Energy-Optimized Path Planning for UAVs to Minimize Fleet Size in Time-Sensitive Data Collection Tasks

In this paper, we investigate the problem of completing data collection tasks for IoTDs (Internet of Things devices) using Unmanned Aerial Vehicles (UAVs) under the constraints of UAVbattery capacity and predefined data age. Our objective was to minimize UAV’s flight energy consumption by optimizing their trajectory allocation and achieve the research goal with the minimum number of UAVs. We transformed the multi-objective optimization problem (MOP) into a Traveling Salesman Problem with Neighborhoods (TSPN) by considering UAVs’ ability to collect data within the communication range of IoT devices. We studied two modes of data collection for UAVs: one in which they can only collect data during hovering and another in which they can collect data while hovering and moving simultaneously. We proposed two algorithms: UAV hovering data collection algorithm with neighborhood (UHDCN) and UAV moving data collection algorithm with neighborhood (UMDCN). We evaluate the performance of the proposed algorithms through extensive simulation experiments. The results demonstrate that UHDCN algorithm requires at least 4.8% fewer number of UAVs compared to existing algorithms, while UMDCN algorithm requires at least 22.2% fewer number of UAVs. Additionally, UHDCN algorithm consumes at least 4.9% less total energy compared to existing algorithms, while UMDCN algorithm consumes at least 22.8 less energy.