A Deployment Strategy for UAV-Aided Data Collection in Unknown Environments

In recent years, unmanned aerial vehicles (UAVs) have been widely applied in traffic offloading and data collection. Due to the advantages in terms of mobility and flexibility, we investigate the data collection scheme for a wireless sensor network (WSN) with randomly distributed ground sensors. In this article, an efficient UAV-aided data collection scheme for large-scale WSN is proposed, and a group of UAVs are deployed to provide service to ground sensors with unknown positions. The goal is to maximize the data transmission rate of the sensor network (SN) by optimizing the coverage area of UAVs and the association of ground sensors with UAVs. To solve the optimization problem, we first introduce a concept termed distributed coverage area (DCA) based on the Reuleaux triangle (RT). Then, an attractive mechanism is designed using Roman domination for UAVs to select an appropriate attractive source. The mechanism can ensure that the UAVs will prioritize providing service above the vertices with the denser distribution of ground sensors, while the remaining UAVs are deployed along the edges of the SN for a more comprehensive coverage. Finally, an ordered improved particle swarm optimization (IPSO) deployment algorithm is proposed to search the random locations of ground sensors and optimize the specific positions of UAVs. The simulation results show the superiority of the proposed scheme, and the coverage performance for data collection is committed.