a specialised IoT service provider called IoTSP. For the sake of simplicity, the IoTSP is responsible for promptly reporting fire alerts to the firefighting station. Trust issues may emerge such that fire may occur without being noticed either because the IoTSP fails to report the fire incident or the firefighting station’s system fails is unavailable. To resolve potential disputes, there is in place a service level agreement (SLA) that requires the IoTSP to emit fire alerts via a shared blockchain ledger (see Fig.  11 ). However, the examination of the overall viability and performance using real deployment settings may encounter some or all of the following hurdles, which include, but are not limited to,

complexity of real IoT deployment merely for experiments : The complexity of real IoT deployment can be a major challenge when it comes to testing and evaluating the viability and performance of a blockchain-based IoT system. Setting up a real IoT deployment can be a time-consuming and resource-intensive process, as it requires the deployment of physical hardware and infrastructure, as well as the integration of various components and technologies. This complexity can make it difficult to conduct experiments and tests in a real deployment setting, particularly if the deployment is large or complex.

Lack of resources : The lack of resources can be a significant challenge when it comes to testing and evaluating the viability and performance of a blockchain-based IoT system. Conducting experiments and tests in a real deployment setting can be resource-intensive, as it requires the deployment of physical hardware and infrastructure, as well as the integration of various components and technologies. This can be particularly challenging for organizations that do not have access to the necessary resources, such as funding, personnel, or technical expertise.

Lack of technical workforce : The lack of a technical workforce can be a major challenge when it comes to testing and evaluating the viability and performance of a blockchain-based IoT system. Setting up a real IoT deployment can be a complex and technical process that requires specialized skills and expertise. This can be particularly challenging for organizations that do not have access to a sufficient number of technical personnel or do not have the necessary expertise in-house.

Cost inefficiency : Conducting experiments and tests in a real deployment setting can be costly and inefficient, particularly if the deployment is large or complex. Setting up a real IoT deployment requires the deployment of physical hardware and infrastructure, as well as the integration of various components and technologies. This can be a time-consuming and resource-intensive process that may not be cost-effective for organizations, particularly if the deployment is only being used for testing and evaluation purposes.

Alternatively, simulation tools can help to overcome the complexity of real IoT deployment by providing a virtual environment for testing and experimentation, allowing for the creation of virtual IoT deployments without the need for physical hardware or infrastructure. They can also help to overcome the lack of resources and technical workforce by providing a more cost-effective and resource-efficient way to conduct experiments and tests and can help organizations assess the viability and performance of a blockchain-based IoT system even if they do not have access to the necessary resources or expertise. Finally, simulation tools can help to reduce the cost inefficiency associated with real deployment by providing a more cost-effective and efficient way to conduct experiments and tests, and by helping organizations to identify potential issues or bottlenecks before deployment.

Motivating Blockchain-based IoT Scenario: A firefighting station and IoT service provider (IoTSP) engage in an SLA where the conformance of the IoTSP is measured based on monitoring logs stored on a shared blockchain ledger

Conceptual architecture

This section illustrates the conceptual architecture for the proposed Blockchain-based IoT simulator. As Fig.  12 depicts, the architecture is divided into three main components, namely, \(\varvec{Configurator}\) 8.1 , \(\varvec{Generator}\) 8.2 , \(\varvec{Simulation\ core}\)    8.3 , and \(\varvec{Reporter}\) 8.4 .

An overview of the Conceptual Model for Simulating Blockchain-based IoT Ecosystems

Configurator

The configurator component in the proposed conceptual model is responsible for setting various parameters for the IoT infrastructure and the blockchain network. This component allows you to specify the required workload for the IoT side using IoTsimOsmosis [ 27 ], which is an extension of CloudSim [ 23 ] that enables you to define various properties related to an IoT architecture, such as sensors, actuators, devices, edge units, networks topology, data centres, computing resources, tasks scheduling, and allocation policies.

On the blockchain side, the configurator enables you to specify an enterprise blockchain network, which should include essential elements such as the number of participating nodes (e.g. miners based on the IoT topology), block settings (e.g. size and difficulty), transaction settings (e.g. size, transaction delay, etc.), consensus algorithm (e.g. proof of work, Raft, etc.), and simulation setups (e.g. the number of running simulators).

The configurator component is an important part of the simulation process, for customising the parameters of the IoT infrastructure and blockchain network to meet the specific needs and requirements. By setting these parameters, you can better understand how different configurations might impact the performance and viability of the system.

Based on the specified configurations as we discussed in the Section Configurator  8.1 , the generator component in the proposed conceptual model is responsible for creating the required infrastructure for the IoT application and the blockchain network based on the specified configurations. The generator component uses the parameters set by the configurator component to create the necessary components and connections for the IoT topology and the blockchain network.

For example, the generator component may create the necessary sensor nodes and edge units for the IoT topology, as well as the protocols for transmitting and receiving data. It may also create the participating nodes for the blockchain network, such as miners or validators, and configure the block settings, transaction settings, and consensus algorithm.

The generator component is an important part of the simulation process, for creating a realistic and functional model of the IoT application and blockchain network based on the specified configurations. This can be useful in testing and evaluating the performance and viability of the system under different scenarios and conditions.

Simulation core

The simulation Core in the proposed conceptual model typically consists of several main components that work together to simulate the operation of the system. These components include:

The transaction factory and workload feeder are components in a simulation environment for a blockchain-based IoT system. The transaction factory is responsible for generating transactions based on the data collected from the workload feeder, while the workload feeder manages the flow of transactions and ensures that they are processed efficiently and accurately. The transaction factory follows a specific process to create and broadcast transactions, including:

Construct a Transactions Structure : The transaction factory prepares the format of the transactions to match the structure required by the blockchain network. This includes defining the data structure and required fields for the transactions, as well as any other requirements or constraints.

Broadcast transactions to miner nodes : Once construct a transaction structure, then broadcasts the prepared transactions to all nodes in the network in order to inform them of the new transactions.

Appending the transactions to the transaction pool a collection of pending transactions that are waiting to be added to the blockchain.

The process of generating and managing transactions is typically repeated until no more transactions are being fed into the system by the workload feeder. Overall, the transaction factory and workload feeder play important roles in the simulation process by generating and managing the flow of transactions within the system, and by helping to test and evaluate the performance and viability of the blockchain-based IoT system. In a blockchain network, miner nodes are responsible for creating blocks of transactions and adding them to the blockchain. When a miner receives transactions in its transaction pool, it will typically try to create a block by selecting a subset of the transactions from the pool and adding them to a new block. The process of creating a block is often referred to as an “event,” as it represents a significant event in the operation of the blockchain network. In order to create a block, a miner must typically perform a consensus algorithm such as a proof of work, which involves using cryptographic algorithms to demonstrate the work that has been done to validate and include the transactions in the block. In a simulation environment, the aim may be to simulate the process of creating blocks and adding them to the blockchain in order to test and evaluate the performance of the network and the miner nodes. In the conceptual mode, we create a Block Factory component.

The block factory is a component in a simulation environment for a blockchain-based IoT system. It is responsible for simulating the process of creating blocks and adding them to the blockchain. The block factory follows a specific process to create and execute transactions, including:

Invoking and Executing Transactions : The miner selects a subset of pending transactions from the transaction pool based on certain criteria, such as the time the transactions were created, the gas price associated with them, or the order in which they were received.

Append Transactions to Next Block : When a miner node receives transactions in its transaction pool, it will typically try to create a block by selecting a subset of the transactions from the pool and adding them to a new block. This process is known as “appending” the transactions to the block, as it involves adding the transactions to the block and preparing them for inclusion in the blockchain.

Constructing block and append it to the local blockchain : After the block has been created with its set of transactions.

Append Block to local Blockchain : Once the block has been constructed, it is ready to be appended to the local copy of the blockchain. This involves adding the block to the end of the local copy of the blockchain and updating the local copy to reflect the new block.

Broadcast the block to other nodes : The miner broadcasts the newly added block to all other nodes in the network in order to inform them of the new block and update their copies of the blockchain.

