Toward Secure and Scalable Vehicular Edge Computing With Zero-Energy RIS Using DRL

DRL; edge computing; intelligent surfaces; security; Vehicular communication; Automotive technology; Computational capability; Edge computing; Efficiency factor; Intelligent surface; Reconfigurable; Reinforcement learnings; Security; Vehicular communications; Zero energies; Computer Science (all); Materials Science (all); Engineering (all)

Abstract :

[en] The escalating demands of advanced automotive technologies exert considerable pressure on modern vehicles' computational capabilities. This scenario underscores the critical need for vehicular edge computing (VEC) networks, which leverage 5G/6G communications to facilitate computational offloading. However, providing seamless access to these services while simultaneously adhering to stringent latency and security requirements presents a formidable challenge. The advent of reconfigurable intelligent surfaces (RIS) heralds a new era of possibilities, which includes enhancing connectivity, boosting data transmission rates, consequently reducing delay, and improving the physical layer security of communication channels. This paper delves into the utilization of zero-energy RIS (ze-RIS) in the context of vehicular computation offloading. Our primary goal is to ensure secure access while optimizing operational efficiency in compliance with various task-related and environmental requirements. The ze-RIS-assisted secure task efficient offloading (DRSTO) scheme is a novel deep reinforcement learning (DRL) framework that cleverly switches communication connections to optimize task offloading efficiency and security thereby resolving this issue. At its core, our assessment strategy revolves around the DRSTO model's secrecy and efficiency factor which serve as both a performance measure and a reward function. Time efficiency and rate of confidentiality are used to evaluate this aspect which provides a thorough evaluation of the scheme's success. Extensive testing and comparison have shown that the DRSTO scheme's efficiency factor can be significantly increased, from 6.05 to 18.10. In addition, the rate of job success has increased dramatically, from 2.12% to 4.63%. When compared to other models that were evaluated, the DRSTO scheme consistently better on several metrics including reward, time frames per step (TFPS) ratio and DRL characteristics.

Disciplines :

Computer science

Author, co-author :

Wahid, Abdul ; Qingdao University, College of Computer Science and Technology, Qingdao, China

Ayzed Mirza, Muhammad ; Qilu Institute of Technology, School of Computer Science and Information Engineering, Jinan, China

Ahmed, Manzoor ; Hubei Engineering University, School of Computer and Information Science, Institute for AI Industrial Technology Research, Xiaogan, China

Sheraz, Muhammad ; Multimedia University, Centre for Wireless Technology, Faculty of Engineering, Cyberjaya, Malaysia

Chuah, Teong Chee ; Multimedia University, Centre for Wireless Technology, Faculty of Engineering, Cyberjaya, Malaysia

Ee Lee, It; Multimedia University, Centre for Wireless Technology, Faculty of Engineering, Cyberjaya, Malaysia

KHAN, Wali Ullah ; University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > SigCom

External co-authors :

yes

Language :

English

Title :

Toward Secure and Scalable Vehicular Edge Computing With Zero-Energy RIS Using DRL

Original title :

[en] Toward Secure and Scalable Vehicular Edge Computing With Zero-Energy RIS Using DRL

Publication date :

11 September 2024

Journal title :

IEEE Access

ISSN :

2169-3536

Publisher :

Institute of Electrical and Electronics Engineers Inc.

Volume :

Pages :

129330 - 129346

Peer reviewed :

Peer Reviewed verified by ORBi

Focus Area :

Security, Reliability and Trust

Additional URL :

http://xplorestaging.ieee.org/ielx8/6287639/10380310/10676973.pdf?arnumber=10676973

Funders :

Multimedia University Research Fellow

Funding text :

This work was supported by the Multimedia University Research Fellow under Grant MMUI/240021.This work was supported by Multimedia University Research Fellow under Grant MMUI/230013

Data Set :

https://ieeexplore.ieee.org/abstract/document/10676973

Available on ORBilu :

since 11 December 2024

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