Energy-Stabilized Computing Offloading Algorithm for UAVs With Energy Harvesting

Recent research on unmanned aerial vehicle-based (UAV) computational offloading algorithms has employed energy harvesting mechanisms to improve the efficiency of edge computing. However, these approaches treat UAVs as relays or power providers which are rarely regarded as the computational nodes in the energy harvesting condition. The most challenging issue is not only the computational efficiency, but also the energy stability of individual computing devices. Stable energy state represents a stable computing service capability, which is very important for edge systems. It depends heavily on proper computational offloading algorithms. In this study, we construct a novel model that uses a cluster of UAVs with energy harvesting capability as a computational core. It is capable of providing long-term computational services for various scenarios. Then, we construct a Lyapunov function through a designed virtual battery energy queue and prove the existence of an upper bound for the Lyapunov drift-plus-penalty function through mathematical transformations. Therefore, we obtain a theoretically stable battery energy queue and design a Lyapunov-chain offloading algorithm based on it. Simulation results show that the proposed Lyapunov-chain offloading algorithm is able to maintain the strong energy stability of each node. It also provides robustness for edge UAV clusters while minimizing the execution delay compared to the baseline offloading scheme.