Multi-UAV Cooperative Task Offloading in Blockchain-Enabled MEC for Consumer Electronics

The development of the Internet of Things (IoT) has made the traditional Consumer Electronics (CE) evolve into a next-generation CE with many intelligent application which generates a large number of computing-intensive tasks and is sensitive to latency. The combination of blockchain technology and multi-UAV-assisted edge computing is considered a promising technology that can process tasks near CE devices instead of sending data to remote data centers, and it has advantages in flexible deployment, differentiated services, and security assurance. This paper considers a Blockchain-enabled MEC scenario where CE devices can offload tasks to multiple-UAV-edge layers for decentralized cooperative computing, and utilize the reputation mechanism of blockchain to identify abnormal nodes in edge offloading. To solve the problem of computation offloading and resource allocation in this scenario, we jointly optimize the CPU cycle frequencies and offloading resources of UAVs and CE devices, and the decision of UAVs. Our goal is to minimize system energy consumption while ensuring the quality of service. The stochastic optimization technique is employed to transform the original stochastic optimization problem into a deterministic optimization problem, decomposing it into multiple sub-problems which can be solved in parallel. Additionally, we design a dynamic priority scoring strategy to facilitate cooperative processing among multiple UAVs and propose a Blockchain-Based Multi-UAV Cooperative Task Offloading (BMCTO) algorithm. Through theoretical analysis and simulation experiments, we demonstrate that the BMCTO algorithm can effectively reduce system energy consumption while maintaining system performance.

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Publication Details

Journal
IEEE Transactions on Consumer Electronics
Published
2024-10-24
DOI
https://doi.org/10.1109/tce.2024.3485633
Citations
5
Primary Topic
IoT and Edge/Fog Computing
Type
article
Field-Weighted Citation Impact
1.74

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article

Multi-UAV Cooperative Task Offloading in Blockchain-Enabled MEC for Consumer Electronics

Lianyong Qi, Xiaokang Zhou, Jiwei Huang, Yaozong Yang et al.
5 citations
IEEE Transactions on Consumer Electronics
IoT and Edge/Fog Computing
1.74
article

Multi-UAV Cooperative Task Offloading in Blockchain-Enabled MEC for Consumer Electronics

Lianyong Qi, Xiaokang Zhou, Jiwei Huang, Yaozong Yang, Ying Chen, Jinfeng Jing
article en
5 citations

Abstract

The development of the Internet of Things (IoT) has made the traditional Consumer Electronics (CE) evolve into a next-generation CE with many intelligent application which generates a large number of computing-intensive tasks and is sensitive to latency. The combination of blockchain technology and multi-UAV-assisted edge computing is considered a promising technology that can process tasks near CE devices instead of sending data to remote data centers, and it has advantages in flexible deployment, differentiated services, and security assurance. This paper considers a Blockchain-enabled MEC scenario where CE devices can offload tasks to multiple-UAV-edge layers for decentralized cooperative computing, and utilize the reputation mechanism of blockchain to identify abnormal nodes in edge offloading. To solve the problem of computation offloading and resource allocation in this scenario, we jointly optimize the CPU cycle frequencies and offloading resources of UAVs and CE devices, and the decision of UAVs. Our goal is to minimize system energy consumption while ensuring the quality of service. The stochastic optimization technique is employed to transform the original stochastic optimization problem into a deterministic optimization problem, decomposing it into multiple sub-problems which can be solved in parallel. Additionally, we design a dynamic priority scoring strategy to facilitate cooperative processing among multiple UAVs and propose a Blockchain-Based Multi-UAV Cooperative Task Offloading (BMCTO) algorithm. Through theoretical analysis and simulation experiments, we demonstrate that the BMCTO algorithm can effectively reduce system energy consumption while maintaining system performance.

IEEE Transactions on Consumer ElectronicsVol. 71(1)
China University of Petroleum, Beijing (CN), China University of Petroleum, East China (CN), Kansai University (JP), Beijing Information Science & Technology University (CN)
National Natural Science Foundation of China, Natural Science Foundation of Beijing Municipality
Openalex Percentile: Top 14%
IoT and Edge/Fog Computing
1.74
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