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In this paper, we present an optimal flying path for unmanned aerial vehicle-assisted internet of things sensor networks using a location aware multi-layer information map considering different utility functions based on the sensor density, energy consumption, flight time, and flying risk level. The overall weighted sum of multi-objective utility functions is maximized using the genetic algorithm. The simulation results verify that the optimum solution points can be obtained by adjusting the weights while satisfying the required constraints

Flying path optimization in UAV-assisted IoT sensor networks