Evolutionary Optimization of Drone-Swarm Deployment for Wireless Coverage

The need for longer lasting and wider wireless coverage has driven the transition from a single drone to drone swarms. Unlike the single drone, drone swarms can collaboratively achieve full coverage over a target area. However, the existing literature on the drones' wireless coverage has largely overlooked one important fact: that the network lifetime is determined by the minimum leftover energy among all drones. Hence, the maximum energy consumption is minimized in our drone-swarms deployment problem (DSDP), which aims to balance the energy consumption of all drones and maximize the full-coverage network lifetime. We present a genetic algorithm that encodes the solutions as chromosomes and simulates the biological evolution process in search of a favorable solution. Specifically, an integer code scheme is adopted to encode the sequence of the drones' deployment. With the order of the drones' sequence determined by the coding process, we introduce a feasibility checking operator with binary search to improve the performance. By relaxing the constraint of full coverage as an objective of coverage rate, we study the tradeoffs between energy consumption, number of drones, and coverage rate of the target area. By taking advantage of the MOEA/D framework with neighboring subproblems searching, we present a drone-swarms deployment algorithm based on MOEA/D (DSDA-MOEA/D) to find the best tradeoff between these objectives. Extensive simulations were conducted to evaluate the performance of the proposed algorithms.