UAV-based System Energy Efficiency Optimization for Air Pollutant Monitoring

Monitoring air pollution has recently taken on the status of being of utmost importance. Nevertheless, it can be difficult to gather information on air quality close to pollution sources, particularly when the locations are complicated, have physical obstacles, or are in motion. However, small unmanned aerial vehicles provide an innovative and palatable new way of studying air pollution and the atmosphere. In this context, this article suggests using drones with sensors to carry out duties involving air pollution monitoring. In particular, a search approach based on a chemotaxis-metaheuristic and a local particle swarm optimization approach is recommended to build an optimal approach that improves UAV cooperative search by reducing overall search time and optimizing energy consumption. Two phases make up the complete approach. The favored objective in the initial phase is plume detection, where each drone operating in LoPSO mode selects its course based on the planar coordinates produced by this mode. The second stage entails instructing the drones to follow spiral trajectories in order to trace the feather that has been discovered. The experimental findings demonstrate that by concentrating on regions with the largest concentrations of contaminants, an implicit priority directs the creation of pollution maps. According to simulation data, the recommended method performs better and shows promising outcomes in terms of complexity and accuracy. Specifically, it can more precisely locate the locations that are the most contaminated and provide greater coverage within the time constraints imposed by the drone's flying period.