Bio-inspired clustering scheme for Internet of Drones application in industrial wireless sensor network

Abstract

Recent technological improvements have revolutionized the wireless sensor network-based industrial sector with the emergence of Internet of Things. Internet of Drones, a branch of Internet of Things, is used for the communication among drones. As drones are mobile in nature, they cause frequent topological changes. This changing topology causes scalability, stability, and route selection issues in Internet of Drones. To handle these issues, we propose a bio-inspired clustering scheme using dragonfly algorithm for cluster formation and management. In this article, we propose cluster head election based on the connectivity with the base station along with the fitness function which consists of residual energy and position of the drones. Furthermore, for route selection we propose an optimal path selection based on the residual energy and position of drone for efficient communication. The proposed scheme shows better results as compared to other bio-inspired clustering algorithms on the basis of evaluation benchmarks such as cluster building time, network energy consumption, cluster lifetime, and probability of successful delivery. The results indicate that the proposed scheme has improved 60% and 38% with respect to ant colony optimization and grey wolf optimization, respectively, in terms of average cluster building time while average energy consumption has improved 23% and 33% when compared to the ant colony optimization and grey wolf optimization, respectively.

A Survey on Cluster Head Selection and Cluster Formation Methods in Wireless Sensor Networks

“…Mobile WSNs have limitations of frequent topology changes and scalability issues. In [83], the authors proposed a bioinspired clustering scheme using the dragonfly algorithm (DA) for the internet of drone application (BICIoD) to handle the issues. Dragonflies have two swarming behaviors called static swarming (finding food), which promotes exploitation, and dynamic behavior (migration), which promotes exploration capability.…”

Search and Rescue in a Maze-like Environment with Ant and Dijkstra Algorithms

“…Practitioners in the field have identified UAVs, used as mobile sensing devices, as having great potential when it comes to providing communication support [35,52] and mobile communication infrastructure [9,18], such as a drone-based (mobile) wireless sensor network (WSN) [53] or a wireless local area network (WLAN) in which communication is realized node to node (N2N) using ad hoc [54] routing [55] to exchange messages between nodes. Examples for the use of dronebased wireless sensors networks (WSN) [56][57][58] are, e.g., healthcare and environmental monitoring [11,[59][60][61]) as well as reconnaissance in the aftermath of a disaster [10], and general applications where the topology of the network is dynamic and adaptive [62].…”

A Drones Optimal Path Planning Based on Swarm Intelligence Algorithms

“…The swarm intelligence (SI) based models are utilized to a near-optimum solution. In an Adhoc technique, there is no centralized control that leads to growing complexities in management and configuration [10]. Therefore, it is necessary to develop this network architecture which contains self-organizing abilities like selfoptimization, self-configuring, self-healing, and adaptive to environments.…”