Abstract
Over the past few years, unmanned aerial vehicles (UAV) or drones have been used for many applications. In certain applications like surveillance and emergency rescue operations, multiple drones work as a network to achieve the target in which any one of the drones will act as the master or coordinator to communicate, monitor, and control other drones. Hence, drones are energy-constrained; there is a need for effective coordination among them in terms of decision making and communication between drones and base stations during these critical situations. This paper focuses on providing an efficient approach for the election of the cluster head dynamically, which heads the other drones in the network. The main objective of the paper is to provide an effective solution to elect the cluster head among multi drones at different periods based on the various physical constraints of drones. The elected cluster head acts as the decision-maker and assigns tasks to other drones. In a case where the cluster head fails, then the next eligible drone is re-elected as the leader. Hence, an optimally distributed solution proposed is called Bio-Inspired Optimized Leader Election for Multiple Drones (BOLD), which is based on two AI-based optimization techniques. The simulation results of BOLD compared with the existing Particle Swarm Optimization-Cluster head election (PSO-C) in terms of network lifetime and energy consumption, and from the results, it has been proven that the lifetime of drones with the BOLD algorithm is 15% higher than the drones with PSO-C algorithm.
Highlights
In the past, drones or unmanned aerial vehicles (UAVs) have been used only as expensive military aircraft or small toys for kids
The Hybrid Particle Swarm Optimization and Genetic Algorithm (HPSOGA) was compared with the simple Particle Swarm Optimization (PSO) algorithm, and the results showed that the former is better than the later in solving both multi-UAV formation reconfiguration problems and finding time-optimal solutions under complicated environments
Since the energy of drones decreases with time, we used the energy model Equation (17) to calculate the updated battery value for every iteration
Summary
Drones or unmanned aerial vehicles (UAVs) have been used only as expensive military aircraft or small toys for kids. Drone-based are expected to increase and India will become the third-largest commercial drone market in the year communication systems provide two kinds of communication—air to communication—air ground Based on the application or problem to be solved, the drones of various types cancan be be classified based on their size, range, and endurance, number rotors,and andendurance, altitude, asnumber shown in applied. The for need for efficient communication among drones the better achievement of this, aofleader electedistoelected co-ordinate the multiple multiplearises. To improve efficiency in battery capacity and to perform separate tasks, multiple drones are divided into clusters. Even in these clusters, leaders are elected for better communication among drones.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
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 callDisclaimer: 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.
