The importance of Internet of Things (IoT) has been demonstrated by the innovative and intelligent technologies and services that it can provide. It is composed of numerous physical objects that are linked to the Internet and are able to communicate with each other. These objects eventually generate a massive amount of data and thereby gather and broadcast them into information systems and technologies such as the cloud, Service-oriented Architecture (SoA), and workflow management system. The integrated technologies have proven their effectiveness in enhancing IoT systems’ availability as services worldwide.
An IoT system can be described as a network of interconnected smart devices and objects that are able to communicate and transfer data using unique identifiers. Communications are processed without the intervention of humans or other computers. IoT has been a platform for different domains, including business, health, and education, which have generated considerable recent research interest. Furthermore, a distributed trust technology, scalability, security, and privacy, is fundamental to the growth of IoT applications [1
]. Therefore, blockchain technology requires these underlying features in order to build a solid transaction process. Blockchain first appeared as a solution for the crypto-currency Bitcoin founded by Satoshi Nakamoto in 2008 [2
] which relies on security features such as digital signatures and distributed consensus mechanisms [3
]. Its transaction flows in a peer-to-peer network of nodes by generating a chain of data structure that contains blocks of encrypted data and information in a chronological order. Blockchains basically form a distributed ledger that provides a data storage service and records secure transactions or transactional events using cryptography.
Integrating blockchain with the IoT environment will inherit some of IoT’s challenges which prevent the direct deployment of blockchain for IoT. In the following, we will discuss critical issues and challenges behind the integration.
The crucial challenge has centered around the use of consensus protocol. Makhdoom et al. [5
] have shown the lack of IoT centric consensus protocol through testing a case scenario of an IoT-based supply chain monitoring system. In addition, Viriyasitavat et al. [6
] have analyzed some challenges related to the use of consensus protocol specifically in the time required to reach a finality settlement. They claimed that most IoT applications require a time-critical operations which indeed affect the finality settlement. The delay is also influenced by the number of nodes which is a primary step in the selection of consensus protocol either public, private, and permissioned blockchain systems. Additionally, storage and trust are critical issues in IoT devices, and blockchains usually generate massive data as composing transaction blocks. The challenge is that IoT devices are unable to verify the transactions generated by others while sender needs historical data. Wang et al. [7
] have stated that the IoT devices should either trust itself by taking the storage load, or trust remote servers, which impose extra communication overhead and secured communication between the IoT devices and the trusted servers. They also claimed that reducing the storage load is done by dealing with IoT devices as a light node in the blockchain system which only save the block headers. Security and privacy have received significant attention in the studies. Wang et al. [7
] have raised an important concern in ensuring the secure input of sensor data to the blockchain. Security should also be confirmed in communication between the IoT devices and the trusted servers. A survey on privacy protection has been presented by Feng et al. [8
]. They classified the privacy attacks into de-anonymization and transaction pattern exposure. These threads could be reduced by considering two privacy requirements: identity and transaction. Wang et al. [7
] have discovered other challenges such as computation, energy, storage, communication, mobility, latency, and capacity.
The rest of the paper is organized as follows: related works are discussed in Section 2
to study the prior researches in adopting blockchain and IoT, it also emphasizes the significance of the topic. In Section 3
, the conceptual model and research hypotheses are presented. The methodology and data analysis are illustrated in Section 4
to validate the proposed model. Discussion is presented in Section 5
. Finally, Section 6
concludes the paper.
