BOLD: Bio-Inspired Optimized Leader Election for Multiple Drones

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.

Machine Learning Methods for UAV Flocks Management-A Survey

“…Ganesan et al [39] aimed to provide an effective solution to elect the CH among multiple drones at different periods based on the various physical constraints of the drones. The elected CH acts as the decision maker and assigns tasks to the other drones.…”

Software in Loop Simulation based Waypoint Navigation for Fixed Wing UAV

“…The shortest path between any two waypoints on sphere called orthodrome under the family of great circle. The shortest distance between any two points on sphere surface is calculated with the help of haversine formula and it is expressed in Equ (3). bearing between any two waypoint on sphere surface is called as waypoint bearing and its is expressed in Equ(4).…”

“…It helps to makeover gain parameters and control algorithm, therefore a platform such as Software in Loop System (SILS) and Hardware in Loop System (HILS) are needed. This will permit fast development, safety and reduce minimum real-time experimental flights [3][4] . Once the autopilot is tuned in simulation environment the gain which was adopted in simulation has been used in real flight which ensures the performance of autopilot in real time.…”

UAV Swarm Intelligence: Recent Advances and Future Trends

“…Modeling the problem under the framework of game theory, the drone can choose multiple strategies to take the next step [57]. [58] proposes a muti-UAV Bio-Inspired Optmizaed Leader Election (BOLE) method, the selected leader acts as a decision maker and assigns taks to other drones.…”

“…The relationship between the layers as shown in FIGURE 1. The decision-making layer is responsible for the evaluation, planning and assignment of tasks [26]- [58], and generate decision data to the path planning layer. The path planning layer manages the sub-tasks and generates corresponding sub-task planning paths based on the decision data [59]- [76].…”