Abstract
Data collection based on autonomous underwater vehicle (AUV) brings significant advantages for underwater wireless sensor networks (UWSNs) to save and balance energy consumption. For some marine monitoring applications, not only the value of information (VoI) of the event but also the timeliness of the event needs to be considered in order to collect the sensed event data as comprehensively as possible to ensure the data completeness in the monitoring area. However, existing research works often ignore the latter, which may lead to inaccurate decisions made by marine applications due to missing data completeness. Therefore, in response to the on-demand scenario where events generate dynamically, we propose an on-demand path planning scheme (OD-PPS) to maximize the completeness of the data collected by the AUV. In the scheme, we first apply iterative local search method to obtain a collection order of the nodes. After optimizing the visiting points of the nodes to shorten the path length, a node re-insert algorithm is proposed to reduce the data loss. Then, for new collection requests generated during the AUV collection process, we update the AUV path according to the location of the node that sent the request. Finally, extensive simulations verify the effectiveness of the proposed scheme.
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Acknowledgment
This research was supported in part by the National Natural Science Foundation of China-Guangdong Joint Fund under Grant No. U1701263, the National Natural Science Foundation of China (NSFC) under Grant No. 61871286, and Tianjin Key Laboratory of Advanced Networking (TANK).
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Yu, T., Liu, C., Qu, W., Zhao, Z. (2021). OD-PPS: An On-Demand Path Planning Scheme for Maximizing Data Completeness in Multi-modal UWSNs. In: Liu, Z., Wu, F., Das, S.K. (eds) Wireless Algorithms, Systems, and Applications. WASA 2021. Lecture Notes in Computer Science(), vol 12937. Springer, Cham. https://doi.org/10.1007/978-3-030-85928-2_2
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