Hybrid Self-Organized Clustering Scheme for Drone Based Cognitive Internet of Things

Abstract

Network management by using a cognitive approach is an attractive solution for drone-based Internet of Things (IoT) environment to provide many modern facilities to IoT users. In this paper, we try to minimize the networking related issues for drone-based IoT by providing a self-organized cluster-based networking solution. We propose a Hybrid Self-organized Clustering Scheme (HSCS) for drone-based cognitive IoT which utilizes a hybrid mechanism of glowworm swarm optimization (GSO) and dragonfly algorithm (DA). The proposed scheme contains cluster formation and cluster head selection mechanism based on GSO. Furthermore, we propose an effective cluster member tracking methodology using the behavioral study of DA which ensures efficient cluster management. The cluster maintenance is performed by a mechanism to identify dead cluster member which improves the stability of the network. Further routing mechanism is proposed for HSCS in which next hop neighbor for data transmission is selected by using the route selection function which ensures efficient communication. The performance of HSCS is evaluated in terms of cluster building time, energy consumption, cluster lifetime, and the probability of delivery success with existed hybrid bio-inspired clustering algorithm.