Abstract
Unmanned Aerial Vehicles (UAVs) have been widely used in data collection, tracking and monitoring in wireless sensor networks (WSNs). By considering the three factors of sensor coverage, energy consumption and Quality of Service (QoS), the WSNs data collection problem is transformed into a location planning model for optimizing K-location of UAVs. Besides, an adaptive search algorithm contains two crucial methods are proposed to address this issue, form which one is the optimal matching method between sensors and UAVs, and the other is automatic location generation strategy of UAVs. Finally, analytical and simulation-based results show that the proposed algorithm has obvious advantages over the KMeans algorithm in location planning option of UAVs.
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Acknowledgments
This work is supported by the PhD Start-Up Fund of Dongguan University of Technology (GC300502-3),the Higher Education Innovation Strong School Project of Guangdong Province of China (2017KQNCX190), the Natural Science Foundation of Guangdong Province (Grant No. 2018A030313014), the research team project of Dongguan University of Technology (Grant No. TDY-B2019009), and the Guangdong University Key Project (2019KZDXM012).
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Li, X., Tao, M. (2020). Location Planning of UAVs for WSNs Data Collection Based on Adaptive Search Algorithm. In: Chen, X., Yan, H., Yan, Q., Zhang, X. (eds) Machine Learning for Cyber Security. ML4CS 2020. Lecture Notes in Computer Science(), vol 12487. Springer, Cham. https://doi.org/10.1007/978-3-030-62460-6_19
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DOI: https://doi.org/10.1007/978-3-030-62460-6_19
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