Cluster Computing

, Volume 22, Supplement 3, pp 7507–7514 | Cite as

Region segmentation model for wireless sensor networks considering optimal energy conservation constraints

  • Xi Chen
  • Tao WuEmail author


In order to improve the life cycle of wireless sensor networks as well as reducing the energy cost, the structural optimization and energy conservation for region segmentation are designed. A region segmentation model for wireless sensor networks based on optimal energy conservation constraints is proposed. The initial network topology for node distribution of wireless sensor networks is constructed. The equivalent network-wide energy balance topology is used for optimal calculation of the coverage area of the sensor network and the shortest path optimization method is used for energy conservation design for sensor network nodes. According to the energy attribute of sensor nodes, the coverage area of wireless sensor networks is segmented optimally to improve the coverage of wireless sensor networks and reduce the energy cost of a single node in the network, to realize the optimal networking of wireless sensor networks. The simulation results show that for the region segmentation model of wireless sensor networks constructed by this method, the quality reliability of transmitting data by network nodes is higher, the regional coverage is stronger and the energy cost is lower, compared with previous works, which effectively prolong the life cycle of wireless sensor networks.


Energy constraint Wireless sensor network Region segmentation Energy cost Coverage 



This work was supported by the Science & Technology Department of Sichuan Province (Grant No. 2016RZ0065 and 2016RZ0053), the Education Department of Sichuan Province (Grant No. 15ZA0396 and 16ZB0212), Southwest Minzu University Graduate Teaching Program (Grant No. 2017YJZX006), the Southwest Minzu University Teaching Reform Program (Grant No. 2017ZC19) and Fundamental Research Funds for the Central Universities, Southwest Minzu University (Grant No. 2018NQN56).


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© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.School of Computer Science and TechnologySouthwest Minzu UniversityChengduChina
  2. 2.School of Computer ScienceChengdu University of Information TechnologyChengduChina

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