Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/107497
Title: ATCFS : effective connectivity restoration scheme for underwater acoustic sensor networks
Authors: Liu, Lingfeng
Ma, Maode
Liu, Chunfeng
Qu, Wenyu
Zhang, Gang
Shu, Yantai
Keywords: Engineering::Electrical and electronic engineering
Computer Network Performance
Network Reliability
Issue Date: 2019
Source: Liu, L., Ma, M., Liu, C., Qu, W., Zhang, G., & Shu, Y. (2019). ATCFS : effective connectivity restoration scheme for underwater acoustic sensor networks. IEEE Access, 7, 87704-87715. doi:10.1109/ACCESS.2019.2921617
Series/Report no.: IEEE Access
Abstract: Underwater acoustic sensor network (UASN) has become one of the enabling technologies for the development of future ocean observation systems (OOSs). However, the UASN could be severely damaged because of the harsh environment that causes the simultaneous failure of many sensor nodes, thus leading to the partition of the network into multiple disconnected segments. In this paper, we studied the crucial issue to reestablish the network connectivity with the least quantity of employed relay nodes. To achieve a clear understanding of the issue, we present its integer nonlinear programming formulation, which is generally NP-hard. So, with the aim of solving the problem efficiently, an original heuristic scheme is proposed in this paper. Two fundamental algorithms are integrated into the scheme, namely, alternating tree construction and Fermat-point selection (ATCFS) as a whole. The results of extensive simulation experiment have confirmed that the ATCFS can solve this problem simply and effectively.
URI: https://hdl.handle.net/10356/107497
http://hdl.handle.net/10220/49701
DOI: http://dx.doi.org/10.1109/ACCESS.2019.2921617
Rights: © 2019 IEEE. Articles accepted before 12 June 2019 were published under a CC BY 3.0 or the IEEE Open Access Publishing Agreement license. Questions about copyright policies or reuse rights may be directed to the IEEE Intellectual Property Rights Office at +1-732-562-3966 or copyrights@ieee.org.
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:EEE Journal Articles

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