An optimization method to improve the performance of unmanned aerial vehicle wireless sensor networks

Abstract

Wireless sensor networks have made great progress in recent years in every aspect of our life. To extend their range of application and provide a further effective option for remote surveillance, unmanned aerial vehicles have been gradually introduced into sensor networks, due to their advantages of flexibility, mobility, and ease of realization. Despite the success of various applications and studies in this new field, unmanned aerial vehicle–wireless sensor network still faces many open challenges, such as the unmanned aerial vehicle capable system framework, land-wireless sensor network management, and unmanned aerial vehicle mission planning strategies. In the article, we propose a cooperative framework for unmanned aerial vehicle–wireless sensor network, which is composed of sensor nodes, fixed-group leaders, and a unmanned aerial vehicle-Sink, in which a three-layer hierarchical network is formed. A land-wireless sensor network k-means driven grouping approach is then presented, which considers the communication performance, the position, and other factors. Additionally, a simulated annealing algorithm is employed to detect the optimal flight trajectory according the ground wireless sensor network architecture. Finally, the proposed approach is compared to other related approaches, and the results have shown better performance of our proposal in terms of energy consumption, flying time, and other relevant evaluation criteria.