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A cluster-based barrier construction algorithm in mobile Wireless Sensor Networks
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DOI：10.1016/j.phycom.2022.101839.png)
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It is important to maximize network lifetime while meeting coverage requirements in Wireless Sensor Networks (WSN). This paper focuses on maximizing the network lifetime of barrier coverage in WSN with mobile sensors. For mobile sensors, movement energy consumption can be much higher than during sensing and communication. Since the battery capacity of the sensor is fixed, more energy can be used for sensing and communication if the movement distance required to build the barrier can be reduced. Therefore, we focus on reducing the movement distance required by the mobile sensors to build the barriers. Also, we can build as many barriers as possible if we use as few mobile sensors as possible to build a barrier. By rotating multiple barriers, we can further extend the lifetime of the network. Based on the above concept, an energy-efficient algorithm is proposed for building barriers in WSNs with mobile sensors. The contribution of this paper are: (1) We find the minimum number of sensors required to construct a barrier for a rectangular region. (2) We explain how to cluster the sensors in the region, and then calculate the linear equations of the barriers based on the results of the clustering. (3) We explain how to set the sojourn points for these linear equations of the barriers, and then assign the mobile sensors to every one of these sojourn points. In short, the proposed algorithm can construct multiple barriers depending on the location of the sensor, with a minimum number of sensors connecting the left and right boundaries of the target field. Finally, the simulation results show that the proposed algorithm has better performance in terms of network lifetime than the previous result. (c) 2022 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
Keyword:
Barrier coverage
Network lifetime
Wireless Sensor Network
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2.2
论文数:
363
被引数:
2.6K
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