2. Related Works
The concept of a connected world was started in the early 1960s and followed by a series of developments up until modern day technology. IoT holds the same concept of connected machines, and was first introduced in 2006 by Adelmann et al. [9
] in a conference paper entitled “Toolkit for bar code recognition and resolving on camera phones-jump starting the internet of things”. In recent years, IoT research has experienced growth and development in an interdisciplinary manner. Different fields of knowledge including technology, applied engineering, economics, business, industry, management, etc., have been discussed as underlying issues for IoT. This can lead to confusion in understanding the direction that IoT development is progressing in [10
]. Most businesses or industrial sectors look to adopt IoT functions and features in an attempt to find novel solutions to their problems [11
]. Dachya et al. [10
] mentioned the top industries that have adopted IoT, which include manufacturing, agriculture, public service, health, electronics, and energy [11
]. In fact, adopting IoT in any industry requires understanding its underlying architecture which consists of four levels. The first is a perception layer composed of different sensors and data collectors, followed by a network layer that controls the transmission of the data; the next layer is the middleware layer, which involves the information processing systems; finally, the fourth level is the application and services level [12
Recently, interest has been generated regarding integrating blockchain into IoT process. Negka et al. [13
] examined the effect of employing blockchain technology to overcome the problems in IoT ecosystems when handling counterfeit copycat devices. They proposed an approach that depends on identifying each one of the used components through the use of Physical Unclonable Functions (PUF) responses, and employs blockchain technology in order to set-up a platform for tracking the supply chain of both component and IoT devices, without requiring the existence of any central authority. In the same context, Rejeb et al. [14
] presented a methodology of deploying blockchain technology with IoT infrastructure to facilitate and flatten the process of modern supply chains and enhance value chain networks. Moreover, Mendki [15
], in his study of edge and fog computing, discovered the use of blockchain based decentralized application to enable IoT edge processing and scaling via enabling resource owner to join the ecosystem and lend the compute resources as needed. As regards leveraging the IoT and blockchain technology to detect distributed denial of service (DDoS) attacks, Spathoulas et al. [16
] installed lightweight agents in multiple IoT installations to detect DDoS attacks conducted through the use of IoT-device botnets. Blockchain ledgers govern information exchanged between IoT smart devices and control the integrity of both the procedure and the information. Finally, Tseng et al. [17
] proposed an architecture that implements blockchain in managing heterogeneous IoT systems, as well as presenting the related challenges in terms of integration and development.
The benefit of using blockchain is to provide a decentralized digital database of transactions, called the distributed ledger, which is maintained and updated by a network of computers that verify a transaction before it is approved and added to the ledger. This allows transaction parties to exchange ownership of digitally represented assets in a real-time and immutable peer-to-peer system without the use of intermediaries. Aloqaily et al. [18
] highlighted the impact of blockchain in P2P energy trading. They proposed a distributed trading framework and smart contracts to recognize the benefits of blockchain with energy. Morkunas et al. illustrated the six steps of asset exchange between two economic actors using blockchain technology [19
]. The transaction process has to be maintained and controlled via blockchain consensus protocols, therefore, Zhang and Lee [20
] compared and analyzed different types of protocols in terms of their strengths, weaknesses, and application scenarios. They reached the conclusion that a good consensus protocol should consider fault tolerance and make the best use of it in the appropriate application scenario.
Any newly introduced technology faces resistance in terms of its adoption in other platforms or environments. Therefore, the Technology Acceptance Model (TAM) is a powerful model developed to measure and analyze the factors involved in the resistance to the adoption of the new technology [21
]. This model was derived from the Theory of Reasoned Action (TRA) [22
], in which the reasoned action is explicitly concerned with behavior; thereby, this theory demonstrates the influence of situations or factors on the attitude and behavior. However, the TAM model concentrates on explaining user acceptance of information technologies through predetermined factors that measure the success of integrating new technologies into a current platform. Boer et al. [23
] presented a description of the TAM in which the user perception on perceived usefulness (PU) and perceived ease of use (PEoU) influence one’s attitude towards a technology, which, in turn, influences the actual technology use or intention to use it. Moreover, the model factors are associated with and influenced by each other, therefore, PEoU influences PU, and PU can be directly associated with the use of technology or the intention to do so. In this model, PU refers to the extent to which someone trusts that the technology will improve their performance. PEoU can be described as the extent to which someone trusts that using the technology is effortless [23
]. Mital et al. [24
] described the studies that have extended the TAM model in different contexts by introducing new factors into the TAM framework, such as underlying belief factors, and antecedent, moderator, and mediator variables. Such extensions consider usage adoption in different contexts: the office context, in mobile marketing, Internet banking, computer center resources, e-shopping, social networks, and smartphones. The challenges involved in theory improvement and adding more factors for each specific context are complicated, therefore, Mital et al. [24
] cited various studies that proposed general factors that can be associated with the original TAM, such as trust, cognitive and social factors, demographic variables, perceived enjoyment, and trust.
This study measures the effect of integrating blockchain technology into IoT technology. We firstly identified the critical factors related to the adoption of blockchain and IoT technologies. The significance of the study concentrates on developing a comprehensive theoretical conceptual model that can explain a large proportion of variance in the intention to adopt blockchain with IoT technology. The Technology Acceptance Model (TAM) was used and developed to assess the level of integration of blockchain and IoT technology. The results of the data analysis gathered throughout the survey show the strength of the proposed blockchain IoT technology model. The statistical analysis on the four dimensions of factors revealed a significant positive influence in terms of adopting blockchain IoT technology. The study confirmed the hypotheses with strong correlations in four dimensions of factors and sub-factors. This was done by including the opinions of specialists in both blockchain and IoT technologies.
