Dynamic Weighted Energy Minimization for Aerial Edge Computing Networks

In this paper, we develop a dynamic weighting strategy which considers the residual energy of different devices in aerial edge computing networks, and formulate a weighted energy consumption optimization problem aimed at extending device operating duration. To solve the formulated problem, we develop a clustering algorithm using K-means++ to establish optimal user-to-unmanned aerial vehicle access relationships, and the optimization problem is decomposed into trajectory, transmit power, and bandwidth sub-problems. Each sub-problem is sequentially solved by using the successive convex approximation algorithm, and the entire optimization problem is resolved by using the block coordinate descent algorithm. Simulation results demonstrate the effectiveness of our proposed weighting strategy in managing the energy levels of users, which prolongs the operational duration of the devices.