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MEC-Based Jamming-Aided Anti-Eavesdropping with Deep Reinforcement Learning for WBANs

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Published:06 December 2021Publication History
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Abstract

Wireless body area network (WBAN) suffers secure challenges, especially the eavesdropping attack, due to constraint resources. In this article, deep reinforcement learning (DRL) and mobile edge computing (MEC) technology are adopted to formulate a DRL-MEC-based jamming-aided anti-eavesdropping (DMEC-JAE) scheme to resist the eavesdropping attack without considering the channel state information. In this scheme, a MEC sensor is chosen to send artificial jamming signals to improve the secrecy rate of the system. Power control technique is utilized to optimize the transmission power of both the source sensor and the MEC sensor to save energy. The remaining energy of the MEC sensor is concerned to ensure routine data transmission and jamming signal transmission. Additionally, the DMEC-JAE scheme integrates with transfer learning for a higher learning rate. The performance bounds of the scheme concerning the secrecy rate, energy consumption, and the utility are evaluated. Simulation results show that the DMEC-JAE scheme can approach the performance bounds with high learning speed, which outperforms the benchmark schemes.

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            • Published in

              cover image ACM Transactions on Internet Technology
              ACM Transactions on Internet Technology  Volume 22, Issue 3
              August 2022
              631 pages
              ISSN:1533-5399
              EISSN:1557-6051
              DOI:10.1145/3498359
              • Editor:
              • Ling Liu
              Issue’s Table of Contents

              Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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              Association for Computing Machinery

              New York, NY, United States

              Publication History

              • Published: 6 December 2021
              • Accepted: 1 February 2021
              • Revised: 1 November 2020
              • Received: 1 October 2020
              Published in toit Volume 22, Issue 3

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