UAV Charging Station Placement in Opportunistic Networks

Unmanned aerial vehicles (UAVs) are now extensively used in a wide variety of applications, including key role within opportunistic wireless networks. These types networks considered well suited for infrastructure-less areas, or urban areas with overloaded cellular For these networks, UAVs envisioned to complement and support network performance; however, the short battery life commercial their need frequent charging can limit utility. This paper addresses challenge station placement UAV-aided network. We implemented three clustering approaches, namely, K-means, Density-Based Spatial Clustering Applications Noise (DBSCAN), random clustering, each approach being examined combination Epidemic, Spray Wait, State-Based Campus Routing (SCR) routing protocols. The simulation results show that determining locations using K-means clusters showed lower message delay higher success rate than deciding location either randomly DBSCAN regardless strategy employed between nodes.