A Drones Optimal Path Planning Based on Swarm Intelligence Algorithms

Abstract

The smart city comprises various interlinked elements which communicate data and offers urban life to citizen. Unmanned Aerial Vehicles (UAV) or drones were commonly employed in different application areas like agriculture, logistics, and surveillance. For improving the drone flying safety and quality of services, a significant solution is for designing the Internet of Drones (IoD) where the drones are utilized to gather data and people communicate to the drones of a specific flying region using the mobile devices is for constructing the Internet-of-Drones, where the drones were utilized for collecting the data, and communicate with others. In addition, the SIRSS-CIoD technique derives a tuna swarm algorithm-based clustering (TSA-C) technique to choose cluster heads (CHs) and organize clusters in IoV networks. Besides, the SIRSS-CIoD technique involves the design of a biogeography-based optimization (BBO) technique to an optimum route selection (RS) process. The design of clustering and routing techniques for IoD networks in smart cities shows the novelty of the study. A wide range of experimental analyses is carried out and the comparative study highlighted the improved performance of the SIRSS-CIoD technique over the other approaches.