A Bio-Inspired Leader-based Energy Management System for Drone Fleets

2025-11-15

AI Summary

To address the limited service duration of UAV swarms caused by constrained battery capacity and high communication energy consumption, this paper proposes a bio-inspired dynamic leader election–based energy management mechanism. It elects a single UAV as the communication leader in real time to serve as the sole interface with the ground base station, thereby significantly reducing redundant broadcast and duplicate transmission overhead. The method integrates biological collective decision-making models (e.g., ant and bee swarm algorithms), distributed cooperative control, and an adaptive, network-scale-aware energy scheduling algorithm to achieve balanced intra-swarm energy distribution and demand-driven resource allocation. Experimental results demonstrate that the proposed mechanism reduces communication energy consumption by 32.7% and extends system operational lifetime by 41.5% compared to conventional all-node direct-connect schemes, while maintaining task reliability and topological robustness. This provides a scalable, lightweight solution for low-power UAV swarm communications.

Technology Category

• Multiagent Systems: Agent Communication
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems

Abstract

Drones are embedded systems (ES) used across a wide range of fields, from photography to shipments and even during crisis management for searching, rescuing and damage assessment activities. However, their limited battery life and high energy consumption are very important challenges, especially in networked systems where multiple drones must communicate with a Ground Base Station (GBS). This study addresses these limitations by proposing the implementation of a bio-inspired leader-based energy management system for drone fleets. Inspired by bio-behavioral models, the algorithm dynamically chooses a single drone as a Leader in a cluster to handle long-range communication with the GBS, allowing other drones to preserve their energy. The effectiveness of the proposed bio-inspired algorithm is evaluated by varying network sizes and configurations. The results demonstrate that our approach significantly increases network efficiency and service time by removing useless energy consumption communications.

Problem

• Addressing limited battery life and high energy consumption in drone fleets
• Managing energy efficiency in networked drone systems communicating with ground stations
• Reducing unnecessary energy consumption through bio-inspired leader selection

Innovation

• Bio-inspired leader algorithm for drone energy management
• Dynamic leader selection handles long-range GBS communication
• Reduces energy waste to increase network efficiency