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Industry 4.0 connects the latest technologies such as cloud computing, Internet of things (IoT), machine learning and artificial intelligence (ML/AI), and blockchain to provide more automation in the industrial process and also bridges the gap between the physical and digital worlds through the cyber-physical system. The inherent feature of IoT dev...
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Academics and industry researchers alike find privacy-preservation of large data to be a very intriguing field of study. Data collection, storage, and processing are the three steps of big data's life cycle. At different stages of the big data life cycle, different privacy and security solutions are used. Many health-care stakeholders are working t...
Citations
... Khattak et al. [74] 2020 Application of Blockchain technology to introduce dynamic pricing in IIoT thereby enabling energy management in smart cities. Yu et al. [75] 2021 BC-enhanced data sharing with traceable and direct revocation in IIoT. Dwivedi et al. [76] 2021 Blockchain-Based Internet of Things and Industrial IoT. Latif et al. [27] 2021 Presented a comprehensive survey on security issues, Blockchain architectures, and applications from the industrial perspective. ...
Currently, Blockchain (BC), Artificial Intelligence (AI), and smart Industrial Internet of Things (IIoT) are not only leading promising technologies in the world, but also these technologies facilitate the current society to develop the standard of living and make it easier for users. However, these technologies have been applied in various domains for different purposes. Then, these are successfully assisted in developing the desired system, such as-smart cities, homes, manufacturers, education, and industries. Moreover, these technologies need to consider various issues-security, privacy, confidentiality, scalability, and application challenges in diverse fields. In this context, with the increasing demand for these issues solutions, the authors present a comprehensive survey on the AI approaches with BC in the smart IIoT. Firstly, we focus on state-of-the-art overviews regarding AI, BC, and smart IoT applications. Then, we provide the benefits of integrating these technologies and discuss the established methods, tools, and strategies efficiently. Most importantly, we highlight the various issues--security, stability, scalability, and confidentiality and guide the way of addressing strategy and methods. Furthermore, the individual and collaborative benefits of applications have been discussed. Lastly, we are extensively concerned about the open research challenges and potential future guidelines based on BC-based AI approaches in the intelligent IIoT system.
... Even though the paper does not elaborate on the role of AI, it provides valuable insights into the rich potential of blockchain technology in health care, especially in addressing data security and privacy, which can be complemented and further enriched by AI interventions. The integration of IoT with blockchain explored by Dwivedi et al. [14] sets a precedent for the profound impact that such a combination can have on diverse domains. In their extensive survey, they examine the need for smart contracts in IoT systems and highlight the state-of-the-art research in the convergence of blockchain and IoT. ...
This paper explores the dynamic intersection of Artificial Intelligence (AI) and the Internet of Things (IoT), focusing on emerging trends in intelligent data analysis and privacy protection. Employing a systematic literature review, we analyze recent advancements and challenges in AI-IoT integration. The paper highlights key developments in sensor data anomaly detection, AI-driven big-data analytics, and IoT-based communication techniques. It delves into the com-plexities of privacy protection in IoT, examining innovative solutions like federated learning and blockchain technologies. The study also addresses the challenges in AI for IoT, prioritizing the most pressing issues such as data security, ethical implementation, and compliance with regula-tions like GDPR. The research underscores the need for adaptable solutions that balance techno-logical advancements with privacy and security considerations, setting a path for future explora-tion in AI-empowered IoT applications.
... This gap indicates a need for more effective data documentation frameworks that align with the workflows and contexts of ML practitioners. Dwivedi et al. (2021) provide a comprehensive survey on the integration of IoT with blockchain technology, addressing challenges such as decentralization, security, and privacy in IoT systems. Their research highlights the potential of blockchain in enhancing data storage and management techniques, suggesting a research gap in exploring blockchain's role in securing and managing IoT data. ...
... The lack of connection between data documentation and responsible AI implications points to a research gap in developing frameworks that explicitly address ethical and responsible use of data in AI and ML applications. Dwivedi et al. (2021) discuss the need for smart contracts in IoT and IIoT systems, indicating a gap in research on the application of blockchain technology for automated and secure transactions in IoT environments. This gap presents opportunities for exploring how blockchain can streamline data storage and processing in IoT networks. ...
... Addressing this gap is crucial for enhancing transparency and usability in data storage and processing. The research gaps identified by Ang et al. (2022), Heger et al. (2022), and Dwivedi et al. (2021) point to the need for integrated, ethical, and secure data storage solutions that leverage the strengths of emerging technologies like AI, ML, blockchain, and IoT. Addressing these gaps will be pivotal in advancing data storage technologies and ensuring their responsible and effective use in various domains, including smart cities, IoT, and industrial applications. ...
