Abstract
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 constrains of the UAV payload and battery capacity. The proposed mission planner system employs the differential evolution optimization algorithm enabling UAVs to maximize the number of visited sensor nodes given the priority of the sensors and avoiding 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.
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The code can be available on demand. In this study, all computations were performed on a desktop PC with an Intel i7 3.20 GHz quad-core processor in MATLAB®2019a.
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MahmoudZadeh, S., Yazdani, A., Elmi, A. et al. Exploiting a fleet of UAVs for monitoring and data acquisition of a distributed sensor network. Neural Comput & Applic 34, 5041–5054 (2022). https://doi.org/10.1007/s00521-021-05906-x
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DOI: https://doi.org/10.1007/s00521-021-05906-x