research-article

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

Authors:

Mohammad Shorfuzzaman,

Yuanhang QiAuthors Info & Claims

ACM Transactions on Internet Technology (TOIT), Volume 22, Issue 3

Article No.: 60, Pages 1 - 17

https://doi.org/10.1145/3453186

Published: 06 December 2021 Publication History

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

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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
Georgia Institute of Technology, USA

Issue’s Table of Contents

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Publication History

Published: 06 December 2021

Accepted: 01 February 2021

Revised: 01 November 2020

Received: 01 October 2020

Published in TOIT Volume 22, Issue 3

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Funding Sources

Science and Technology Plan Project of Guangzhou City
Guangdong Special Project in Key Field of Artificial Intelligence for Ordinary University
Guangzhou Yuexiu District Science and Technology Plan Major Project
Taif Univer-sity Researchers Supporting Project

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https://dl.acm.org/doi/10.1145/3652029
Qu SZhao SLi BHe YCai XZhang LWang YTsafrir DMusuvathi MGupta RAbu-Ghazaleh N(2024)CIM-MLC: A Multi-level Compilation Stack for Computing-In-Memory AcceleratorsProceedings of the 29th ACM International Conference on Architectural Support for Programming Languages and Operating Systems, Volume 210.1145/3620665.3640359(185-200)Online publication date: 27-Apr-2024
https://dl.acm.org/doi/10.1145/3620665.3640359
Sharma GMallik RPandey NSingh A(2024)Effect of Interfering Transmitter on the Secrecy of Diffusive Molecular Timing ChannelsIEEE Transactions on Communications10.1109/TCOMM.2024.336562972:6(3323-3337)Online publication date: Jun-2024
https://doi.org/10.1109/TCOMM.2024.3365629
Shafee AAwaad TMoro A(2024)A Survey of Edge Computing Privacy and Security Threats and Their Countermeasures2024 IEEE Computer Society Annual Symposium on VLSI (ISVLSI)10.1109/ISVLSI61997.2024.00093(484-489)Online publication date: 1-Jul-2024
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