Overall, the block factory plays a crucial role in the simulation process by helping to simulate the operation of the blockchain network and by providing valuable insights into its performance and viability. Once a block has been broadcasted to the blockchain network, it becomes the responsibility of the block receivers to validate the block and decide whether to accept it and add it to their copy of the blockchain. The process of validating a block involves verifying that the block meets all of the requirements and standards of the blockchain network. This may include checking the block header to ensure that it includes a valid reference to the previous block in the blockchain, and verifying the transactions contained in the block to ensure that they are valid and properly formatted. In the conceptual mode, we create the Received Blocks component.

Received Blocks : a component in a simulation environment for a blockchain-based IoT system. It is responsible for receiving blocks that have been broadcasted to the network and deciding whether to accept them and add them to the local copy of the blockchain. The received blocks component typically follows a specific process when receiving a new block, which may include the following steps:

Check Validity of Received Block when receiving a new block, one of the key tasks of the Received Blocks component is to check the validity of the received block. This involves performing a series of validation checks on the block to ensure that it meets all of the requirements and standards of the blockchain network.

Updating and Append it to the Local Blockchain if the received block is deemed to be valid, the next step in the process is to update the local copy of the blockchain and append the received block to it. This involves adding the received block to the end of the local copy of the blockchain and updating the local copy to reflect the new block.

Updating the transaction pool Once the new block has been added to the local blockchain, the node will update the transaction pool by removing the transactions that were included in the block. This leaves the transaction pool with only the transactions that have not yet been included in a block, allowing the node to continue the process of verifying and adding new transactions to the blockchain.

The benchmark report is an important part of the simulation process, as it provides detailed information about the performance and viability of a blockchain-based IoT system. In our proposed conceptual model, once the simulation is finished, the simulator will prepare the benchmark report as an Excel file, which consists of several sheets each of which provides specific information about different aspects of the system as shown below

Configuration : This provides important information about the parameters used to conduct the experiment, such as the type and number of nodes, the blockchain protocol used, and any other relevant system parameters. This information is important for understanding the context in which the simulation was conducted, and can help to identify any factors that may have influenced the performance of the system.

Overall result : A benchmark report provides a summary of the overall performance of a blockchain-based IoT system. This includes a range of statistics that can be useful for understanding the system’s performance and identifying any issues or opportunities for improvement. Some examples of the types of statistics that might be included in the “Overall result” section include:

Total number of blocks: This is the total number of blocks that were added to the blockchain during the simulation.

Total number of blocks including transactions: This is the total number of blocks that contained at least one transaction.

Total number of blocks without transactions: This is the total number of blocks that did not contain any transactions.

Average block size: This is the average size of the blocks in the blockchain.

Total number of transactions: This is the total number of transactions that were processed during the simulation.

Average number of transactions per block: This is the average number of transactions included in each block.

Average transaction inclusion time: This is the average time it took for a transaction to be included in a block.

Average transaction size: This is the average size of the transactions processed during the simulation.

Total number of pending transactions: This is the total number of transactions that were waiting to be processed at the end of the simulation. Average block propagation time: This is the average time it took for a block to be propagated (i.e., disseminated) to all nodes in the network.

Average transaction latency: This is the average time it took for a transaction to be processed and added to the blockchain.

Transaction execution: This is the percentage of transactions that were successfully processed during the simulation.

Transaction throughput: This is the number of transactions that were processed per second.

Blocks overview : A benchmark report provides details about the individual blocks that were added to the blockchain during the simulation. This includes information such as the block ID, previous block ID, block depth, block timestamp, block size, number of transactions, and the minter (the node responsible for creating the block). This information can be useful for understanding the overall performance of the system at the block level.

Transactions latency overview : A benchmark report provides details about the latency of individual transactions in a blockchain-based IoT system. Latency refers to the time it takes for a transaction to be processed and added to the blockchain, and it can have a significant impact on the overall performance of the system. Included in this section are details about the transaction latency of each transaction, including the transaction ID, creation time, confirmation time, and transaction latency. This information can be useful for understanding the overall performance of the system at the transaction level.

Pending Transactions overview : A benchmark report provides details about transactions that were not executed during the simulation. These transactions may not have been executed for a variety of reasons, such as being delayed due to insufficient resources or other issues.

Statistic : A benchmark report provides statistical information about the performance of a blockchain-based IoT system. Specifically, it provides details about the distribution of block time and block latency, including the minimum, maximum, mean, and standard deviation of these metrics.

During the evaluation process, we presented the conceptual model of the blockchain simulator to a group of experts and invited them to discuss and provide feedback on the model. We clarified any unclear areas and used a questionnaire to gather structured feedback from the participants based on their knowledge and experience. The questionnaire-based approach allowed us to gather detailed and structured feedback on the model and its various components, and we used this information to test the validity of the conceptual model. We had defined objectives for the evaluation process and will be presenting our findings in the following sections.

Our planned initiative is intended for experts in the fields of the Internet of Things (IoT) and blockchain technology. We conducted a study with 10 participants, all of whom were doctoral candidates with a focus on research related to blockchain, smart cities, and other IoT topics. The participants’ research interests included cloud computing, edge computing, smart contract-based service level agreements in the IoT, and blockchain-based IoT. The scientific interests of each participant are summarized in the Table  3 .

The evaluation of the conceptual model was conducted using a focus group and a questionnaire. Focus groups are a useful technique for gathering detailed and in-depth feedback from a group of individuals. During the focus groups, we discussed the conceptual model with the participants and invited them to provide their thoughts and opinions. The participants then completed a questionnaire that included both closed-ended and open-ended questions to provide more detailed feedback on the model. The use of both focus groups and a questionnaire allowed us to gather a wide range of perspectives and insights on the model.

The focus group began with a presentation on the challenges of implementing Blockchain and IoT. We also mentioned the limitations of current simulators. Next, the participants were asked to read and analyze the framework of the IoTOsmosis simulator [ 27 ]. To further clarify the concept, we provided a use case example. Finally, we introduced the conceptual model and asked the participants to complete a questionnaire consisting of four closed-end questions and two open-end questions related to the conceptual model.

To what extent are you satisfied with the conceptual model?

To what extent are you satisfied with the conceptual model’s generality?

Assuming that IoTOsmosis is the base IoT simulator in the conceptual model, to what extent do you agree that it covers your requirements?

ease of use

configurability

extensibility

maintainability

network topology

To what extent does the blockchain part cover your requirements?

Focus Group

What are your overall thoughts on the conceptual model for the blockchain simulator?

Do you believe the conceptual model for the blockchain simulator is comprehensive and well-designed, or are there any areas that you feel need further improvement or refinement?

Experimental results

To validate the proposed conceptual model, we distributed a questionnaire to 10 participants. The questionnaire began by asking about their satisfaction with the conceptual model and its applicability. Specifically, we asked the participants, “To what extent are you satisfied with the conceptual model?” The results, shown in Fig.  13 , indicate that the majority of participants were satisfied, with 60% expressing satisfaction and 30% expressing complete satisfaction. 10% were neutral about the model.

Participant satisfaction with the conceptual model

Next, the participants were asked about their thoughts on the applicability of the conceptual model. The results of this question are shown in Fig.  14 . It’s worth noting that there is complete agreement about the model, with 40% of participants indicating that they were completely satisfied and 60% indicating that they were satisfied. Based on these results, we can confirm that the proposed conceptual model is a good fit.

Participant satisfaction with the conceptual model’s generality

Four questions were asked to assess the usability, configurability, maintainability, and available network topology of the IoTOsmosis simulator, which is at the core of the proposed conceptual model. Specifically, the participants were asked about their level of agreement with the ease of use of the simulator (“To what extent do you agree with its ease of use?”). The results, shown in Fig. 15 , indicate that 30% completely confirm the simulator’s usability, while 40% agree with its ease of use. 20% were neutral about the simulator, and 10% disagree with its ease of usability.

Participant agreement with the ease of use of the IoTOsmosis simulator

Additionally, the participants were asked about the configurability of the IoTOsmosis simulator (“To what extent do you agree with its configurability?”). The results, shown in Fig. 16 , show that 60% of participants (10% completely agree and 50% agree) are satisfied with the simulator’s configurability. 20% had neutral or disagreeing opinions. The participants were also asked about the maintainability of the simulator (“To what extent do you agree with its maintainability?”). The results, shown in Fig. 17 , indicate a clear agreement with 70% of participants (20% strongly agree and 50% agree) indicating satisfaction. 20% disagreed with the ease of maintainability, while 10% were neutral. A final question about the IoTOsmosis simulator was “To what extent do you agree with the network topology?” The results are shown in Fig. 18 .

