Localization and Clustering Based on Swarm Intelligence in UAV Networks for Emergency Communications

Abstract— In recent years, unmanned aerial vehicle (UAV) networks have been a focus area of the academic and industrial research community. They have been used in many military and civilian applications. Emergency communication is one of the essential requirements for first responders and victims in the aftermath of natural disasters. In such scenarios, UAVs may configure ad hoc wireless networks to cover a large area. In UAV networks, however, localization and routing are challenging tasks owing to the high mobility, unstable links, dynamic topology, and limited energy of UAVs. Here, we propose swarm-intelligence-based localization and clustering schemes in UAV networks for emergency communications. First, we propose a new three-dimensional (3D) swarm-intelligence-based localization (SIL) algorithm based on particle swarm optimization (PSO) that exploits the particle search space in a limited boundary by using the bounding box method. In the 3D search space, anchor UAV nodes are randomly distributed and the SIL algorithm measures the distance to existing anchor nodes for estimating the location of the target UAV nodes. Convergence time and localization accuracy are improved with lower computational cost. Second, we propose an energy-efficient swarm-intelligence-based clustering (SIC) algorithm based on PSO, in which the particle fitness function is exploited for inter-cluster distance, intra-cluster distance, residual energy, and geographic location. For energy-efficient clustering, cluster heads are selected based on improved particle optimization. The proposed SIC outperforms five typical routing protocols regarding packet delivery ratio, average end-to-end delay, and routing overhead. Moreover, SIC consumes less energy and prolongs network lifetime.