UAV-Edge Cloud collaboration for online offloading and trajectory control in multi-layer Mobile Edge Computing

Abstract

Integrating Mobile Edge Computing (MEC) with Unmanned Aerial Vehicles (UAVs) offers enhanced coverage and computational support for mobile IoT devices (MIDs). Due to the inherent computational capacity and energy constraints of UAV, existing UAV-assisted MEC systems struggle to satisfy computation-intensive network services for numerous MIDs. To address this issue, this paper proposes a UAV and ground-based Edge Cloud (EC) collaboration MEC system. Specifically, the EC is equipped with an energy transmitter to provide wireless power transfer (WPT) to the UAV, thereby collaboratively managing backlog tasks in scenarios where task and energy arrive stochastically. To achieve optimal service delivery, we formulate a long-term stochastic optimization problem aiming to jointly optimize UAV energy consumption and system throughput while ensuring task queue stability. However, this NP-hard problem posed by the stochastic nature of task arrivals and energy constraints, we develop an online offloading and trajectory control (OOTC) algorithm. This algorithm uses Lyapunov optimization theory to transform the long-term stochastic optimization problem into a deterministic per-slot optimization problem. The OOTC algorithm decouples control decisions across consecutive time slots, reducing computational complexity and ensuring queue stability without relying on statistical knowledge. We further decompose it into three subproblems, and derive suboptimal solutions by the Successive Convex Approximation (SCA) and the Lagrangian duality. Simulations show OOTC algorithm outperforms benchmarks and maintains stability.