PRobust: a percolation-based robustness optimization model for underwater acoustic sensor networks

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dc.contributor.authorZhang, Zhaowei
dc.contributor.authorLiu, Chunfeng
dc.contributor.authorQu, Wenyu
dc.contributor.authorZhao, Zhao
dc.contributor.authorGuo, Weisi
dc.date.accessioned2024-12-12T13:45:55Z
dc.date.available2024-12-12T13:45:55Z
dc.date.freetoread2024-12-12
dc.date.issued2024-12-31
dc.date.pubOnline2024-10-29
dc.description.abstractIn Underwater Acoustic Sensor Networks (UASNs), the robustness of network is greatly affected by complex marine environments when implementing multi-hop data transmission. Factors such as the underwater acoustic channel and dynamic topological changes induced by multi-layered oceanic vortices exacerbate this influence. However, there is currently a research gap in the specific area of robustness optimization for UASNs. Existing studies on robustness optimization are unsuitable for UASNs as they neglect the considerations of the marine environment and node characteristics (e.g., residual energy). In this work, we propose PRobust, a percolation-based robustness optimization model for UASNs. PRobust consists of two distinct phases: percolation modeling and bottleneck optimization. In the percolation modeling phase, we incorporate both node and edge features, considering the physical and topological properties, and introduce a novel approach for calculating link quality. In the bottleneck optimization phase, we devise a graph theory-based method to identify bottlenecks, leveraging the flow information recorded by nodes to improve the accuracy of bottleneck discovery. Moreover, we integrated time slots and a current movement model into the proposed model, allowing its applicability to dynamically changing UASNs. Extensive simulation results indicate that, compared to existing methods, PRobust significantly enhances network robustness and performance with the same overhead after bottleneck optimization.
dc.description.journalNameIEEE Transactions on Network and Service Management
dc.identifier.citationZhang Z, Liu C, Qu W, et al., (2024) PRobust: a percolation-based robustness optimization model for underwater acoustic sensor networks. IEEE Transactions on Network and Service Management, Available online 29 October 2024
dc.identifier.eissn1932-4537
dc.identifier.elementsID558570
dc.identifier.issn1932-4537
dc.identifier.urihttps://doi.org/10.1109/tnsm.2024.3487956
dc.identifier.urihttps://dspace.lib.cranfield.ac.uk/handle/1826/23283
dc.language.isoen
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)
dc.publisher.urihttps://ieeexplore.ieee.org/document/10737363
dc.rightsAttribution-NonCommercial 4.0 Internationalen
dc.rights.urihttp://creativecommons.org/licenses/by-nc/4.0/
dc.subject4605 Data Management and Data Science
dc.subject4606 Distributed Computing and Systems Software
dc.subject46 Information and Computing Sciences
dc.subject14 Life Below Water
dc.subjectNetworking & Telecommunications
dc.subject4006 Communications engineering
dc.subject4604 Cybersecurity and privacy
dc.subject4606 Distributed computing and systems software
dc.subjectUnderwater Acoustic Sensor Networks
dc.subjectRobustness Optimization
dc.subjectPercolation
dc.subjectCurrent Movement
dc.subjectData Transmission
dc.subjectNetwork Traffic
dc.titlePRobust: a percolation-based robustness optimization model for underwater acoustic sensor networks
dc.typeArticle
dc.type.subtypeJournal Article

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