Participant agreement with the configurability of the IoTOsmosis simulator

Participant agreement with the maintainability of the IoTOsmosis simulator

Participant agreement with the effectiveness of the blockchain part in meeting their requirements

Finally, the questionnaire concluded by asking the participants about their thoughts on the ability of blockchain to meet their requirements. The results are shown in Fig. 19 . A closer look at the figure reveals a high level of agreement (30% strongly agree and 50% agree) in the usefulness of blockchain. There was an equal number of neutral (10%) and disagreeing (10%) opinions.

Overall, a questionnaire was distributed to 10 participants to validate the proposed conceptual model for a blockchain-based Internet of Things (IoT) simulator. The questionnaire asked participants about their satisfaction with the conceptual model and its applicability, as well as their thoughts on the usability, configurability, maintainability, and network topology of the IoTOsmosis simulator, which is at the core of the proposed model. The participants were also asked about the effectiveness of the blockchain part in meeting their requirements. The results of the questionnaire showed that the majority of participants were satisfied with the conceptual model and its applicability, with 40% expressing complete satisfaction and 60% expressing satisfaction. The results also indicated that the IoTOsmosis simulator was generally well-received, with most participants expressing satisfaction with its usability, configurability, maintainability, and network topology. Additionally, a high level of agreement was observed regarding the usefulness of blockchain in meeting participants’ requirements, with 30% strongly agreeing and 50% agreeing. Based on these results, it can be concluded that the proposed conceptual model is a good fit. To provide a complete overview, Table 4 shows how the questionnaire questions align with the predefined objectives.

Focus group

P1 stated that “I believe that the conceptual model is well-designed and comprehensive, and it appears to include a set of key features and capabilities necessary for effectively simulating and evaluating blockchain-based IoT systems. I think that these capabilities will be crucial for understanding and optimizing the performance of blockchain-based IoT systems and for identifying any potential issues or challenges that may arise during deployment” .

P2 stated that “Overall, I think the conceptual model is solid and well thought-out. It covers all of the key components and functions that I would expect to see in a simulator of this kind, and it seems like it would be relatively straightforward to implement based on the design that has been presented. There are a few areas where I think the model could be improved. For example, it might be helpful to have more granular control over the various parameters and settings of the simulator, such as the ability to specify the type of consensus algorithm or the block size” .

P3 stated that “I believe the conceptual model is a promising concept. The wide range of features and capabilities included in the conceptual model is impressive. Also, the simulation core seems to be well-designed, with components such as the transaction factory and workload feeder, the consensus component, and the monitoring and evaluation components. However, it might be helpful to have more options for customizing the simulation, such as the ability to specify different types of transactions or to customize the workload feeder in greater detail” .

P4 stated that “As a beginner in the fields of blockchain and IoT, I found the conceptual model of the simulator to be easy to understand and well-structured. It effectively explains the various components and their functions and provides a helpful overview of how they work together. While the concept model is clear, it would be beneficial to include more information about the specific consensus algorithms that will be implemented in the implementation phase. This added detail would help to further clarify the inner workings of the simulator” .

P5 stated that “ The conceptual model of the simulator appears to be very promising, as it includes a number of metrics that allow users to get a sense of how it will perform in the real world. However, it might be useful to make the model more flexible by allowing it to be deployed in different layers rather than just the edge layer. This would give users more control over where and how they deploy the blockchain network and make it easier to adapt to different use cases and environments” .

P6 stated that “The conceptual model of the simulator is well-designed. It provides a means to evaluate the performance of different blockchain and IoT solutions and helps users to identify potential challenges and issues. The inclusion of a monitoring component is a particularly useful feature that enables users to track the performance of the system over time and identify any changes” .

P7 stated that “In my opinion, the architecture of the simulator is well-designed and comprehensive. It includes most of the necessary features for evaluating blockchain-based IoT systems. However, there may be potential for further improvement in terms of its ability to simulate enterprise blockchain environments and its integration with other simulators beyond IoTSim-Osmosis. Enhancing these capabilities would increase the versatility and usefulness of the simulator for a wider range of applications” .

P8 stated that “I think the conceptual model is good because it includes a variety of features. One aspect of the model that I really appreciate is the generator component, which makes the model integrated with different simulators” .

P9 stated that “I think the model is a strong foundation for further research and development. It covers a wide range of important features and capabilities, and it seems like it would be useful for a variety of different applications. I do think there are a few areas where the model could be improved or expanded upon. For example, simulation capabilities to evaluate the performance of the simulator under different conditions. Additionally, it would be helpful to have more options for generating and analyzing results, such as the ability to compare different simulation scenarios or to run simulations over longer periods of time” .

P10 stated that “I think the proposed conceptual model is good, and the inclusion of the configurator component is a great idea, as it allows users to customize the parameters of the simulation to meet their specific needs and requirements. However, I think it would be helpful to have more options for configuring the blockchain network, such as the ability to specify different types of consensus algorithms or to customize the block settings in greater detail” .

Through analysing the participants’ responses as shown in Table 5 , the result of the evaluation of the conceptual model showed that it is generally well-regarded. The reason underpinning this attitude is the inclusion of a wide range of key features and capabilities that make it a suitable foundation for creating a simulation environment for blockchain-based IoT applications. However, there are also a few areas where the model could be improved or expanded upon. Some participants have suggested adding more granular control over the various parameters and settings of the simulator. Others have suggested including more information about the specific consensus algorithms that will be implemented in the implementation phase and making the model more flexible by allowing it to be deployed in different layers. To provide a complete overview, Table 6 shows how the questionnaire questions align with the predefined objectives.

Conclusion and future work

IoT systems are becoming increasingly common, but their centralization introduces limitations. However, it is expected that blockchain technology could potentially overcome these limitations and unlock new opportunities for IoT. A major challenge is that there is currently no reliable simulator for evaluating the use of blockchain as a solution for IoT problems. This drives our current efforts to research and design a simulator for this purpose. To gain a deeper understanding of this notion, we conducted two studies, which included a questionnaire and interviews with experts. The questionnaire results showed a high level of familiarity with both IoT and blockchain, as well as a strong belief that blockchain could address various challenges faced by IoT. This belief was further supported by the expert interviews. Through these studies, we discovered that a major challenge is the lack of a simulator environment that can accurately simulate blockchain-based IoT applications. Motivated by this, we have developed a conceptual model as a foundation for creating a simulation environment for blockchain-based IoT applications. To ensure the effectiveness of the conceptual model, we employed two research methods which included a questionnaire and a focus group with experts. The evaluation of the conceptual model revealed that it is generally well-received due to its comprehensive range of key features and capabilities that make it an ideal foundation for building a simulation environment for blockchain-based IoT applications. Our future work aims to create and validate a simulation environment for blockchain-based IoT applications, allowing for the testing and validation of blockchain-based IoT systems before they are deployed in the real world.

Availability of data and materials

All data created during this research are available at GitHub\footnote{\url{ https://github.com/AlbshriAdel/conceptual-architecture-data }}.

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Acknowledgements

We thank the Program Committee of IEEE SmartIoT for inviting us to extend our previous study [ 28 ] and submit it to this Journal.

This work is funded in part by the EPSRC, under grant number EP/V042017/1. Scalable Circular Supply Chains for the Built Environment.

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Adel Albshri

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Ali Alzubaidi

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Maher Alharby

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Bakri Awaji

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Designing and collecting data for investigating the requirements for building a blockchain simulator for IoT Applications was carried out by A. Albshri and B. Awaji; A. Albshri analyzed the results. The methodology was developed by A. Albshri and A. Alzbuaidi; The conceptual architecture was developed by A. Albshri and A. Alzbuaidi; A. Albshri and A. Alzbuaidi designed and executed the data collection and evaluation; A. Albshri analyzed the results. The manuscript was drafted by A. Albshri; then, reviewed and edited by A. Albshri, E. Solaiman, A. Alzbuaidi, M. Alharby, K. Mitra and B. Awaji; E. Solaiman is the principal investigator (lead supervisor) of the project. All authors have reviewed and approved the manuscript.

