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Adaptive Data Collection Using UAV With Wireless Power Transfer for Wireless Rechargeable Sensor Networksopen access

Authors
Yoon, IkjuneNoh, Dong Kun
Issue Date
Jan-2022
Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Keywords
Energy harvesting; mobile sink; UAV; wireless power transfer; wireless sensor networks.
Citation
IEEE ACCESS, v.10, pp.9729 - 9743
Journal Title
IEEE ACCESS
Volume
10
Start Page
9729
End Page
9743
URI
http://scholarworks.bwise.kr/ssu/handle/2018.sw.ssu/42421
DOI
10.1109/ACCESS.2022.3144846
ISSN
2169-3536
Abstract
Wireless sensor networks (WSNs) are used to collect large amounts of data over a wide area. However, the sensor nodes used in WSNs have a short lifespan because they are generally battery-operated. Research is actively underway to increase the lifespan of WSNs via techniques such as energy harvesting and wireless power transfer (WPT). This study proposes a scheme for clustering and adjusting the WSN data collection rate to alleviate the hotspot problem and improve network connectivity. The proposed scheme is targeted at WSNs in which the sensor nodes harvest solar energy. In addition, an unmanned aerial vehicle (UAV) traverses along a given path and delivers energy to sensor nodes using WPT, while collecting data as a sink node. In this scheme, the number of cluster heads is determined by considering the number of nodes at each hop distance and the maximum amount of data that can be transferred with the available energy. Further, sensor nodes limit the amount of data they collect to reduce the relay load on intermediate nodes. To achieve this, nodes consider the amount of data transferred by parent nodes, especially nodes in the hotspot, and the number of child nodes. The sink node traverses along a given path, and collects the data accumulated in the cluster head while supplying its remaining energy to the cluster head. The amount of energy it transfers is determined by considering the number of cluster heads. Consequently, nodes in the hotspot are prevented from becoming blacked out from a lack of energy, and the energy harvesting efficiency is increased. Simulation results indicate that the proposed scheme reduces the number of blackout nodes in the hotspot areas near cluster heads. Furthermore, the data collection and monitoring performance increases with the increase in network connectivity.
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College of Information Technology (Department of Smart Systems Software)
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