In an era where data is the new gold, understanding the evolution and future trajectory of data storage technologies is crucial. This paper delves into the transformative journey from traditional storage methods to contemporary paradigms like cloud and edge computing, underpinned by the burgeoning influence of Big Data, IoT, AI, and machine learning. The study's aim is to provide a comprehensive analysis of these technologies, assessing their development, efficacy, and the challenges they face in meeting the escalating demands of data storage. The methodology employed is a meticulous synthesis of literature reviews, case studies, and comparative analyses. This approach facilitates an in-depth exploration of the historical evolution of data storage, the paradigm shifts from cloud to edge computing, and the interplay between technological advancements and user demands. The study also scrutinizes the security concerns inherent in these technologies and identifies strategic directions for future research. Key findings reveal that while cloud computing has revolutionized data storage with its scalability and flexibility, edge computing emerges as a vital solution to latency and bandwidth limitations. The integration of AI and machine learning is identified as a pivotal factor in enhancing the efficiency and intelligence of data storage systems. However, this integration presents unique challenges, necessitating innovative solutions. Conclusively, the study recommends a continued focus on innovation in data storage technologies, emphasizing the development of integrated, secure, and efficient solutions. Future research should particularly explore the potential of AI and machine learning in overcoming current limitations. The paper's scope encompasses a comprehensive overview of the current state and future potential of data storage technologies, making it a valuable resource for researchers, technologists, and policymakers in the field. Keywords: Data Storage Technologies, Cloud Computing, Edge Computing, Big Data, Internet of Things (IoT), Artificial Intelligence (AI).
... A large number of research papers surveyed the various blockchain (BC) solutions for IoT networks and presented several taxonomies and summaries from different perspectives for these solutions [6][7][8][9][10][11][12][13][14]. These papers reviewed the integration of BC into IoT in general and classified the solutions and systems in the literature using various classification models and methods. ...
... Reference This paper [6][7][8][9][10][11][12][13][14] [15] [16] [17] [18] Reviews blockchain solutions for resource-constrained networks • We analyze all lightweight BC solutions that were proposed in the literature. Based on this analysis, we classified the solutions into five main categories based on the lightweight aspect addressed in the solution. ...
Abstract
The proliferation of resource-constrained devices has become prevalent across various digital applications, including smart homes, smart healthcare, and smart transportation, among others. However, the integration of these devices brings many security issues. To address these concerns, Blockchain technology has been widely adopted due to its robust security characteristics, including immutability, cryptography, and distributed consensus. However, implementing blockchain within these networks is highly challenging due to the limited resources of the employed devices and the resource-intensive requirements of the blockchain. To overcome these challenges, a multitude of researchers have proposed lightweight blockchain solutions specifically designed for resource-constrained networks. In this paper, we present a taxonomy of lightweight blockchain solutions proposed in the literature. More precisely, we identify five areas within the “lightweight” concept, namely, blockchain architecture, device authentication, cryptography model, consensus algorithm, and storage method. We discuss the various methods employed in each “lightweight” category, highlighting existing gaps and identifying areas for improvement. Our review highlights the missing points in existing systems and paves the way to building a complete lightweight blockchain solution for networks of resource-constrained devices.
... A Standard structure of a blockchain[8]. ...
Blockchain is an exciting new technology that has garnered attention across multiple industries. This new technology offers several advantages, including decentralization, transparency, and immutability. However, several issues limit the effectiveness of this technology, such as scalability, interoperability, and privacy. A systematic review of blockchain scalability research was conducted using three primary databases: ACM, Science Direct, and IEEE. The review examined the state of the art in blockchain scalability, identifying the most important research trends and challenges. The solutions that have been established can be categorized into two main groups: those that pertain to block storage and those that pertain to the underlying blockchain mechanism. Numerous solutions were suggested for each main group. The most common proposed solutions for improving the scalability of blockchain networks in the literature are improving the consensus algorithm and using sharding. Most of the solutions were proof of concept and need more investigation in the future
... As stated by Sanjeev Dwivedi et al., the main objective of IoT-based smart home automation systems is to enhance and simplify people's lives [19]. These systems utilize a variety of smart consumer products and sensors to provide convenience. ...
... These systems utilize a variety of smart consumer products and sensors to provide convenience. The authors explain that the IoT industry incorporates automated electrical appliances, wearable electronics, and tracking devices across various sectors such as agriculture, healthcare, and energy [19]. However, due to their limited processing capacity, IoT devices are susceptible to security attacks. ...
... However, due to their limited processing capacity, IoT devices are susceptible to security attacks. The authors have identified four major categories of IoT-related attacks, namely physical attacks, network attacks, software attacks, and data attacks [19]. To address these security concerns, the authors suggest that blockchain technology can serve as a robust solution. ...
With the development of Internet of Things (IoT) technologies, industries such as healthcare have started using low-powered sensor-based devices. Because IoT devices are typically low-powered, they are susceptible to cyber intrusions. As an emerging information security solution, blockchain technology has considerable potential for protecting low-powered IoT end devices. Blockchain technology provides promising security features such as cryptography, hash functions, time stamps, and a distributed ledger function. Therefore, blockchain technology can be a robust security technology for securing IoT low-powered devices. However, the integration of blockchain and IoT technologies raises a number of research questions. Scalability is one of the most significant. Blockchain’ scalability of low-powered sensor networks needs to be evaluated to identify the practical application of both technologies in low-powered sensor networks. In this paper, we analyse the scalability limitations of three commonly used blockchain algorithms running on low-powered single-board computers communicating in a wireless sensor network. We assess the scalability limitations of three blockchain networks as we increase the number of nodes. Our analysis shows considerable scalability variations between three blockchain networks. The results indicate that some blockchain networks can have over 800 ms network latency and some blockchain networks may use a bandwidth over 1600 Kbps. This work will contribute to developing efficient blockchain-based IoT sensor networks.