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Albshri, A., Alzubaidi, A., Alharby, M. et al. A conceptual architecture for simulating blockchain-based IoT ecosystems. J Cloud Comp 12 , 103 (2023). https://doi.org/10.1186/s13677-023-00481-z

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Integrating Digital Twins with IoT-Based Blockchain: Concept, Architecture, Challenges, and Future Scope

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In recent years, there have been concentrations on the Digital Twin from researchers and companies due to its advancement in IT, communication systems, Cloud Computing, Internet-of-Things (IoT), and Blockchain. The main concept of the DT is to provide a comprehensive tangible, and operational explanation of any element, asset, or system. However, it is an extremely dynamic taxonomy developing in complication during the life cycle that produces an enormous quantity of the engendered data and information from them. Likewise, with the development of the Blockchain, the digital twins have the potential to redefine and could be a key strategy to support the IoT-based digital twin’s applications for transferring data and value onto the Internet with full transparency besides promising accessibility, trusted traceability, and immutability of transactions. Therefore, the integration of digital twins with the IoT and blockchain technologies has the potential to revolutionize various industries by providing enhanced security, transparency, and data integrity. Thus, this work presents a survey on the innovative theme of digital twins with the integration of Blockchain for various applications. Also, provides challenges and future research directions on this subject. In addition, in this paper, we propose a concept and architecture for integrating digital twins with IoT-based blockchain archives, which allows for real-time monitoring and control of physical assets and processes in a secure and decentralized manner. We also discuss the challenges and limitations of this integration, including issues related to data privacy, scalability, and interoperability. Finally, we provide insights into the future scope of this technology and discuss potential research directions for further improving the integration of digital twins with IoT-based blockchain archives. Overall, this paper provides a comprehensive overview of the potential benefits and challenges of integrating digital twins with IoT-based blockchain and lays the foundation for future research in this area.

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1 Introduction

Recently, the evolution in information technologies such as the Internet of Things (IoT), Cloud Computing (CC), and Cyber-Physical System (CPS) besides development in communication technologies, are revolutionizing the system approach to transmitting information between various sources. Undoubtedly, the revolution in all aspects of life nowadays is due to digitalization. Because of this advance and rebellion in Information-communication technology (ICT), the Digital Twins (DT) has emerged as one of the talented ideas [ 1 , 2 , 3 , 4 , 5 , 6 , 7 , 8 ]. The DT is a digital representation of a real system from the perspective of (CPS). Therefore, a virtual complement of a convinced system mimics its actual performance. During full lifecycle development, the digital data from the virtual system, coupled with a real system, characterizes the information from the physical system. Consequently, combining digital and its real counterparts creates an effective way to handle, control, and improve coordination when the system is operating [ 4 ]. Furthermore, the DT records all data collected from physical sensors as well as the execution system response. As a result, the critical role of DT is to predict and diagnose the behavior of the physical system in order to foresee any malfunction or fault and feed data to the system in order to provide the best possible maintenance [ 6 ].

The virtual representation of the system is the major goal of DT to enhance productivity, increase the operation's flexibility, and decrease maintenance prices and efforts. On the Other Hand, DT could be run depending on the requirement, either on a cloud-hosted system or at the edge layer, to address the issues of Industry 4.0, which arise from the production of linked components. DT, on the other hand, supports both a clarification of the behavior of the actual system and the best possible solutions for the physical model. To improve the control action, foresee the system performance, and endorse decision-making, DT uses a fundamental modeling system, simulation procedures, and transparent simulation [ 9 , 10 , 11 , 12 ].

The digital twins' concept was first introduced by Grieves in 2002 [ 13 ], and then NASA was the first to apply it to constructing virtual space machine models. Existing DT works and implementations are still in their early stages, according to the literature, and require a lot of effort. However, It is well-integrated into a wide range of applications, such as biomedical systems, manufacturing, aerospace, agriculture, Smart Cities, and weather forecasting [ 1 , 2 ]. In addition, many distinct specialist engineers and computer scientists are needed in this essential subject to design an efficient Digital twin system for any physical system. Their responsibilities will include building and designing the essential product prototype as well as creating a detailed explanation of the virtual system.

With the advance of modern technologies currently, the digital twin is integrating with other technologies like cloud computing, Artificial Intelligence (AI), Big data, blockchain, and IoT, and sensor data fusion to develop a dataset that will be reorganized and modified when its actual counterparts alter [ 7 , 10 , 14 , 15 , 16 ]. In recent years, IoT integration with DT has allowed for greater visibility of physical Twins and their current status. It also simplifies the system function's connection and documentation so that the physical devices' performance may be understood and clarified. Furthermore, Artificial Intelligence is continually improving DT's abilities by processing data from its physical counterpart and surroundings. Data assessment and prediction results in the detection of patterns, classification of data, and identification of the model. Additionally, it provides the essential features necessary for producing the necessary action from the vast amounts of data and information generated by the DT, such as classification, regression, clustering, and pattern recognition. The introduction of modern technologies, such as Blockchain, has recently made it easier to use Digital Twins in practical applications [ 14 , 15 ]. Also, the deployment of digital twins and the developing AI technologies like deep learning with cyber-physical systems (CPS) is aimed at improving manufacturing output and productivity [ 15 ]. It proposes a concept for smart manufacturing and Industry 4.0 through the use of conceptual schema and modern technology. In [ 23 ], they presented a novel scheme for fault detection and prognosis based on Deep learning and Digital Twins. The deep learning system may simply identify the malfunction in the system by observing the weakening of the actual part and making a comparison with its normal behavior. Digital Twins are used to creating a defect diagnosis for an electrical machine, which is proposed in [ 20 ]. A modified DT with the Industrial IOT system is introduced in [ 24 ] to improve the vision for smart production. It is believed that adopting DT is critical to developing the Industrial IoT, as it would enhance customer connection with the supply chain.

Digital twins are digital representations of physical objects or systems, which can be used to monitor and control their behavior in real-time. The Internet of Things (IoT) is a network of connected devices that can share data and communicate with each other. Together, digital twins and IoT can be used to create smart systems that can optimize performance and predict potential issues. Artificial intelligence (AI) can be used to analyze data collected from digital twins and IoT devices, to identify patterns and make predictions. AI algorithms can be used to develop control systems that can optimize the behavior of complex systems, such as manufacturing processes or transportation networks. Blockchain technology can provide a secure and transparent way to store and share data between different parties. This can be especially useful for digital twins and IoT devices, which can generate large amounts of data that need to be shared and verified in real-time. Blockchain can also be used to develop decentralized control systems, which can operate autonomously and provide a high level of security and resilience. In summary, digital twins, IoT, AI, control systems, and blockchain are all technologies that can work together to create smart and secure systems. By combining these technologies, it is possible to develop systems that can optimize performance, predict and prevent issues, and operate autonomously with a high level of security and transparency.

In this work, we will highlight the use of blockchain with the digital twins to benefit from their characteristics such as full transparency, promising availability, trusted traceability, and immutableness of the transactions. In conclusion, the primary contributions of this work can condense as follows:

Providing an outline of Digital Twins characteristics and applications and explore the digital Twins process.

Presenting perceptions, concerns, and application of Blockchain technology.

Highlighting the recent studies of the integration of blockchain with digital twins for various applications.

Providing a framework for blockchain-based digital twins for IoT systems.

Presenting opportunities and future research directions for this cutting-edge research subject.

Thus, the major contributions of this paper are:

Conceptual Framework The paper provides a conceptual framework that integrates digital twins with blockchain technology for the Internet of Things (IoT). This framework is designed to improve the efficiency, security, and transparency of IoT systems by enabling the creation of secure, decentralized, and immutable digital replicas of physical assets.

Architecture The paper proposes an architecture for integrating digital twins with blockchain technology that includes four layers: the device layer, the data layer, the blockchain layer, and the application layer. This architecture enables IoT devices to communicate with each other and share data securely and transparently, while also enabling the creation of smart contracts and other blockchain-based applications.

Challenges The paper discusses the challenges of integrating digital twins with blockchain technology, including issues related to data privacy, security, scalability, interoperability, and governance. The paper proposes several solutions to these challenges, including the use of encryption, decentralized identity, and consensus algorithms.

Future scope The paper highlights the potential future applications of integrating digital twins with blockchain technology, including in areas such as supply chain management, energy management, healthcare, and smart cities. The paper also identifies several areas for future research, including the development of new consensus algorithms, the integration of artificial intelligence and machine learning, and the exploration of new blockchain-based business models.

