Abstract

The border security situation is complex and severe, and the border patrol system relying on the ground-air cooperative architecture has been paid attention to by all countries as an important means of protecting national security. In the flying ad-hoc network (FANET), under the ground-air cooperative architecture, an unmanned aerial vehicle (UAV) uses a patrol mobility model to improve patrol efficiency. Since the patrol mobility model leads to frequent changes in UAV movement direction to improve patrol efficiency, selecting some clustering utility factors and calculating utility factors in previous clustering algorithms do not apply to this scenario. To solve the above problems, in this paper, we propose a border patrol clustering algorithm (BPCA) based on the ground-air cooperative architecture, which is based on the existing weighted clustering algorithm and improved in terms of the selection of utility factors and calculations of utility factors in cluster head selection. This algorithm comprehensively considers the effects of relative speed, relative distance, and the movement model of the UAV on the network topology. Extensive simulation results show that this algorithm can extend the duration time of cluster heads and cluster members and improve the stability of clusters and the reliability of links.

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call

Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.