Exploiting a Fleet of UAVs for Monitoring and Data Acquisition of a Distributed Sensor Network
This work addresses the problem of efficient data collection from distributed sensor networks for applications like environmental monitoring, meteorology, agriculture, and renewable energy, offering an incremental improvement in mission planning.
This study proposes a collaborative mission planning system for a team of UAVs to efficiently collect data from a distributed sensor network, maximizing visited sensor nodes while considering UAV payload and battery constraints. The system uses the Differential Evolution (DE) optimization algorithm to prioritize sensors and avoid redundant data collection.
This study proposes an efficient data collection strategy exploiting a team of Unmanned Aerial Vehicles (UAVs) to monitor and collect the data of a large distributed sensor network usually used for environmental monitoring, meteorology, agriculture, and renewable energy applications. The study develops a collaborative mission planning system that enables a team of UAVs to conduct and complete the mission of sensors' data collection collaboratively while considering existing constraints of the UAV payload and battery capacity. The proposed mission planner system employs the Differential Evolution (DE) optimization algorithm enabling UAVs to maximize the number of visited sensor nodes given the priority of the sensors and avoiding the redundant collection of sensors' data. The proposed mission planner is evaluated through extensive simulation and comparative analysis. The simulation results confirm the effectiveness and fidelity of the proposed mission planner to be used for the distributed sensor network monitoring and data collection.