Charging Station Placement in Unmanned Aerial Vehicle Aided Opportunistic Networks

Abstract

Unmanned aerial vehicles (UAVs) are widely used in many application areas within opportunistic networks. In this paper, we investigate the charging station placement problem in the application scenario with ten UAVs deployed in an opportunistic network environment. We have used a real-world dataset that contains human mobility traces from North Carolina State University. The UAVs cruise on the network with spiral shapes and distribute messages to the nodes on the ground. The charging station locations are generated with random, Density-based spatial clustering of applications with noise (DBSCAN) and k-means clustering approaches. The evaluation results indicate that the k-means algorithm with three clusters outperformed the other two methods in terms of the success rates and the message delay.