Optimizing energy efficiency in mobile edge computing: Leveraging latency-aware offloading, clustering, and UAV placement strategies

Abstract

In the context of next-generation 5G and beyond communication networks, integrating Unmanned Aerial Vehicles (UAVs) with Mobile Edge Computing (MEC) is crucial. Hybrid Non-orthogonal Multiple Access (H-NOMA) has been recognized as a prominent technique for reducing energy consumption during data offloading. However, the literature assumes that all users in the cluster have latency requirements and interference levels such that implementing H-NOMA is optimal, overlooking other scenarios. Furthermore, the position of UAV-hosted MEC is not optimized. To address these constraints, we propose an adaptive offloading method where users can utilize either H-NOMA or OMA for data offloading in designated time slots based on their conditions. We substantiate this proposal through a comparative analysis of energy consumption between H-NOMA and OMA. Additionally, we introduce a novel Maximum Latency Difference Clustering and Power Allocation (MLDC & PA) algorithm for organizing smart terminals (STs) and allocating power. Furthermore, we propose a heuristic-based optimization approach for UAV positioning to minimize offloading energy and enhance network efficiency. Simulation results confirm that the proposed approach has superior energy consumption reduction capabilities compared to state-of-the-art techniques.