Early wildfire detection by air quality sensors on unmanned aerial vehicles: Optimization and feasibility

“Millions of acres of forests are destroyed by wildfires every year, causing ecological, environmental, and economical losses. The recent wildfires in Australia and the Western U.S. smothered multiple states with more than fifty million acres charred by the blazes. The warmer and drier climate makes scientists expect increases in the severity and frequency of wildfires and the associated risks in the future. These inescapable crises highlight the urgent need for early detection and prevention of wildfires. This work proposed an energy management framework that integrated unmanned aerial vehicle (UAV) with air quality sensors for early wildfire detection and forest monitoring. An autonomous patrol solution that effectively detects wildfire events, while preserving the UAV battery for a larger area of coverage was developed. The UAV can send real-time data (e.g., sensor readings, thermal pictures, videos, etc) to nearby communications base stations (BSs) when a wildfire is detected. An optimization problem that minimized the total UAV’s consumed energy and satisfied a certain quality-of-service (QoS) data rate were formulated and solved. More specifically, this study optimized the flight track of a UAV and the transmit power between the UAV and BSs. Finally, selected simulation results that illustrate the advantages of the proposed model were proposed”--Abstract, page iii

Early Wildfire Detection using Uavs Integrated with Air Quality and Lidar Sensors

Every year, wildfires burn out countless hectares of lands, resulting in ecological, environmental, and economic damage. This paper presents an energy management system that consists of an unmanned aerial vehicle (UAV) equipped with air quality and light detection and ranging (LiDAR) sensors for monitoring forests and recognizing flames early. We develop a novel approach for autonomous patrolling system. This approach has the advantage of effectively detecting wildfire incidents, while optimizing the energy consumption of the UAV\u27s battery to cover large areas. When a wildfire is detected, the UAV is able to transmit real-time data, such as sensor readings and LiDAR data, to the nearby communication tower. We formulate an optimization problem that minimizes the overall UAV\u27s energy consumption due to patrolling. Based on the pollutant dispersion mode, we propose a novel UAV patrolling solution based on genetic algorithm with the goal of maximizing the patrolling coverage of the UAV taking into account the UAV\u27s battery constraints. More specifically, we optimize the UAV\u27s flight path using a plume dispersion model to find the concentration of common gases of wildfire. Finally, simulations are presented to show the efficiency and validity of the solution

Unmanned-Aircraft-System-Assisted Early Wildfire Detection with Air Quality Sensors †

Numerous Hectares of Land Are Destroyed by Wildfires Every Year, Causing Harm to the Environment, the Economy, and the Ecology. More Than Fifty Million Acres Have Burned in Several States as a Result of Recent Forest Fires in the Western United States and Australia. According to Scientific Predictions, as the Climate Warms and Dries, Wildfires Will Become More Intense and Frequent, as Well as More Dangerous. These Unavoidable Catastrophes Emphasize How Important Early Wildfire Detection and Prevention Are. the Energy Management System Described in This Paper Uses an Unmanned Aircraft System (UAS) with Air Quality Sensors (AQSs) to Monitor Spot Fires Before They Spread. the Goal Was to Develop an Efficient Autonomous Patrolling System that Detects Early Wildfires While Maximizing the Battery Life of the UAS to Cover Broad Areas. the UAS Will Send Real-Time Data (Sensor Readings, Thermal Imaging, Etc.) to a Nearby Base Station (BS) When a Wildfire is Discovered. an Optimization Model Was Developed to Minimize the Total Amount of Energy Used by the UAS While Maintaining the Required Levels of Data Quality. Finally, the Simulations Showed the Performance of the Proposed Solution under Different Stability Conditions and for Different Minimum Data Rate Types