Editorial: Blockchain in Industrial IoT Applications: Security and Privacy Advances, Challenges, and Opportunities

Choo, Kim‐Kwang Raymond; Yan, Zheng; Meng, Weizhi

doi:10.1109/tii.2020.2966068

Cited by 59 publications

(24 citation statements)

References 13 publications

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“…A blockchain is essentially a distributed and secure ledger that records all the transactions into a hierarchically expanding chain of blocks [42], [43], [44], [45], [46]. Each block in the blockchain is linked to its previous block through the hash value of the parent block, except for the first block, usually called the genesis block which does not have a parent block.…”

Section: A Blockchainmentioning

confidence: 99%

Convergence of Blockchain and Edge Computing for Secure and Scalable IIoT Critical Infrastructures in Industry 4.0

Dai

Wang

2021

IEEE Internet Things J.

213

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Critical infrastructure systems are vital to underpin the functioning of a society and economy. Due to ever-increasing number of Internet-connected Internet-of-Things (IoTs) / Industrial IoT (IIoT), and high volume of data generated and collected, security and scalability are becoming burning concerns for critical infrastructures in industry 4.0. The blockchain technology is essentially a distributed and secure ledger that records all the transactions into a hierarchically expanding chain of blocks. Edge computing brings the cloud capabilities closer to the computation tasks. The convergence of blockchain and edge computing paradigms can overcome the existing security and scalability issues. In this paper, we first introduce the IoT/IIoT critical infrastructure in industry 4.0, and then we briefly present the blockchain and edge computing paradigms. After that, we show how the convergence of these two paradigms can enable secure and scalable critical infrastructures. Then, we provide a survey on state-of-the-art for security and privacy, and scalability of IoT/IIoT critical infrastructures. A list of potential research challenges and open issues in this area is also provided, which can be used as useful resources to guide future research.

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Section: A Blockchainmentioning

confidence: 99%

Convergence of Blockchain and Edge Computing for Secure and Scalable IIoT Critical Infrastructures in Industry 4.0

Dai

Wang

2021

IEEE Internet Things J.

213

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“…Federated learning has been proposed to address this issue but more attempts are expected in the future. • Blockchain-Assisted CC and EC: Blockchain has attracted much attention from both academia and industry [182], [183]. It is also interesting to design the blockchain-based networks for CC and EC.…”

Section: B Future Research Trends 1) From CC and Ec Perspectivementioning

confidence: 99%

A Review on Computational Intelligence Techniques in Cloud and Edge Computing

Asim

Wang

et al. 2020

IEEE Trans. Emerg. Top. Comput. Intell.

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Cloud computing (CC) is a centralized computing paradigm that accumulates resources centrally and provides these resources to users through Internet. Although CC holds a large number of resources, it may not be acceptable by real-time mobile applications, as it is usually far away from users geographically. On the other hand, edge computing (EC), which distributes resources to the network edge, enjoys increasing popularity in the applications with low-latency and high-reliability requirements. EC provides resources in a decentralized manner, which can respond to users' requirements faster than the normal CC, but with limited computing capacities. As both CC and EC are resource-sensitive, several big issues arise, such as how to conduct job scheduling, resource allocation, and task offloading, which significantly influence the performance of the whole system. To tackle these issues, many optimization problems have been formulated. These optimization problems usually have complex properties, such as non-convexity and NP-hardness, which may not be addressed by the traditional convex optimization-based solutions. Computational intelligence (CI), consisting of a set of nature-inspired computational approaches, recently exhibits great potential in addressing these optimization problems in CC and EC. This paper provides an overview of research problems in CC and EC and recent progresses in addressing them with the help of CI techniques. Informative discussions and future research trends are also presented, with the aim of offering insights to the readers and motivating new research directions.

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“…Blockchain is an integrated application of distributed data storage, peer-to-peer transmission, consensus mechanism, cryptographic algorithms, and other technologies and has the advantages of decentralization, data immutability, and programmability. Since Satoshi Nakamoto first proposed Bitcoin's concept [1] in 2008, the underlying technology blockchain has been widely used in the fields of digital currency, smart manufacturing, supply chain management, anticounterfeiting data service, and so on [1][2][3][4][5][6][7]. Simultaneously, blockchain security has also received widespread attention from the academic community.…”

Section: Introductionmentioning

confidence: 99%

Bitcoin Theft Detection Based on Supervised Machine Learning Algorithms

Chen

Wei

2021

Security and Communication Networks

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Since its inception, Bitcoin has been subject to numerous thefts due to its enormous economic value. Hackers steal Bitcoin wallet keys to transfer Bitcoin from compromised users, causing huge economic losses to victims. To address the security threat of Bitcoin theft, supervised learning methods were used in this study to detect and provide warnings about Bitcoin theft events. To overcome the shortcomings of the existing work, more comprehensive features of Bitcoin transaction data were extracted, the unbalanced dataset was equalized, and five supervised methods—the k-nearest neighbor (KNN), support vector machine (SVM), random forest (RF), adaptive boosting (AdaBoost), and multi-layer perceptron (MLP) techniques—as well as three unsupervised methods—the local outlier factor (LOF), one-class support vector machine (OCSVM), and Mahalanobis distance-based approach (MDB)—were used for detection. The best performer among these algorithms was the RF algorithm, which achieved recall, precision, and F1 values of 95.9%. The experimental results showed that the designed features are more effective than the currently used ones. The results of the supervised methods were significantly better than those of the unsupervised methods, and the results of the supervised methods could be further improved after equalizing the training set.

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Editorial: Blockchain in Industrial IoT Applications: Security and Privacy Advances, Challenges, and Opportunities

Cited by 59 publications

References 13 publications

Convergence of Blockchain and Edge Computing for Secure and Scalable IIoT Critical Infrastructures in Industry 4.0

Convergence of Blockchain and Edge Computing for Secure and Scalable IIoT Critical Infrastructures in Industry 4.0

A Review on Computational Intelligence Techniques in Cloud and Edge Computing

Bitcoin Theft Detection Based on Supervised Machine Learning Algorithms

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