A Hybrid Genetic Algorithm for Sustainable Wireless Coverage of Drone Networks

Recent years have witnessed increasingly more uses of drone networks for providing wireless coverage to ground users. Each drone is constrained in its energy storage and wireless coverage, and it consumes most energy when flying to the top of the target area, leaving limited leftover energy for hovering at its deployed position and providing wireless coverage. The literature largely overlooks this sustainability issue of drones' energy consumption during deployment, and we aim to minimize the maximum energy consumption among all drones after their deployment. This min-max drone deployment problem solving requires drones to cooperate with each other in deployment distance and altitude to evenly use up their energy, which is shown to be NP-hard. Thus, we propose a hybrid genetic algorithm to solve the min-max drone deployment problem. In our proposal, the integer code scheme is used to encode the sequence of drones' deployment. The energy consumption determined by the horizontal and vertical flying distance is adopted as the fitness value. With the determined order of the drones sequence by coding process, we introduce a feasibility checking operator with binary search to archive the optimum. Experimental study shows that the algorithm has capability and superiority to find good solutions under different drones' characteristics distribution and outperforms solutions from existing competitors by extensive simulations.