An Enhanced Particle Swarm Approach for UAV Data Collection Service in Disaster Environment

Abstract

In the scenario of Unmanned Aerial Vehicle -Wireless Sensor Network(UAV-WSN) collaborative assisted communication, traditional research focuses on the acquisition rate and energy consumption optimization of UAVs, without considering the underlying wireless sensor network. In the air-ground collaborative disaster relief scenario proposed in this article, a UAV path planning framework based on binary particle swarm algorithm(BPSO) is proposed to solve the UAV trajectory planning problem in UAV-assisted wireless sensor networks. The framework proposed in this paper realizes path planning for the dynamic allocation of data transmission time and acquisition energy consumption between collection nodes and UAVs. The time of data transmission and the energy consumption of collection nodes for UAV are the constraints. Considering the data migration of the underlying network(WSN), aiming at maximizing the value of data utilization. The simulation experiments validated the good performance in terms of integrity and efficiency. Compared with the algorithm that does not consider the underlying network data migration, the algorithm proposed in this paper can maximize the energy utilization efficiency of the UAV. In this paper, the data migration of the underlying network is considered, and a node is completely collected, which ensures the integrity of the data, and the performance is 55.19% better than the algorithm without considering the integrity. At the same time, as for collection efficiency, the performance of the framework is 38.04% better than the greedy algorithm, 18.55% better than the OR-Tools path planning algorithm proposed by Google, and 48.07% better than the straight algorithm. It has important theoretical significance and application value.