... Multiple examples can be found in the literature across various contexts. For example, in the industry, for industrial IoT solutions [11,27], such as those focusing on supply chain [85], or food traceability [47]. Athavale et al. integrate blockchain with IoT for storing and managing data [9], and similarly, Ozyilmaz et al. take advantage of smart contracts to develop a marketplace for data obtained by IoT devices [56]. ...
Nowadays, Internet of Things (IoT) devices, especially wearable devices, are commonly integrated into modern intelligent healthcare software. These devices enable medical practitioners to monitor pervasively patients’ parameters outside the clinical environment. However, the ease of manipulating wearable devices and their data streams raises concerns regarding patient privacy and data trust. Distributed ledger technologies (DLT) offer solutions to enhance resistance against information manipulation and eliminate single points of failure. By leaveraging DLT, wearable-based solutions can be developed with a wider range of capabilities. This paper carries out an analysis of shortcomings, limitations, potential applications and needs in the medical domain, to introduce Phonendo 1.0, a DLT–IoT-based platform designed to capture data streams from wearable devices and publishing them on a distributed ledger technology infrastructure. The architecture and its difference services are justified based on the identified needs and challenges in the medical domain.
... Health sensors and sensor-based aided devices are battery-powered devices that can be damaged by high temperature [29]. Particularly, if health sensors are damaged or corrupted due to high temperatures, health data can be lost [30]. Additionally, in high-temperature conditions, health sensors may observe false data. ...
... sensor-based aided devices are battery-powered devices that can be damaged by high temperature [29]. Particularly, if health sensors are damaged or corrupted due to high temperatures, health data can be lost [30]. Additionally, in high-temperature conditions, health sensors may observe false data. ...
Blockchain technology is an information security solution that operates on a distributed ledger system. Blockchain technology has considerable potential for securing Internet of Things (IoT) low-powered devices. However, the integration of IoT and blockchain technologies raises a number of research issues. One of the most important is the energy consumption of different blockchain algorithms. Because IoT devices are typically low-powered battery-powered devices, the energy consumption of any blockchain node must be kept low. IoT end nodes are typically low-powered devices expected to survive for extended periods without battery replacement. Energy consumption of blockchain algorithms is an important consideration in any application that combines both technologies, as some blockchain algorithms are infeasible because they consume large amounts of energy, causing the IoT device to reach high temperatures and potentially damaging the hardware; they are also a possible fire hazard. In this paper, we examine the temperatures reached in devices used to process blockchain algorithms, and the energy consumption of three commonly used blockchain algorithms running on low-powered microcontrollers communicating in a wireless sensor network. We found temperatures of IoT devices and energy consumption were highly correlated with the temperatures reached. The results indicate that device temperatures reached 80 °C. This work will contribute to developing energy-efficient blockchain-based IoT sensor networks.
... Therefore, it is challenging to create new, lightweight algorithms or procedures for IoT devices without first weighing the advantages and disadvantages of current teaching techniques [148]. ...
Citation: Mazhar, T.; Talpur, D.B.; Shloul, T.A.; Ghadi, Y.Y.; Haq, I.; Ullah, I.; Ouahada, K.; Hamam, H.
... Therefore, it is challenging to create new, lightweight algorithms or procedures for IoT devices without first weighing the advantages and disadvantages of current teaching techniques [148]. ...
The Internet of Things (IoT) is a well-known technology that has a significant impact on
many areas, including connections, work, healthcare, and the economy. IoT has the potential to
improve life in a variety of contexts, from smart cities to classrooms, by automating tasks, increasing
output, and decreasing anxiety. Cyberattacks and threats, on the other hand, have a significant impact
on intelligent IoT applications. Many traditional techniques for protecting the IoT are now ineffective
due to new dangers and vulnerabilities. To keep their security procedures, IoT systems of the future
will need AI-efficient machine learning and deep learning. The capabilities of artificial intelligence,
particularly machine and deep learning solutions, must be used if the next-generation IoT system is to
have a continuously changing and up-to-date security system. IoT security intelligence is examined
in this paper from every angle available. An innovative method for protecting IoT devices against a
variety of cyberattacks is to use machine learning and deep learning to gain information from raw
data. Finally, we discuss relevant research issues and potential next steps considering our findings.
This article examines how machine learning and deep learning can be used to detect attack patterns
in unstructured data and safeguard IoT devices. We discuss the challenges that researchers face, as
well as potential future directions for this research area, considering these findings. Anyone with an
interest in the IoT or cybersecurity can use this website’s content as a technical resource and reference.
