  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.