This paper is organized as follows. The concepts, characteristics, and applications of a digital twin are presented in Sect.  2 , while the integration of digital twins with blockchain is provided in Sect.  3 . The challenges and Future directions in research are introduced in Sect.  4 . Finally, the conclusion of the paper is presented in Sect. 5.

2 Digital Twins

2.1 taxonomy of digital twins.

In the 1970s, the first Digital Twins technique is developed during NASA's Apollo mission. In this technique, two identical spacecraft were released, one on Earth for flight training and the other to replicate the behaviour. In 2002, Grieves was the first to establish the essential digital twin paradigm for product life cycle management (PLM) [ 17 ]. The fundamental concepts are to develop the digital simulated model for a physical system where the simulation model’s information is twinned with the data from the real system throughout its entire life. Thus, the DT is incessantly analyzing the system's behaviour and performance in order to determine the location and the time of the fault is likely to occur and provide the best solution. As a result, digital Twin is known as an active paradigm capable of handling the variances in real-world systems. Appropriately, product lifecycle management is being digitalized (PLM) to increase the physical product's efficiency and ensure consistent and quick operation.

The three key elements of the DT are shown in Fig.  1 as the following: (1). The actual product, (2). The virtual model, and (3). The relationship between the digital model and the actual product transmits the data from the actual product to the virtual model and vice versa. A block diagram and a mathematical model are required to create such a digital twins system for a physical element. Later, a simulation is developed that is as close to the real thing as possible. Using a variety of communication and visualization approaches, the digital replica is recognized as sensor information for the real-time system. Then, to increase performance and depict how the thing would behave in a real model, this mimic model generates information and digital data that is fed back to its actual part. Consequently, a DT is believed to be an advanced system that can replicate, monitor, examine, regulate, control, and improve the response and function of the system.

Model of digital twins

Digital Twin is a relatively recent idea in control systems. Control system aims to regulate other system based on the required performance and actual output and it plays a crucial role in both automation and industry. So DT with a control system work in managing the product and making an optimal decision on the performance of the whole system like a Model Predictive Control (MPC) is implemented in DT from operational and sensor data to provide insights into the state of the equipment and the possibility of various failure types.

The network of physical items, or "things," that are implanted with sensors, software, and other technologies to communicate and exchange data with other devices and systems through the internet is referred to as the Internet of Things (IoT). Moreover, digital twins can IoT data because Iot provides real-time monitoring that can help in analytics to improve the performance of the system and give actual details of the whole process.

Some characteristics are distinctive for DT, which set this technology apart from existing technologies. These characteristics are:

The DT is a representation of the element's or system's real-time performance with a digital technique that renders it intelligent and fully programmable [ 7 ]. As the virtual representation is evolving simultaneously with the actual system through the operating process, predictive analysis and synchronization of fresh data with the physical system improve the operation [ 18 ].

Within Digital Twins, data is homogeneity. Data generated in several areas throughout the physical ecology can interact with one another. Likewise, there is an interaction between dynamic and historical data to give the necessary action. Due to the enormous amount of data, it must be a combination of DT with other techniques such as analysis methods, Big data procedures, and data fusion algorithms to deal with it that gives precise, meaningful data [ 6 ].

A fault diagnosis and fault prognosis use digital traces [ 20 ]. Both gathered data and feature extraction are combined to verify the location of the fault and apply this diagnosis to enhance the system’s design so that the same error and trouble will arise less in the future.

Self-adaptation and self-parametrization abilities [ 13 ], this allocates to adjust and enhance a physical model. Because of DT’s abilities, the modifications in a component will not disturb any components in the system, and it is simpler to change an unprofitable component with a superior one to enhance the overall behavior.

Within DT technology, optimization algorithms are utilized to attain the highest productivity by establishing a set of high-value alternative decisions based on a set of goals, criteria, and limitations.

2.2 Structure of Digital Twins

As demonstrated in Fig.  2 , the DT is regarded as an innovative technology thanks to its substantial effects on closing the gap between real assets, digital simulation, information, and services. Corresponding to Gartner Research [ 19 ], DT is likely to be employed by two-thirds of firms using IoT in the coming future. Many organizations propose Digital Twins systems, including Microsoft Azure IoT, GE Predix, IBM, Cisco Systems, Oracle Cloud SAP Leonardo, and other industrial manufacturers. Figure  3 shows an example of SAP Leonardo.

Vision of digital twins in PLM

Example of DT’s vendor [ 11 ]

The DT is a merging of various fields like computer science, control systems, and telecommunication scientist. The required DT system is created of key parts containing a digital model, data processing, and datasets [ 5 ]. The digital model is a digital reproduction of the real items and their elements, a model of the system's performance and defects, and a telecommunication model. It describes the structure of subsystems, subassemblies, and modules and creates a simulated version of every component with the gained receiving measurements from production, actions, and operation. To improve the product’s reliability, a replica of the sensors can be introduced inside the digital part of the system[ 20 ]. Also, the digital simulation could be a mirror image of the physical items by detecting and classifying the key element of actual items. Furthermore, sensor networks, storage systems, and data transmission are critical for digitally modeling the system and analyzing the data in DT. For operation analysis and validation of the virtual model of the process, both numerical models and simulation are needed.

Data fusion is the second key component of DT, and it is used to diagnose, forecast, and prescribe the actual system’s behavior by preprocessing and optimizing data. It analyses the physical assets’ performance by processing a large amount of data and information. For diagnostic and prognostic, the faults in physical systems, the analyzing data is transferred through knowledge rule-based.

In general, data fusion involves combining data from multiple sources to create a more comprehensive and accurate picture of a system or environment. In the context of digital twins, data fusion algorithms can help to merge data from various sensors and sources to create a unified view of the physical system being modeled. Some specific data fusion techniques that are commonly used in digital twins include:

Kalman filtering A recursive algorithm that estimates the state of a system by combining noisy measurements with a dynamic model of the system.

Bayesian networks A probabilistic model that uses graphical representations to model the relationships between variables and perform probabilistic inference.

Dempster–Shafer theory A mathematical framework for combining evidence from multiple sources that uses belief functions to represent uncertainty.

Fuzzy logic A technique that uses fuzzy sets to represent imprecise or uncertain information and can be used to model complex relationships between variables.

Neural networks A type of machine learning model that can be used to learn patterns in data and make predictions.

These are just a few examples of the many data fusion techniques that can be used in digital twins, and the specific technique chosen will depend on the nature of the system being modeled and the data available.

Connections and operations are the final component of DT for dealing with complex systems. A complex function that any single unit cannot perform can be accomplished by cooperating with the components to transmit critical information between components and continuously refining the digital model. With facility processes such as modelling, operation monitoring, and fault-tolerant, DT can define a real thing and recognize the malfunction data can be identified by DT technology and then suggest a fix that is appropriate for it. Because of the preceding, a crucial question arises.: Why will Digital Twins be the industry's cornerstone in the coming years? The basic concept behind Digital Twins is to use information and data to replicate physical sources. Hence, DT’s main merit is to boost efficiency and decrease the expense of the operation. Self-adaptation and self-learning are two of DT's characteristics. This aids in the modification of the system and provides an upcoming idea for the procedure to be done. As a result, while preparing to develop an advanced item, suggest a modification, or offer a new model, the knowledge and the data gathered from the Digital Twins would be employed to reduce problems experienced during the operation or product life cycle.

Other pros of utilizing DT are decreasing the fixing time, upgrading the operation procedure, and expecting malfunction. As the data from sensors are collected and analyzed, DTs can diagnose the behavior of the actual model and determine the best course of action. Then the data is classified to diagnose and determine the field's maintenance requirements. Besides, DT can analyze and conclude the object's condition, the duration of the valuable cycle, and the operation's ability. As a result, it will be able to detect and recognize the problem in the future, preventing both foreseen and unpredicted catastrophic operations, and the spare part will be simply estimated. Along with the data from the online measurement, the Digital Twin's simulated part and working history serve as the basis for more powerful service improvement. Stockholders can quickly access the real-time reports and relevant information because DT provides good interaction and records for the operation of the process.

