Enhancing Secrecy Energy Efficiency in RIS-Aided Aerial Mobile Edge Computing Networks: A Deep Reinforcement Learning Approach

2025/05/16 by Aly Sabri Abdalla, Abdalla, Aly Sabri, Vuk Marojevic +1
Computer Science · Engineering · #Advanced Wireless Communication Technologies #Cryptography and Security (cs.CR) #Distributed #FOS: Computer and information sciences #FOS: Electrical engineering #IoT and Edge/Fog Computing #Parallel #Systems and Control (eess.SY) #UAV Applications and Optimization #and Cluster Computing (cs.DC) #electronic engineering #information engineering

paper · pdf · doi:10.48550/arxiv.2505.10815

openalex publication_date 2025/05/16 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

This paper studies the problem of securing task offloading transmissions from ground users against ground eavesdropping threats. Our study introduces a reconfigurable intelligent surface (RIS)-aided unmanned aerial vehicle (UAV)-mobile edge computing (MEC) scheme to enhance the secure task offloading while minimizing the energy consumption of the UAV subject to task completion constraints. Leveraging a data-driven approach, we propose a comprehensive optimization strategy that jointly optimizes the aerial MEC (AMEC)'s trajectory, task offloading partitioning, UE transmission scheduling, and RIS phase shifts. Our objective centers on optimizing the secrecy energy efficiency (SEE) of UE task offloading transmissions while preserving the AMEC's energy resources and meeting the task completion time requirements. Numerical results show that the proposed solution can effectively safeguard legitimate task offloading transmissions while preserving AMEC energy.

Citations

• DDPG Learning for Aerial RIS-Assisted MU-MISO Communications
• HyAR: Addressing Discrete-Continuous Action Reinforcement Learning via Hybrid Action Representation
• Reconfigurable Intelligent Surface Aided Mobile Edge Computing: From Optimization-Based to Location-Only Learning-Based Solutions
• Intelligent Reflecting Surface Enhanced Wireless Network via Joint Active and Passive Beamforming
• Energy Minimization for Wireless Communication with Rotary-Wing UAV

Related

• Secure Offloading in NOMA-Aided Aerial MEC Systems Based on Deep Reinforcement Learning
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• Energy Efficient Task Offloading in UAV-Enabled MEC Using a Fully Decentralized Deep Reinforcement Learning Approach
• Securing the Skies: An IRS-Assisted AoI-Aware Secure Multi-UAV System with Efficient Task Offloading