The use of unmanned aerial vehicles(UAVs) in the collection of data from wireless devices, sensor nodes, and the Internet of Things(IoT) devices has recently received significant attention. In this paper, we investiga...The use of unmanned aerial vehicles(UAVs) in the collection of data from wireless devices, sensor nodes, and the Internet of Things(IoT) devices has recently received significant attention. In this paper, we investigate the data collection process from a set of smart meters in advanced metering infrastructure(AMI) enabled by UAVs. The objective is to minimize the total annual cost of the electric utility by jointly optimizing the number of UAVs, their power source sizing, the charging locations as well as the data collection trip planning. This is achieved while considering the energy budgets of batteries of UAVs and the required amount of collected data. The problem is formulated as a mixed-integer nonlinear programming(MINLP), which is decoupled into two sub-problems where a candidate UAV and a number of buildings are first grouped into trips via genetic algorithms(GAs), and then the optimum trip path is found using a traveling salesman problem(TSP) branch and bound algorithm. Simulation results show that the battery capacity or the number of UAVs increases as the coverage area or the density increases.展开更多
基金supported by project#EFRG18-GER-CEN-10 from the American University of Sharjah。
文摘The use of unmanned aerial vehicles(UAVs) in the collection of data from wireless devices, sensor nodes, and the Internet of Things(IoT) devices has recently received significant attention. In this paper, we investigate the data collection process from a set of smart meters in advanced metering infrastructure(AMI) enabled by UAVs. The objective is to minimize the total annual cost of the electric utility by jointly optimizing the number of UAVs, their power source sizing, the charging locations as well as the data collection trip planning. This is achieved while considering the energy budgets of batteries of UAVs and the required amount of collected data. The problem is formulated as a mixed-integer nonlinear programming(MINLP), which is decoupled into two sub-problems where a candidate UAV and a number of buildings are first grouped into trips via genetic algorithms(GAs), and then the optimum trip path is found using a traveling salesman problem(TSP) branch and bound algorithm. Simulation results show that the battery capacity or the number of UAVs increases as the coverage area or the density increases.
