Queue-aware computation offloading for UAV-assisted edge computing in wind farm routine inspection

Abstract

Integration of unmanned aerial vehicles (UAVs) and edge computing into the wind farm routine inspection provides a promising approach to enhancing inspection effectiveness and decreasing operation maintenance costs. In light of the finite battery power and computational capacity of UAVs, a dynamic queue-aware UAV-assisted edge computing inspection wind farm framework is investigated with the goal of minimizing the long-term energy consumption of UAVs. The Lyapunov optimization theory is utilized to decouple the long-term stochastic optimization problem into four short-term deterministic subproblems, including the task splitting, the UAV-side computing resource allocation, the task offloading, and the edge server-side computing resource allocation. Furthermore, a Lyapunov optimization-based dynamic queue-aware computation offloading algorithm (LODQCO) is presented to optimize task offloading and resource allocation jointly. The optimal UAV-side computing resource is determined by a closed form formula, and then the optimal task offloading decision is tackled by applying the classical interior point method. Finally, the edge server-side computing resource is addressed via a linear optimization CPLEX solver. Based on simulation results, LODQCO is superior to the benchmark algorithms with respect to the energy consumption, queue backlogs, and queuing delays.