- Title
- Physical-layer security based mobile edge computing for emerging cyber physical systems
- Creator
- Chen, Lunyuan; Tang, Shunpu; Balasubramanian, Venki; Xia, Junjuan; Zhou, Fasheng; Fan, Lisheng
- Date
- 2022
- Type
- Text; Journal article
- Identifier
- http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/189447
- Identifier
- vital:17438
- Identifier
-
https://doi.org/10.1016/j.comcom.2022.07.037
- Identifier
- ISSN:0140-3664 (ISSN)
- Abstract
- This paper studies a secure mobile edge computing (MEC) for emerging cyber physical systems (CPS), where there exist K eavesdroppers in the network, which can threaten the task offloading. These K eavesdroppers can work either in a colluding mode where they cooperate to decode the secret message, or in a non-colluding mode where the eavesdroppers decode the message individually. For both eavesdropping nodes, we design the secure MEC system by devising a computation offloading ratio, transmit power and computational capability allocation to optimize the system performance mainly measured by the latency. In particular, a novel deep reinforcement learning (DRL) together with convex optimization (DRCO) is proposed, where the DRL is used to find a proper solution to the offloading ratio, while the convex optimization is implemented to solve the allocation of transmission power and computational capability. Simulation results show that the proposed DRCO method is superior to other conventional methods, and can provide a guaranteed secrecy and latency. © 2022
- Publisher
- Elsevier B.V.
- Relation
- Computer Communications Vol. 194, no. (2022), p. 180-188
- Rights
- All metadata describing materials held in, or linked to, the repository is freely available under a CC0 licence
- Rights
- Copyright @ 2022 Published by Elsevier B.V.
- Subject
- 4006 Communications engineering; 4009 Electronics, sensors and digital hardware; 4606 Distributed computing and systems software; Cyber physical systems; Eavesdropping; Mobile edge computing; Secure communication
- Reviewed
- Funder
- This work was supported in part by the National Natural Science Foundation of China (Nos. 61871139 / 62101145 ), in part by the International Science and Technology Cooperation Projects of Guangdong Province (No. 2020A0505100060 ), in part by the Natural Science Foundation of Guangdong Province (No. 2021A1515011392 ), in part by the Guangdong Basic and Applied Basic Research Foundation (No. 2021A1515011812 ), and in part by the research program of Guangzhou University (Nos. YK2020008 / YJ2021003 ).
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