Energy and Latency-Aware Resource Management for UAV-Assisted Mobile Edge Computing Against Jamming

Abstract

Unmanned aerial vehicles (UAVs) have been increasingly employed as aerial servers in mobile edge computing (MEC) systems, providing essential computing, communication, and storage services for edge users. This UAV-assisted MEC paradigm shows great promise in enhancing both computing and communication performances. However, the presence of malicious jammers poses significant challenges to the system's reliability and efficiency. In this study, we explore the resource management problem in a multi-UAV-assisted MEC scenario under the influence of multiple malicious jammers. To mitigate the impact of jamming attacks, we propose a resource management approach with the primary objective of minimizing system energy consumption and latency while adhering to UAV energy constraints. Due to the dynamic and time-varying nature of the communication environment, we present a deep reinforcement learning (DRL)-based algorithm that dynamically adjusts the CPU frequency and communication bandwidth of the UAV to optimize the system performance even under jamming attacks. Through simulations, we demonstrate the effectiveness of the proposed algorithm in significantly reducing the overall system latency (both computational and communication latency) as well as minimizing energy consumption.