Joint User Association and Deployment Optimization for Energy-Efficient Heterogeneous UAV-Enabled MEC Networks

Abstract

Unmanned aerial vehicles (UAVs) providing additional on-demand communication and computing services have become a promising technology. However, the limited energy supply of UAVs, which constrains their service duration, has emerged as an obstacle in UAV-enabled networks. In this context, a novel task offloading framework is proposed in UAV-enabled mobile edge computing (MEC) networks. Specifically, heterogeneous UAVs with different communication and computing capabilities are considered and the energy consumption of UAVs is minimized via jointly optimizing user association and UAV deployment. The optimal transport theory is introduced to analyze the user association sub-problem, and the UAV deployment for each sub-region is determined by a dragonfly algorithm (DA). Simulation results show that the energy consumption performance is significantly improved by the proposed algorithm.

PPO-Based Joint Optimization for UAV-Assisted Edge Computing Networks

“…In addition, Refs. [25][26][27] investigate the deployment of UAVs while keeping in mind the energy consumption of edge computing networks. The current research places greater emphasis on the energy consumption of the network, whereas the investigation into latency, a crucial factor for meeting user requirements, requires further development.…”

“…DRL in Edge Computing: Currently, the research into applying deep reinforcement learning to address the computation offloading and resource allocation issues in edge computing systems primarily focuses on the decision-making of computation offloading [27]. Ref.…”