IDEAS home Printed from https://ideas.repec.org/a/eee/phsmap/v596y2022ics0378437122001820.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378437122001820
    Download Restriction: Full text for ScienceDirect subscribers only. Journal offers the option of making the article available online on Science direct for a fee of $3,000
    File URL: https://libkey.io/10.1016/j.physa.2022.127182?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. 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.
    2. 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).
    3. 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.
    4. 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.
    5. 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.
    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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. 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.
    2. Luo, Xiaojuan & Hu, Yuhen & Zhu, Yu, 2014. "Topology evolution model for wireless multi-hop network based on socially inspired mechanism," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 416(C), pages 639-650.
    3. Xiuwen Fu & Yongsheng Yang & Haiqing Yao, 2018. "Analysis on Invulnerability of Wireless Sensor Network towards Cascading Failures Based on Coupled Map Lattice," Complexity, Hindawi, vol. 2018, pages 1-14, January.
    4. Jiang, Nan, 2014. "WDEM: Weighted dynamics and evolution models for energy-constrained wireless sensor networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 404(C), pages 323-331.
    5. Zhou, Bin & He, Zhe & Wang, Nianxin & Wang, Bing-Hong, 2016. "A method of characterizing network topology based on the breadth-first search tree," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 450(C), pages 682-686.
    6. Liang, Wei & Shi, Yuming & Huang, Qiuling, 2014. "Modeling the Chinese language as an evolving network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 393(C), pages 268-276.
    7. Yan Qiang & Bo Pei & Weili Wu & Juanjuan Zhao & Xiaolong Zhang & Yue Li & Lidong Wu, 2014. "Improvement of path analysis algorithm in social networks based on HBase," Journal of Combinatorial Optimization, Springer, vol. 28(3), pages 588-599, October.
    8. Chen, Ling-Jiao & Zhang, Zi-Ke & Liu, Jin-Hu & Gao, Jian & Zhou, Tao, 2017. "A vertex similarity index for better personalized recommendation," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 466(C), pages 607-615.
    9. Stephanie Rend'on de la Torre & Jaan Kalda & Robert Kitt & Juri Engelbrecht, 2016. "On the topologic structure of economic complex networks: Empirical evidence from large scale payment network of Estonia," Papers 1602.04352, arXiv.org.
    10. Gabrielle Demange, 2012. "On the influence of a ranking system," Social Choice and Welfare, Springer;The Society for Social Choice and Welfare, vol. 39(2), pages 431-455, July.
    11. Dong-Rui Chen & Chuang Liu & Yi-Cheng Zhang & Zi-Ke Zhang, 2019. "Predicting Financial Extremes Based on Weighted Visual Graph of Major Stock Indices," Complexity, Hindawi, vol. 2019, pages 1-17, October.
    12. Wei, Daijun & Deng, Xinyang & Zhang, Xiaoge & Deng, Yong & Mahadevan, Sankaran, 2013. "Identifying influential nodes in weighted networks based on evidence theory," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(10), pages 2564-2575.
    13. Tsao, J.Y. & Boyack, K.W. & Coltrin, M.E. & Turnley, J.G. & Gauster, W.B., 2008. "Galileo's stream: A framework for understanding knowledge production," Research Policy, Elsevier, vol. 37(2), pages 330-352, March.
    14. Pier Paolo Saviotti, 2011. "Knowledge, Complexity and Networks," Chapters, in: Cristiano Antonelli (ed.), Handbook on the Economic Complexity of Technological Change, chapter 6, Edward Elgar Publishing.
    15. Duan, Shuyu & Wen, Tao & Jiang, Wen, 2019. "A new information dimension of complex network based on Rényi entropy," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 516(C), pages 529-542.
    16. Chung-Yuan Huang & Chuen-Tsai Sun & Hsun-Cheng Lin, 2005. "Influence of Local Information on Social Simulations in Small-World Network Models," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 8(4), pages 1-8.
    17. Weihua Lei & Luiz G. A. Alves & Luís A. Nunes Amaral, 2022. "Forecasting the evolution of fast-changing transportation networks using machine learning," Nature Communications, Nature, vol. 13(1), pages 1-12, December.
    18. Wang, Jianrong & Wang, Jianping & Han, Dun, 2017. "Nonlinear dynamic evolution and control in CCFN with mixed attachment mechanisms," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 466(C), pages 120-132.
    19. Sun, Bingbin & Yao, Jialing & Xi, Lifeng, 2019. "Eigentime identities of fractal sailboat networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 520(C), pages 338-349.
    20. Leto Peel & Tiago P. Peixoto & Manlio De Domenico, 2022. "Statistical inference links data and theory in network science," Nature Communications, Nature, vol. 13(1), pages 1-15, December.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:phsmap:v:596:y:2022:i:c:s0378437122001820. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/physica-a-statistical-mechpplications/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.