Optimizing Forest Fire Prevention: Intelligent Scheduling Algorithms for Drone-Based Surveillance System

TL;DR

This paper develops and tests intelligent scheduling algorithms for drone-based forest fire monitoring, demonstrating significant efficiency improvements in task completion times through extensive experiments.

Contribution

It introduces novel scheduling algorithms specifically designed for drone forest monitoring, optimizing task completion times to improve fire detection and response.

Findings

RID algorithm achieved up to 90.3% performance improvement

Algorithms effectively reduced total task completion time

Experimental results validated the efficiency of proposed methods

Abstract

Given the importance of forests and their role in maintaining the ecological balance, which directly affects the planet, the climate, and the life on this planet, this research presents the problem of forest fire monitoring using drones. The forest monitoring process is performed continuously to track any changes in the monitored region within the forest. During fires, drones' capture data is used to increase the follow-up speed and enhance the control process of these fires to prevent their spread. The time factor in such problems determines the success rate of the fire extinguishing process, as appropriate data at the right time may be the decisive factor in controlling fires, preventing their spread, extinguishing them, and limiting their losses. Therefore, this research presented the problem of monitoring task scheduling for drones in the forest monitoring system. This problem is solved by developing several algorithms with the aim of minimizing the total completion time required to carry out all the drones' assigned tasks. System performance is measured by using 990 instances of three different classes. The performed experimental results indicated the effectiveness of the proposed algorithms and their ability to act efficiently to achieve the desired goal. The algorithm $RID$ achieved the best performance with a percentage rate of up to 90.3% with a time of 0.088 seconds.