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BOLD: Bio-Inspired Optimized Leader Election for Multiple Drones
Rajesh Ganesan1, X Mercilin Raajini2, Anand Nayyar3,4
1Department of Information Technology, MIT campus, Anna University, Chennai 600 044, India.
Sensors (Basel, Switzerland)
|June 5, 2020
Summary
This study introduces BOLD, a new algorithm for electing drone cluster heads. BOLD enhances drone network lifetime by 15% compared to existing methods, improving coordination in critical missions.
Area of Science:
- Robotics and Artificial Intelligence
- Network Engineering
- Distributed Systems
Background:
- Unmanned Aerial Vehicles (UAVs) are increasingly used in networked applications like surveillance and emergency rescue.
- Effective coordination and energy management are critical for multi-drone systems due to energy constraints.
- Existing cluster head election methods face challenges in dynamic, energy-constrained environments.
Purpose of the Study:
- To propose an efficient, dynamic cluster head election approach for multi-drone networks.
- To ensure robust decision-making and task assignment among drones.
- To improve the overall network lifetime and energy efficiency.
Main Methods:
- Developed Bio-Inspired Optimized Leader Election for Multiple Drones (BOLD) algorithm.
- BOLD utilizes two AI-based optimization techniques for dynamic cluster head selection.
- Employs a dynamic election process based on physical drone constraints and includes fail-over re-election.
Main Results:
- BOLD demonstrated a 15% increase in drone network lifetime compared to Particle Swarm Optimization-Cluster head election (PSO-C).
- BOLD shows improved energy consumption efficiency in simulations.
- The algorithm effectively manages dynamic leader election and task delegation.
Conclusions:
- BOLD provides an optimized, distributed solution for leader election in multi-drone systems.
- The proposed method significantly enhances network lifetime and energy efficiency.
- BOLD offers a promising approach for improving the performance of coordinated drone operations.
