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A heuristic node placement strategy for extending network lifetime and ensuring target coverage in mobile wireless sensor networks

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Abstract

Prolonging 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.

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Data Availability

The authors declare that the data supporting the findings of this study are available at https://github.com/nguyenphuctan-dev/mwsn-lifetime-experiment-data.

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Acknowledgements

This research is funded by Ministry of Education and Training under project number B2023.DNA.13.

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Correspondence to Nguyen Thi Hanh.

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Binh, H.T.T., Hanh, N.T., Tan, N.P. et al. A heuristic node placement strategy for extending network lifetime and ensuring target coverage in mobile wireless sensor networks. Evol. Intel. (2024). https://doi.org/10.1007/s12065-024-00916-9

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  • DOI: https://doi.org/10.1007/s12065-024-00916-9

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