Concurrent Practical Byzantine Fault Tolerance for Integration of Blockchain and Supply Chain

Xu, Xiaolong; Zhu, Dawei; Yang, Xiaoxian; Wang, Shuo; Qi, Lianyong; Dou, Wanchun

doi:10.1145/3395331

Cited by 71 publications

(34 citation statements)

References 29 publications

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“…Several more examples of such studies are presented in [36,37]. In [38], it is proposed to use the concurrent PBFT consensus mechanism to solve the problem of the fast node expansion in the supply chain. At the same time, the authors propose to classify peers in the supply chain into several clusters using transaction analysis.…”

Section: E Practical Byzantine Fault Tolerancementioning

confidence: 99%

On Performance of PBFT Blockchain Consensus Algorithm for IoT-Applications With Constrained Devices

et al. 2021

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Cyber-physical systems and the Internet of things (IoT) are becoming an integral part of the digital society. The use of IoT services improves human life in many ways. Protection against cyber threats is an utmost important prospect of IoT devices operation. Malicious activities lead to confidential data leakage and incorrect performance of devices becomes critical. Therefore, development of effective solutions that can protect both IoT devices data and data exchange networks turns in to a real challenge. This study provides a critical analysis of the feasibility of using blockchain technology to protect constrained IoT devices data, justifies the choice of Practical Byzantine Fault Tolerance (PBFT) consensus algorithm for implementation on such devices, and simulates the main distributed ledger scenarios using PBFT. In this paper we investigate typical IoT network scenarios that can disrupt system performance. To ensure the adequacy of the models under study, we have analyzed the characteristics of real constrained IoT devices in terms of computing power and data rate. The simulation results demonstrate efficiency of the blockchain technology for constrained devices and make it possible to evaluate applicability limits of the chosen consensus algorithm.

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Section: E Practical Byzantine Fault Tolerancementioning

confidence: 99%

On Performance of PBFT Blockchain Consensus Algorithm for IoT-Applications With Constrained Devices

et al. 2021

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“…Sometimes, the hackers send a large number of instructions and data to the terminal of the mobile device, which makes the communication network appear to be blocked negatively. Due to data transmission via the wireless network, the hackers analyze the frequency so that they can complete wiretapping work [42]. In severe cases, communication data will be tampered by hackers, causing a negative phenomenon of data loss.…”

Section: Secure Risk Of Data Transmissionmentioning

confidence: 99%

A Survey on Secure Deployment of Mobile Services in Edge Computing

Cui

Fei

Yin³

2021

Security and Communication Networks

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Mobile edge computing (MEC) is an emerging technology that is recognized as a key to 5G networks. Because MEC provides an IT service environment and cloud-computing services at the edge of the mobile network, researchers hope to use MEC for secure service deployment, such as Internet of vehicles, Internet of Things (IoT), and autonomous vehicles. Because of the characteristics of MEC which do not have terminal servers, it tends to be deployed on the edge of networks. However, there are few related works that systematically introduce the deployment of MEC. Also, secure service deployment frameworks with MEC are even rare. For this reason, we have conducted a comprehensive and concrete survey of recent research studies on secure deployment. Although numerous research studies and experiments about MEC service deployment have been conducted, there are few systematic summaries that conclude basic concepts and development strategies about secure service deployment of commercial MEC. To make up for the gap, a detailed and complete survey about relative achievements is presented.

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“…Yu et al [28] considered that when multiple mobile users offload repetitive computing tasks to the edge of the network, they developed a cache-enhanced service offloading strategy based on sharing computing results to reduce the delay in task processing. As the subject of machine learning becomes more and more popular, researchers have also begun to study how to apply machine learning to edge IoT systems [29][30][31]. Liu et al [28] grouped users according to priority.…”

Section: Related Workmentioning

confidence: 99%

Edge Server Placement for Service Offloading in Internet of Things

2021

Security and Communication Networks

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With the rapid development of the Internet of Things, a large number of smart devices are being connected to the Internet while the data generated by these devices have put unprecedented pressure on existing network bandwidth and service operations. Edge computing, as a new paradigm, places servers at the edge of the network, effectively relieving bandwidth pressure and reducing delay caused by long-distance transmission. However, considering the high cost of deploying edge servers, as well as the waste of resources caused by the placement of idle servers or the degradation of service quality caused by resource conflicts, the placement strategy of edge servers has become a research hot spot. To solve this problem, an edge server placement method orienting service offloading in IoT called EPMOSO is proposed. In this method, Genetic Algorithm and Particle Swarm Optimization are combined to obtain a set of edge server placements strategies, and Simple Additive Weighting Method is utilized to determine the most balanced edge server placement, which is measured by minimum delay and energy consumption while achieving the load balance of edge servers. Multiple experiments are carried out, and results show that EPMOSO fulfills the multiobjective optimization with an acceptable convergence speed.

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Concurrent Practical Byzantine Fault Tolerance for Integration of Blockchain and Supply Chain

Cited by 71 publications

References 29 publications

On Performance of PBFT Blockchain Consensus Algorithm for IoT-Applications With Constrained Devices

On Performance of PBFT Blockchain Consensus Algorithm for IoT-Applications With Constrained Devices

A Survey on Secure Deployment of Mobile Services in Edge Computing

Edge Server Placement for Service Offloading in Internet of Things

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