A Novel Sensor Clustering Algorithm for UAV-Assisted Wireless Sensor Networks

In wireless sensor networks (WSNs), unmanned aerial vehicles (UAVs) contribute to improving network throughput and extending its lifetime, since sensors are energy-limited devices. This study aims to optimize the efficiency of UAV data collection from WSNs by minimizing the required number of hovering locations (HLs) while ensuring successful data transmission from each sensor to the UAV in a single trip. The reduction of HLs is crucial for optimizing the UAV trajectory. To this end, we propose an efficient algorithm to partition sensors into clusters with a specified radius, denoted as R. Numerical results show that the proposed algorithm performs compared to the adaptive K-means clustering algorithm in terms of the number of HLs.