Adaptive swarm intelligence optimization for Unmanned Aerial Vehicle-assisted edge computing

Abstract

The integration of Unmanned Aerial Vehicles (UAVs) into Mobile Edge Computing (MEC) systems presents a promising paradigm for delivering low-latency, on-demand computational services in dynamic and infrastructure-scarce environments. However, achieving efficient and reliable UAV deployment poses a significant optimization challenge. This challenge necessitates the simultaneous maximization of ground user coverage and robust inter-UAV connectivity, while reducing redundant overlap, which directly minimizes energy consumption and improves overall network efficiency under dynamic operational constraints. To address this complex multi-objective problem, this paper proposes an innovative swarm intelligence-driven optimization framework. The core of this framework is a Gradient-Based Optimization (GBO) algorithm specifically designed for UAV deployment. This algorithm uniquely integrates global exploration capabilities with local refinement mechanisms to navigate the intricate solution space effectively. Furthermore, we introduce a temporal graph modeling approach to capture and predict dynamic UAV–user interactions, enabling adaptive, real-time UAV repositioning in response to changing environmental conditions and user demands. Extensive simulation-based evaluations, conducted within a realistic simulated disaster-affected geographic area, validate the efficacy of our proposed framework. The GBO-driven approach achieves high operational performance: exceeding 85% user coverage, and significantly reducing coverage overlap by 43.7%, with superior convergence characteristics, reaching 95% of its total fitness improvement within just two iterations, indicating extremely fast early-stage convergence. It provides a scalable, adaptive service computing framework for time-critical, resource-constrained UAV-assisted MEC systems.