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Post-Disaster Unmanned Aerial Vehicle Base Station Deployment Method Based on Artificial Bee Colony Algorithm

Ground-based communication facilities are at risk of being destroyed after natural disasters, such as earthquakes, floods, tsunamis, hurricanes, fires, or terrorist attacks. Unmanned aerial vehicles (UAV) can be used as air base stations to support user equipment (UE). UAV base station (UAV-BS) development plays a major part in rescue operations and post-disaster reconstruction. Improving network throughput in the UAV-BS signal coverage area and reducing deployment costs while maintaining effective communication are important issues that need to be addressed. In view of the problem, this paper demonstrates the problem of maximizing network throughput under the constraint of UAV-BS capacity by deploying UAV-BS. We proposed a UAV-artificial bee colony (U-ABC) algorithm to solve the problem of UAV-BS deployment. U-ABC algorithm can calculate the optimal flight position of each UAV-BS and maximum network throughput in the disaster area. In performance evaluation, we compared U-ABC algorithm with genetic algorithm, Greedy-ABC algorithm, PSO algorithm, DI-PSO algorithm and PSO-GWO algorithm. We analyzed the flight height altitude of UAV-BSs, the interference factor of UAV-BS, and the influence of the number of UE on network throughput. Results show that the proposed method improved the overall network throughput and achieved a high UE coverage rate under a given number of UAV-BSs.