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Evolution model of high quality of service for spatial heterogeneous wireless sensor networks

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  • Xiong, Chong-Wei
  • Tang, Ming
  • Wang, Xiao-Hua
  • Liu, Ying
  • Shi, Jia

Abstract

The complex network theory is helpful to design the self-organizing evolution mechanism of the network and generate wireless sensor networks (WSNs) with high quality of service. Focusing on the heterogeneous WSNs, we firstly propose an evolution model based on the fitness model in complex network and then propose two self-organizing evolution models based on the topology hops for heterogeneous WSNs. By considering the topology hops in the self-organizing evolution mechanism, the two models can effectively reduce the reverse connection and balance the load of nodes around the sink in network evolution process. Simulation results show that compared with three classical models, the proposed models provide a higher quality of service, such as longer network lifetime, better energy-balanced factor, shorter shortest path length and stronger robustness against malicious attacks. Our self-organizing evolution models provide a new perspective and reference for the design of heterogeneous WSNs with high quality of service.

Suggested Citation

  • Xiong, Chong-Wei & Tang, Ming & Wang, Xiao-Hua & Liu, Ying & Shi, Jia, 2022. "Evolution model of high quality of service for spatial heterogeneous wireless sensor networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 596(C).
    • Handle: RePEc:eee:phsmap:v:596:y:2022:i:c:s0378437122001820
    DOI: 10.1016/j.physa.2022.127182
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    References listed on IDEAS

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    1. Xia, Yongxiang & Wang, Cong & Shen, Hui-Liang & Song, Hainan, 2020. "Cascading failures in spatial complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 559(C).
    2. Zhu, Hailin & Luo, Hong & Peng, Haipeng & Li, Lixiang & Luo, Qun, 2009. "Complex networks-based energy-efficient evolution model for wireless sensor networks," Chaos, Solitons & Fractals, Elsevier, vol. 41(4), pages 1828-1835.
    3. Li, Shudong & Li, Lixiang & Yang, Yixian, 2011. "A local-world heterogeneous model of wireless sensor networks with node and link diversity," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 390(6), pages 1182-1191.
    4. Lü, Linyuan & Zhou, Tao, 2011. "Link prediction in complex networks: A survey," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 390(6), pages 1150-1170.
    5. Barabási, Albert-László & Albert, Réka & Jeong, Hawoong, 1999. "Mean-field theory for scale-free random networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 272(1), pages 173-187.
    6. Ying Duan & Xiuwen Fu & Wenfeng Li & Yu Zhang & Giancarlo Fortino, 2017. "Evolution of Scale-Free Wireless Sensor Networks with Feature of Small-World Networks," Complexity, Hindawi, vol. 2017, pages 1-15, July.
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