Please use this identifier to cite or link to this item: https://elib.vku.udn.vn/handle/123456789/3953
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dc.contributor.authorHuynh, Thi Thanh Binh-
dc.contributor.authorNguyen, Thi Hanh-
dc.contributor.authorNguyen, Phuc Tan-
dc.contributor.authorLa, Van Quan-
dc.contributor.authorDang, The Ngoc-
dc.contributor.authorNguyen, Huu Nhat Minh-
dc.contributor.authorHuynh, Cong Phap-
dc.date.accessioned2024-07-29T02:25:58Z-
dc.date.available2024-07-29T02:25:58Z-
dc.date.issued2024-01-
dc.identifier.issn1864-5917-
dc.identifier.urihttps://doi.org/10.1007/s12065-024-00916-9-
dc.identifier.urihttps://elib.vku.udn.vn/handle/123456789/3953-
dc.descriptionEvolutionary Intelligencevi_VN
dc.description.abstractProlonging network lifetime has long been one of the most critical challenges in designing wireless sensor networks in general and mobile wireless sensor networks in particular. Regarding network lifetime, one of the factors affecting it the most is energy efficiency. In a mobile wireless sensor network, compared to stationary ones, energy management has an even greater impact on the network lifetime since the movement of the sensors drains an enormous amount of energy. Moreover, in target-based wireless sensor networks, it is mandatory to ensure target coverage along with lifetime optimization. In this paper, we investigate a mobile sensor network model where stationary targets must be continuously monitored by mobile sensors. In order to maximize network lifetime and guarantee the coverage of all targets in the monitoring region, we take sensor nodes’ movement into account. We propose the Lifetime Effective Movement Algorithm, a novel heuristic approach consisting of determining the optimal regions for sensor deployment and scheduling sensor nodes’ movement, to address this issue. Experimental results demonstrate that our proposed algorithm outperforms two existing approaches in terms of network lifetime with an improvement varying from 125% to 269%. Moreover, the proposed method produces an approximation ratio in the range of 82.14- 88.41% compared to the exact solution.vi_VN
dc.language.isoenvi_VN
dc.publisherSpringer Naturevi_VN
dc.subjectTarget coveragevi_VN
dc.subjectMobile wireless sensor networkvi_VN
dc.subjectHeuristic algorithmvi_VN
dc.subjectNetwork lifetimevi_VN
dc.titleA heuristic node placement strategy for extending network lifetime and ensuring target coverage in mobile wireless sensor networksvi_VN
dc.typeWorking Papervi_VN
Appears in Collections:NĂM 2024

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