Secure Task Offloading in Blockchain-Enabled Mobile Edge Computing With Deep Reinforcement Learning

【Author】 Samy, Ahmed; Elgendy, Ibrahim A.; Yu, Haining; Zhang, Weizhe; Zhang, Hongli

【Source】IEEE TRANSACTIONS ON NETWORK AND SERVICE MANAGEMENT

【影响因子】4.758

【Abstract】Mobile Edge Computing (MEC) is a promising and fast-developing paradigm that provides cloud services at the edge of the network. MEC enables IoT devices to offload and execute their real-time applications at the proximity of these devices with low latency. Such applications include efficient manufacture inspection, virtual/augmented reality, image recognition, Internet of Vehicles (IoV), and e-Health. However, task offloading experiences security and privacy attacks such as data tampering, private data leakage, data replication, etc. To this end, in this paper, we propose a new blockchain-based framework for secure task offloading in MEC systems with guaranteed performance in terms of execution delay and energy consumption. First, blockchain technology is introduced as a platform to achieve data confidentiality, integrity, authentication, and privacy of task offloading in MEC. Second, we formulate an integration model of resource allocation and task offloading for a multi-user with multi-task MEC systems to optimize the energy and time cost. This is an NP-hard problem because of the curse-of-dimensionality and dynamic characteristics challenges of the considered scenario. Therefore, a deep reinforcement learning-based algorithm is developed to derive the close-optimal task offloading decision efficiently. Theoretical analysis and experimental results demonstrate that the proposed framework is resilient to several task offloading security attacks and it can save about 22.2% and 19.4% of system consumption with respect to the local and edge execution scenarios. Moreover, the benchmark analysis proves that the framework consumes few resources in terms of memory and disk usage, CPU utilization, and transaction throughput.

【Keywords】Task analysis; Blockchains; Internet of Things; Peer-to-peer computing; Security; Hafnium; Privacy; Blockchain; mobile edge computing; task offloading; security; privacy; deep reinforcement learning

【发表时间】2022 DEC

【收录时间】2023-05-14

【文献类型】理论模型

【主题类别】

区块链技术-协同技术-边缘计算