2.3 Digital Twins Applications

Nowadays, there are many fields in which Digital Twins are applied, such as manufacturing, biomedical engineering, smart constructions, smart cities, and electrical energy, as shown in Fig.  4 as follows:

Smart manufacturing The primary sector in which Digital Twins are used in industry and manufacturing. DTs are mostly used in the area of product lifecycle systems in manufacturing. So, the DT's strategy in a lifecycle process is to gather and examine the information of a full life cycle operation and use that information to improve the system or results [ 7 , 23 ]. In [ 6 ], The utilization of DT in Big Data and Smart Manufacturing is described in depth. The differences between innovative trends and Digital Twins, as well as their significance, are presented for expanding the industries. In [ 5 ], the historical significance of DT in the production process and the life-cycle of the production are discussed. The main contributions of DT are provided for the creation and manufacturing of the assets. The authors in [ 16 ] provide a solution to data management difficulties for a DT of a product using blockchain technology. The proposed data managing system is built in a system that connects whole components implicated in the system, and later Blockchain is applied to guarantee that only permitted individuals have access to the database. A new framework in [ 24 ] is presented to providea data-driven framework for creation of simulation models from smart factory data is proposed. It facilitates the production processes by directing entrepreneurs to build a DT system and utilize the information provided.

Digital Twin's Applications

The DT is applied to the power plant in [ 25 ]. When DT is used to analyze a big power grid, the time necessary for assessment is decreased, and decision-making for operation is enhanced, resulting in a lower time–cost value when evaluated with another technology like as CPSs. Also, Digital Twins is working on the development of power electronics, as presented in [ 26 ]. The converter layer is modelled and viewed using FPGA in the DT for fault diagnosis. A comparative analysis with real-time intervention is given to discover irregularity in power electronics. In [ 27 ], an Experimental Digital Twins (EDT) is established to achieve a direct integration between actual and digital models and develop a representing domain. EDT is used to implement a controller technique, a smart approach, and a simulation-based procedure. Reference [ 28 ] presents the research approach for 3D printing using DT, machine learning, and Big Data. The computationally expensive period for trial and error is cut in this paradigm, and the product's time-to-market is reduced.

Smart cities Digital twin innovations have proven to become smarter in presenting useful smart city features. As the Internet of Things (IoT) and communications have advanced, so has research in this subject, leading to increased interest in the digitalization of our life. The latest technology that can be used for DT in smart cities is presented in [ 31 ]. The suggested architecture uses a knowledge-based method with machine learning to provide classification and decision-making processes for managing the transportation and power in a major city [ 32 ] describes a framework for developing smart healthcare services for individuals in smart cities through Digital Twins. A practical study demonstrated the proposed method approach's effectiveness by replicating the information collected and examining with retrieving the data.

Energy management in smart cities through the use of Digital Twins. Benchmark based on smart meter data processing is developed in [ 33 ] to establish a daily energy-building recommendation. The framework will determine what is the difference between the new expectations from conventional and annual energy assessment methods, as well as investigate how these processes can help power control close to real [ 34 ] gives us a glimpse of emergency management based on DT innovation. The paradigm is constructed by the incorporation of Information and Communication technologies with Digital Twins to produce a robust promise for enhancing the performance of catastrophe administration actions. Using data from high-fidelity boiler simulations, historical process data, and machine learning to manage a complex, multivariate system, a digital twin is developed for the Atikokan Generating Station in [ 35 ]. Dynamic predictions with uncertainty, comprising variables measured at the plant and variables required for constraining and optimising the digital twin, are among the outputs from the digital twin.

Biomedical Health care is the most effective industry that has benefited from using Digital Twins principles. Emerging healthcare IoT, fitness bands, and E-health technologies have supported the use of Digital Twins in biomedical areas. DTs technologies are utilized in the health industry to estimate the operation of examination equipment, evaluate and recommend medication, recognize lifestyle changes, enhance hospital activities, provide remote surgery, and assist governments with patient healthcare. A DT virtualization biological approach will assist clinicians in developing in vitro ways for predicting how the real organ will operate in either specified condition [ 36 ] investigate the growth of Digital Twins in the biomedical concept affects therapy, diagnostics, and well-being. In patient care, it stresses that the development of individual DT and information derived from it may be utilized in rehabilitation, augmentation, dietary behaviors, and illness prediction. In [ 11 ], the use of DT in the biomedical area is presented to aid in the analysis and detection of living things and nonliving objects, hence improving healthcare and welfare.

Saracco et al. investigated the use of DT in the pandemic of Covid-19 in their white paper [ 35 ] and specified the concept of Personal Digital Twins (PDT). Various firms are entering this industry because of its tendency and value of pandemic management with its economic implications. Using PDT plays a critical part in the actual observation of biological signals, tracking the actual position of the end user, and identifying the recourses to infect persons. DT has been combined with trend technologies such as Industry 4.0, Virtual reality (VR), and IoT to create a model of telemedicine surgeries [ 36 ]. A robotic arm with VR is used in this prototype, and they are attached through a 4G mobile platform to perform remote surgery on patients. The intricacies and pitfalls of integrating Digital Twins with a paradigm are examined and assessed emerging technologies that can help alleviate these challenges. Integration of DT with ICT in view of cloud technology is presented in [ 37 ] to create a framework that allows for the observation, examination, and prediction of a patient's state. With the usage of DT and Cloud technology, anticipating the disease and problems of older people can be done using IoT wearable sensors and in-home sensors.

3 Blockchain-Based Digital Twins

The advancements in current networking processing and storage technologies have resulted in the establishment of a new idea known as Digital Twins. The term "digital twins" refers to the interpretation of real-world tools, products, or components in our environment. It is used for physical component configuration, monitoring, diagnostics, and prognostics throughout its life cycle. Most current techniques used to create DTs are typically centralized, which has a disadvantage in terms of enabling audits, trustworthy data provenance, and traceability. As a result, the idea of DT may be reformatted under the introduction of Blockchain technology. It could be a critical approach to assisting IoT-based digital twin applications in transmitting information and quantity into the Internet with full clarity while also suggesting secure and trusted traceability, accessibility, and transaction immutability. The paper describes a context for Blockchain-based Digital Twins.

3.1 Overview

In recent times, Digital Twin (DT) is can be used for the creation, examination, diagnostics, and expectation of the physical element in its life cycle. The DT is considered as the representation of a real-world physical asset, product, or component around us. The previous study utilized DT’s definitions that emphasized two essential features, which are as follows:

Every definition highlights the link between the physical model and its simulated form [ 38 ].

That link is developed by creating real-time data from measurements [ 39 ]. The DT model will be compared to other concepts, such as cross-reality environments or co-spaces and replica models, which aim to synchronize with the actual system and its cyber model [ 40 ].

The DT of the product involves three modules: (1) the actual product, (2) the simulated model, and (3) The transmission link between the real component and the digital model. The DT for vehicle production is shown in Fig.  5 . The interaction between the actual and the digital model component is crucial in maintaining the strength and performance of DT. The information transferred from the simulated model to the actual product could be used to monitor and empower its performance.

A digital twin of car product

With the advent of Blockchain technology in recent years, it may now be utilized to redefine digital twins in a variety of Internet of Things applications. It may be used to securely and transparently transfer data and value across the Internet. To be conservative, a central intermediary capable of analytics and storing data is required to improve a digital twin system. The Blockchain may be used to produce and monitor digital twins safely and irreversibly. Similarly, a safe, trustworthy, robust, and dependable approach is required to track and trace the various steps in the development of DTs. Likewise, connecting digital twins and blockchain would help businesses and brands secure their products against counterfeiting and avoid financial losses.

3.2 Blockchain

In 2008, Nakamoto [ 41 ] created a blockchain to serve as the public transaction record for the cryptocurrency bitcoin. Blockchain is a growing collection of cryptographically connected blocks. The Blockchain network is structured as an ordered list of blocks; as illustrated in Fig.  6 , each block refers to the one before it, resulting in a blockchain. When a block is produced and uploaded to the blockchain, its transactions cannot be altered or reversed [ 42 ].

Blockchain connected network

The blockchain core is the consensus mechanism that declares all consensus nodes in the system agree on the overall status of the blockchain. Data producers, consensus nodes, and a data pool comprise a blockchain network. Data producers must first submit their data to the data pool, as indicated in Fig.  7 . Consensus nodes in the consensus network will then capture the data from the data pool. After confirming the seized data, the consensus node runs the consensus protocol and chooses the bookkeeping node. The bookkeeping node is responsible for writing data to the Blockchain [ 43 ].

