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

Rosario Napoli, Antonio Celesti, Massimo Villari, Maria Fazio

TL;DR

The paper tackles energy-limited drone fleet operation by introducing a PSO-inspired, bio‑based EMS that designates a gBest leader per cluster to manage communications with the Ground Base Station ($GBS$) and rotates leadership according to a battery threshold $B_T = B_0 \times (1 - T/100)$. It demonstrates, via OMNeT++/INET simulations across varying network sizes, that distributing long-range communication duties to rotating cluster leaders significantly extends network lifetime and reduces overall energy expenditure for transmissions to the $GBS$. The work also analyzes the tradeoffs between threshold settings and messaging overhead, highlighting the balance needed between responsiveness and energy efficiency. Overall, the approach offers a scalable, energy-aware mechanism for drone fleets that enhances autonomy in applications such as crisis management and remote sensing by adapting to dynamic network conditions.

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.

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

TL;DR

The paper tackles energy-limited drone fleet operation by introducing a PSO-inspired, bio‑based EMS that designates a gBest leader per cluster to manage communications with the Ground Base Station () and rotates leadership according to a battery threshold . It demonstrates, via OMNeT++/INET simulations across varying network sizes, that distributing long-range communication duties to rotating cluster leaders significantly extends network lifetime and reduces overall energy expenditure for transmissions to the . The work also analyzes the tradeoffs between threshold settings and messaging overhead, highlighting the balance needed between responsiveness and energy efficiency. Overall, the approach offers a scalable, energy-aware mechanism for drone fleets that enhances autonomy in applications such as crisis management and remote sensing by adapting to dynamic network conditions.

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.

Paper Structure

This paper contains 11 sections, 1 equation, 6 figures, 5 algorithms.

Table of Contents

  1. Introduction
  2. State of the Art
  3. Bio-Inspired approach for Leader Election
  4. The proposed Energy Management Strategy
  5. Initialization Phase
  6. Update Phase
  7. Election initialization Phase
  8. Election Phase
  9. Termination Phase
  10. Results
  11. Conclusions

Figures (6)

  • Figure 1: Different Architectures in Drone Fleet management.
  • Figure 2: Drone's Routine
  • Figure 3: Time/Threshold Results.
  • Figure 4: Event on death
  • Figure 5: Average Death Time
  • ...and 1 more figures