The dimensions of factors were evaluated using the survey. When comparing the different dimensions and factors of the model with respect to mean, data-related factors were found to be the most influential in relation to the other factors. In particular, data validity was the most critical factor, and more attention should be paid to it. Thus, professionals and users of blockchain and IoT systems have to be aware of data validation strategies and regulations. As was discussed in Section 3.2
, data validity focuses on the correctness of data, since the methodology involved in blockchain IoT technology requires a level of distribution and partitioning. This maintains an open environment that produces massive data that requires reliable and efficient communication between connected devices. Adaptive buffers, error detection values, and thresholds are some example of techniques to guarantee data validity [68
When it comes to the coefficient of determination
, which measures the proportion of the variance in the dependent variable that is predictable from the independent variable(s), it is notable from Table 4
that the behavioral intention factor and its sub-factors had the highest value of 0.94. This indicates the importance of attitude towards blockchain and IoT systems in addition to social influencers. In particular, social networks and reputation have a critical impact on behavioral intention since they have the highest
In terms of the Pearson correlation shown in Table 3
, the greatest correlation was demonstrated between perceived usefulness and transaction intention. This correlation had an indirect effect on the other factors. Generally, the results illustrate that PU not only has a direct effect on the intention to use a technology, but also it has an indirect effect through the TI on the intention to use a technology. Thus, the results show that TI has a direct effect on the intention to use blockchain and IoT technology [26
With regard to the negative impact on the conceptual model, the results show no significant negative impact except security factors and the personal competency in attitude factor. The values are very close to the other factors in the model, and they only show a slight difference when compared with the rest of the factors (see Table 4
). In this case, security risks should not be covered in this type of technology unless security threats have encountered [57
]. In today’s digital interconnected platforms especially for IoT systems, which consist of open and connected devices which are exposed to attacks and possibilities for cybercriminals. As discussed in Section 3.2.2
, guaranteeing decentralization, streamlining, and risk-free transactions within IoT infrastructures is critical to limit cyber attacks targeting centralized systems [60
In summary, trust-related factors in the adoption of blockchain IoT technology were more significant than behavioral intention factors. This is because of the importance of data-related factors on trust. Thus, professionals should consider this in terms of increasing the level of trust. The findings additionally revealed that social-related factors are important for the adoption of blockchain IoT technology, but the consequences of this are limited in terms of behavioral intention. To explain, professionals may have a solid reputation, good social networking skills, a good level of interest, and can commit, but they have low or no personal competency, which affects the overall behavioral intention to use the technology.
In conclusion, this paper addresses the close connection between blockchain and IoT technologies. In the digital era, new technologies such as blockchain and IoT systems have come to dominate in several industries because of the robustness and effectiveness they can offer. In this paper, we propose a conceptual model that identifies the critical factors that have an impact on integrating blockchain and IoT technology. The analysis of the study reveals the positive impact of data-related factors, i.e., data integrity, data validity, data governance, and data privacy. Moreover, behavioral intention and the related factors have a significant effect in terms of adopting these two technologies. In contrast, security-related factors are less significant as compared with the other factors, which is reasonable since these types of technology require open communication and form an open network of connected devices. This study has some limitations that need to be addressed. The first limitation concerns the generalizability of the findings. The study was conducted in Saudi Arabia, which highly promotes the emergence of technologies and tools that support the 2030 vision. This involves enhancing business and investment in the country and empowering the financial industry to a tremendous degree. Thus, countries with a limited technological infrastructure will not able to adopt the proposed model effectively. In addition, these technologies are novel and professionals with a limited technical background will struggle to adopt them in their business. Therefore, knowledge of the factors that effect their integration is not sufficient in terms of attaining the potential benefits.
Future work may further explore the problem under study, extending it to encompass more factors or enhancing the current model to fit any improvements in both technologies. Since this model is directed at applications and domains that exist in Saudi Arabian sectors, the model could be improved and aligned with cultural beliefs and/or governmental regulations. In terms of practical enhancements, some changes in practical standards or performance indicators could effect the model’s adoption, thus more factors could be adopted. Lastly, to verify the validity of the proposed model, a real-world experiment should be performed in real industries to tackle any limitations and/or to reveal avenues for further research.