The Blockchain network’s operation

The Blockchain system is a decentralized, distributed, and public digital ledger in which no record may be altered retrospectively without affecting all following blocks. Table 1 summarizes the many kinds of Blockchain. Blockchain is now employed in a variety of industries, including transportation, healthcare, electronic voting, logistics, and so on.

There are different algorithms that can be used for features extraction in blockchain systems, depending on the specific use case and the type of data being analyzed. One commonly used algorithm for feature extraction in blockchain systems is the SHA-256 algorithm.

The SHA-256 algorithm is a cryptographic hash function that takes input data and produces a fixed-length output, known as a hash. This hash is unique to the input data, and any changes to the input data will result in a different hash value. In blockchain systems, the SHA-256 algorithm is often used to create a digital fingerprint, or hash, of each transaction in the blockchain. This hash is then stored in the blockchain ledger, providing proof of the transaction and ensuring that the transaction cannot be altered or tampered with.

Other algorithms that may be used for feature extraction in blockchain systems include the Elliptic Curve Digital Signature Algorithm (ECDSA), the Secure Hash Algorithm (SHA-3), and the Advanced Encryption Standard (AES). The specific algorithm used will depend on the requirements of the use case, such as the need for security, speed, or efficiency.

Optimization algorithms are used to find the best solution to a problem by iteratively adjusting the values of parameters until a desired objective function is optimized or minimized. The objective function is a measure of how well the system is performing, and the optimization algorithm seeks to find the values of the parameters that result in the best performance.

There are many types of optimization algorithms, including gradient descent, simulated annealing, genetic algorithms, and particle swarm optimization. Each algorithm works differently and may be better suited for different types of problems. Gradient descent is one of the most commonly used optimization algorithms. It works by calculating the gradient, or rate of change, of the objective function with respect to the parameters. It then adjusts the parameters in the direction of the negative gradient to minimize the objective function.

Simulated annealing is another optimization algorithm that is based on a thermodynamic process. It works by starting with a high energy state and iteratively cooling the system until it reaches a low energy state, which corresponds to the optimal solution. Genetic algorithms are inspired by the process of natural selection. They work by creating a population of potential solutions and iteratively applying selection, crossover, and mutation to the population to generate new solutions. Particle swarm optimization is a population-based optimization algorithm that is based on the behavior of a swarm of particles. It works by iteratively adjusting the velocity and position of the particles in the search space to find the optimal solution.

The parameters of an optimization algorithm depend on the specific algorithm and the problem being solved. Common parameters include the learning rate, which determines the step size of the parameter updates, and the stopping criteria, which determine when the algorithm should terminate. Other parameters may include population size, mutation rate, and crossover rate, depending on the algorithm being used. The values of these parameters can greatly affect the performance of the optimization algorithm, and may need to be carefully tuned to obtain the best results.

3.3 Integrating Blockchain and Digital Twins

Blockchain with Digital twins is supposed to be used in tandem to improve security and help businesses prevent fraud and duplication of their components or/and model. Businesses have constantly been faking their items in recent years. Technology is available to everyone and is evolving at a rapid pace. As a result, it is considerably simpler for fraudsters to make duplicates than offer them to naïve buyers. These scammers not only create cash damages to respectable businesses but can also produce long-term reputational damage. The integration of digital twins with blockchain technology has the opportunity to provide a solution for preventing fraud and assisting businesses in maintaining the legitimacy of their products.

The number of IoT devices will exceed 20 billion by 2020. Millions of digital twins will be supported by these gadgets. Digital twins will be one of the essential cornerstones of physical item digitalization. Alternatively, blockchain technology will offer transparency with its decentralized structure, further improving digital data security. The notion of merging digital twins and blockchain may be utilized in a variety of fields, including logistics and biomedical systems. Figure  8 depicts the advantages of utilizing Blockchain for Digital Twins.

Benefits of using Blockchain for Digital Twins [ 39 ]

Integrating digital twins into a blockchain system involves creating a digital representation of a physical object, asset or system (the "twin") on the blockchain. This twin can then be used to track the object's performance, maintenance, and other data in real-time. This can be especially useful for industries like manufacturing, where the digital twin can be used to optimize production processes and identify potential issues before they occur.

However, there are several challenges associated with integrating digital twins into a blockchain system. These include:

Data standardization In order for digital twins to be integrated into a blockchain system, there must be a standardized format for the data associated with the twin. This can be difficult to achieve, especially in industries with complex systems and multiple data sources.

Data security Digital twins can contain sensitive information about physical objects and systems, making them vulnerable to cyber attacks. Integrating them into a blockchain system requires strong security measures to protect against unauthorized access.

Integration with existing systems Many companies already have established systems for tracking and managing their physical assets. Integrating digital twins into these systems can be a complex and time-consuming process.

Scalability As the number of digital twins in a system grows, the blockchain network must be able to handle the increased volume of data. This can be a challenge for some blockchain systems, which may not be designed to handle large amounts of data.

So, integrating digital twins with IoT involves connecting physical devices to a network and collecting data from sensors and other sources. The data is then used to create digital twins, which are digital representations of the physical objects or systems. These digital twins can be used to monitor and control the behavior of the physical systems in real-time, optimizing performance and predicting potential issues.

Blockchain technology can be used to secure and manage the data collected from IoT devices and digital twins. By using blockchain, it is possible to create a decentralized and transparent system that can store and share data securely between different parties. This can be especially useful for digital twins and IoT devices, which generate large amounts of data that need to be shared and verified in real-time.

In an IoT-based blockchain study, researchers may use a combination of techniques such as data analytics, machine learning, and cryptography to develop a secure and efficient system for managing data from IoT devices and digital twins. They may also develop new algorithms or protocols for optimizing the performance of the system and ensuring its scalability and reliability.

In summary, while integrating digital twins into a blockchain system can offer many benefits, there are several challenges that must be overcome to make it a viable solution for businesses.

3.4 Blockchain-Based Digital Twins Framework

In this part, we provide the suggested Blockchain-based Digital Twins system, namely BlockTwins, to the immune DT model, to guarantee the security of every transaction in the twins' system during the communication between the virtual and physical assets; transactions would not need to rely on third-party verifications. Every transaction would instead be timestamped before being added to a chain of hash-based proofs of work. This could stop any unauthorized users and criminals from making harmful changes to the system from the outside (Fig.  9 ).

Product lifecycle management based on Digital twin

Product design, production, maintenance, and other aspects of the product lifetime are created in the industrial control system and manufacturing, and these data are referred to as product lifecycle data. It's a difficult procedure with a lot of little aspects to consider at each stage. Product lifecycle management is necessary to ensure that all activities throughout the product lifecycle are under control. The advent of the DT has provided a means for monitoring all product activities throughout its full lifespan and optimizing process efficiency based on the Digital Twin's simulation outcomes., as shown in Fig.  5 . Thus, blockchain can be used to handle the problems in the data management of digital twins within the product lifecycle securely and efficiently. That issues related to data exchange, storage, access, and data validity.

Product lifecycle management (PLM) and process lifecycle management (PLM) are both concepts related to the management of digital twins. A digital twin is a virtual representation of a physical product, system, or process that can be used to monitor, analyze, and optimize its performance over its lifecycle. Product lifecycle management involves managing the entire lifecycle of a product, from concept and design to manufacturing, operation, and disposal. This includes managing product data, engineering changes, and collaboration between different teams and stakeholders.

Process lifecycle management, on the other hand, involves managing the entire lifecycle of a process, from process design to execution and monitoring. This includes managing process data, automation, and optimization. Both PLM and PLM are important for ensuring that digital twins are designed, developed, and operated in the most efficient and effective manner possible. By managing the entire lifecycle of a product or process, organizations can identify areas for improvement, optimize performance, and reduce costs.

Blockchain can be used to balance the load in PLM and PLM by providing a secure and transparent platform for managing data and transactions. Blockchain technology allows for the creation of tamper-proof, distributed ledgers that can be used to track the entire lifecycle of a digital twin, from its initial design to its end-of-life disposal. By using blockchain technology, organizations can ensure that all stakeholders have access to the same information and can trust the data that is being shared. This can help to streamline communication and collaboration between different teams and stakeholders, reducing the risk of errors, delays, and misunderstandings.

Overall, the combination of PLM, PLM, and blockchain can help organizations to create and manage digital twins in a more efficient, effective, and secure manner, improving performance and reducing costs over the entire lifecycle of a product or process.

To model the proposed scheme, it’s necessary to develop a Blockchain network to link every participant within the product lifecycle. The transaction records all activities of the DT for the object between competitors. The sensor’s readings between the real and virtual models are also captured as part of the transaction. Using hashing algorithms along with timestamps, these transactions are saved in the blocks connected, require the whole procedure, and are used to track the events. A blockchain-based product management network was created by these blocks interconnecting. Similarly, Blockchain is applied to manage primary stages engaged in the construction procedure of DTs, as shown in Fig.  10 . The proposed product lifecycle management-based Blockchain is shown in Fig.  11 .

Blockchain as controlling entity of DT design procedure

Blockchain-based product lifecycle management

Hashing algorithms are cryptographic functions that can be used to convert a piece of data into a fixed-size, unique digital fingerprint, or hash. In the context of digital twins, hashing algorithms can be used to provide a secure and tamper-proof way to store and verify data. We have provided hashing algorithms in the article for several reasons, including:

Data integrity Hashing algorithms can be used to ensure that data has not been tampered with or modified in any way. By generating a unique hash for each piece of data, any changes made to the data will result in a different hash value. This can help to ensure the integrity of the data stored in digital twins.

Data security Hashing algorithms can also be used to provide data security. By storing hash values instead of the original data, organizations can ensure that sensitive information is not exposed to unauthorized parties.

Efficient data comparison Hashing algorithms can also be used to compare large amounts of data efficiently. Instead of comparing the entire data set, organizations can compare hash values to quickly identify differences and anomalies.

Blockchain integration Hashing algorithms are often used in blockchain technology to create digital signatures, validate transactions, and ensure the integrity of the blockchain ledger.

Overall, hashing algorithms are an important tool for ensuring the security and integrity of data stored in digital twins, and can help organizations to efficiently manage and analyze large amounts of data.

An optimization technique is a mathematical method that helps to find the best solution to a problem within given constraints. Optimization techniques are used in various fields, such as engineering, finance, economics, and science, to find the optimal solution to problems that involve maximizing or minimizing an objective function. Some common optimization techniques are linear programming, quadratic programming, genetic algorithms, simulated annealing, and gradient descent. Blockchain for Digital Twins is a concept that uses blockchain technology to create digital twins, which are virtual replicas of physical objects or systems. The digital twin can store data about the physical object, such as its design, maintenance history, and current status. By using blockchain, the data stored in the digital twin is secure, immutable, and transparent.

Blockchain for Digital Twins is needed because it can provide many benefits, such as:

Improved traceability and transparency By using blockchain technology, the data stored in the digital twin is immutable, meaning that it cannot be altered or deleted. This provides greater transparency and traceability for the physical object, which can be beneficial for various applications, such as supply chain management or asset tracking.

Increased efficiency Digital twins can be used to optimize the performance and maintenance of physical objects. By analyzing data from the digital twin, it is possible to identify potential issues and take preventive actions, which can reduce downtime and maintenance costs.

Enhanced security Blockchain technology provides enhanced security for the data stored in the digital twin. Because the data is stored on a decentralized network, it is less vulnerable to cyber attacks or data breaches.

Overall, Blockchain for Digital Twins is a promising concept that can bring many benefits to various industries.

4 Challenges and Future Research Directions

The research community faces numerous difficulties and challenges in the integration of Digital Twins in several parts of our lives.

One of the key issues with the application is the high price for large enterprises due to the requirement of multiple software elements in addition to levels of hardware parts to make a DT. As a result, significant work has been carried out in order to construct a low-cost or establish a regular DT framework in the production and health sectors.

Data privacy and security are two key issues with DT. Data and security privacy are two more key issues with DT. Because the process displays information or data, protecting this data from viruses and hacking is a significant problem in DT since it can destroy essential information in digital contexts. Despite emerging innovations such as Blockchain having been used to provide a solution to the DT security challenge, this field is still in its early stages. Other issues include data collection and storage, as a massive volume of data and information flows between hardware components. Big data, cloud technology, artificial intelligence, and sensor data fusion are strategies utilized for creating a framework for the growth of technologies that would support this challenge.

Technical issues include real-time communication and data delay. Working in physical environments may expose you to DT. New technologies, such as 5G communication, IoT technology, and data compression, are being used to address this issue. Another issue that occurs while implementing DT is the requirement for ongoing maintenance and modification of virtual conditions. Deep Learning algorithms, for example, could potentially enrich and improve Digital Twins technology in this regard. In Digital Twins, standardization and ethical issues are key concerns. The usage of DT in industry or healthcare is not currently standardized. Also, the ethical problem in studies is especially important in healthcare and science because there is access to high-fidelity data about the patient, and his lifestyle demands the use of ethical standards.

5 Conclusions and Future Works

The Digital Twin is a dynamically changing notion that grows in functionality during its life cycle. This study explained the definition and design of DTs and the benefits of using them in numerous applications such as production, smart cities, and biomedical. Because of its major impacts on bridging the difference between the real, digital environment and information, the DT has recently become an emergent technology. Similarly, with the introduction of Blockchain technology, the idea of digital twins may be reinterpreted. This led to powerful technology for empowering IoT-based digital twins to exchange information and benefits through the Internet with features such as visibility, trustworthiness, and tracking. Therefore, this work also presented a framework of Blockchain-based Digital Twins for IoT system. Finally, the challenges and opportunities in this innovative topic are discussed.

The integration of digital twins with IoT-based blockchain is an emerging field with numerous potential future works. Here are some possible avenues for future research:

Performance optimization One area that requires attention is performance optimization. Currently, integrating digital twins with IoT-based blockchain can be computationally intensive, leading to latency and scalability issues. Future research can focus on developing optimized algorithms and architectures that can improve the efficiency and speed of the integration process.

Enhancing security The integration of digital twins with IoT-based blockchain aims to enhance security and privacy. However, there is a need to address security challenges, such as data tampering and unauthorized access. Future research can focus on developing more robust security protocols and architectures that can improve the security of the system.

Interoperability Another area that requires attention is interoperability. Integrating digital twins with IoT-based blockchain can involve multiple technologies and platforms. Future research can focus on developing interoperability standards that can enable seamless integration of digital twins with IoT-based blockchain across different platforms.

Real-time monitoring and control Digital twins are known for their ability to simulate real-world scenarios. Future research can explore the potential of integrating digital twins with IoT-based blockchain for real-time monitoring and control of physical systems. This can be useful in a wide range of applications, including manufacturing, healthcare, and transportation.

Sustainability Integrating digital twins with IoT-based blockchain can contribute to sustainability efforts by enabling more efficient use of resources and reducing waste. Future research can focus on exploring the potential of this integration to promote sustainable practices across various industries.

Data analytics and machine learning Integrating digital twins with IoT-based blockchain can generate large amounts of data. Future research can focus on developing advanced data analytics and machine learning algorithms that can extract insights and patterns from this data.

Application in various industries Finally, future research can explore the potential of integrating digital twins with IoT-based blockchain in various industries, including manufacturing, healthcare, transportation, and energy. This can help to identify specific use cases and applications where this integration can have the most significant impact.

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Ezz El-Din Hemdan

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Walid El-Shafai

Department of Electronics and Electrical Communications Engineering, Faculty of Electronic Engineering, Menoufia University, Menouf, 32952, Egypt

Department of Industrial Electronics and Control Engineering, Faculty of Electronic Engineering, Menoufia University, Menouf, 32952, Egypt

Amged Sayed

Department of Electrical Energy Engineering, College of Engineering and Technology, Arab Academy for Science Technology & Maritime Transport, Smart Village, Egypt

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Hemdan, E.ED., El-Shafai, W. & Sayed, A. Integrating Digital Twins with IoT-Based Blockchain: Concept, Architecture, Challenges, and Future Scope. Wireless Pers Commun 131 , 2193–2216 (2023). https://doi.org/10.1007/s11277-023-10538-6

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DOI : https://doi.org/10.1007/s11277-023-10538-6

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