IDEAS home Printed from https://ideas.repec.org/a/sae/intdis/v13y2017i6p1550147717713626.html

Scale-free topology optimization for software-defined wireless sensor networks: A cyber-physical system

Author

Listed:
  • Ru Huang
  • Xiaoli Chu
  • Jie Zhang
  • Yu Hen Hu

Abstract

Due to the limited resource and vulnerability in wireless sensor networks, maximizing the network lifetime and improving network survivability have become the top priority problem in network topology optimization. This article presents a wireless sensor networks topology optimization model based on complex network theory and cyber-physical systems using software-defined wireless sensor network architecture. The multiple-factor-driven virtual force field and network division–oriented particle swarm algorithm are introduced into the deployment strategy of super-node for the implementation in wireless sensor networks topology initialization, which help to rationally allocate heterogeneous network resources and balance the energy consumption in wireless sensor networks. Furthermore, the preferential attachment scheme guided by corresponding priority of crucial sensors is added into scale-free structure for optimization in topology evolution process and for protection of vulnerable nodes in wireless sensor networks. Software-defined wireless sensor network–based functional architecture is adopted to optimize the network evolution rules and algorithm parameters using information cognition and flow-table configure mode. The theoretical analysis and experimental results demonstrate that the proposed wireless sensor networks topology optimization model possesses both the small-world effect and the scale-free property, which can contribute to extend the lifetime of wireless sensor networks with energy efficiency and improve the robustness of wireless sensor networks with structure invulnerability.

Suggested Citation

  • Ru Huang & Xiaoli Chu & Jie Zhang & Yu Hen Hu, 2017. "Scale-free topology optimization for software-defined wireless sensor networks: A cyber-physical system," International Journal of Distributed Sensor Networks, , vol. 13(6), pages 15501477177, June.
  • Handle: RePEc:sae:intdis:v:13:y:2017:i:6:p:1550147717713626
    DOI: 10.1177/1550147717713626
    as

    Download full text from publisher

    File URL: https://journals.sagepub.com/doi/10.1177/1550147717713626
    Download Restriction: no

    File URL: https://libkey.io/10.1177/1550147717713626?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
    ---><---

    References listed on IDEAS

    as
    1. Ru Huang & Xiaoli Chu & Jie Zhang & Yu Hen Hu, 2015. "Energy-Efficient Monitoring in Software Defined Wireless Sensor Networks Using Reinforcement Learning: A Prototype," International Journal of Distributed Sensor Networks, , vol. 11(10), pages 360428-3604, October.
    2. Barabási, Albert-László & Ravasz, Erzsébet & Vicsek, Tamás, 2001. "Deterministic scale-free networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 299(3), pages 559-564.
    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. Zhong, Xiang & Liu, Jiajun & Gao, Yong & Wu, Lun, 2017. "Analysis of co-occurrence toponyms in web pages based on complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 466(C), pages 462-475.
    2. Blagus, Neli & Šubelj, Lovro & Bajec, Marko, 2012. "Self-similar scaling of density in complex real-world networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(8), pages 2794-2802.
    3. Razdan, Ashok, 2013. "Networks in extensive air showers," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(4), pages 982-986.
    4. Gao, Yan & Liu, Gengyuan & Casazza, Marco & Hao, Yan & Zhang, Yan & Giannetti, Biagio F., 2018. "Economy-pollution nexus model of cities at river basin scale based on multi-agent simulation: A conceptual framework," Ecological Modelling, Elsevier, vol. 379(C), pages 22-38.
    5. Blasi, Monica Francesca & Casorelli, Ida & Colosimo, Alfredo & Blasi, Francesco Simone & Bignami, Margherita & Giuliani, Alessandro, 2005. "A recursive network approach can identify constitutive regulatory circuits in gene expression data," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 348(C), pages 349-370.
    6. Katz, J. Sylvan, 2006. "Indicators for complex innovation systems," Research Policy, Elsevier, vol. 35(7), pages 893-909, September.
    7. Hollingshad, Nicholas W. & Turalska, Malgorzata & Allegrini, Paolo & West, Bruce J. & Grigolini, Paolo, 2012. "A new measure of network efficiency," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(4), pages 1894-1899.
    8. Zhang, Yue & Huang, Ning & Xing, Liudong, 2016. "A novel flux-fluctuation law for network with self-similar traffic," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 452(C), pages 299-310.
    9. Zhang, Zhongzhi & Rong, Lili & Comellas, Francesc, 2006. "High-dimensional random Apollonian networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 364(C), pages 610-618.
    10. Sun, Lina & Huang, Ning & Li, Ruiying & Bai, Yanan, 2019. "A new fractal reliability model for networks with node fractal growth and no-loop," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 514(C), pages 699-707.
    11. Yinhu Zhai & Jia-Bao Liu & Shaohui Wang, 2017. "Structure Properties of Koch Networks Based on Networks Dynamical Systems," Complexity, Hindawi, vol. 2017, pages 1-7, March.
    12. Farkas, I & Derényi, I & Jeong, H & Néda, Z & Oltvai, Z.N & Ravasz, E & Schubert, A & Barabási, A.-L & Vicsek, T, 2002. "Networks in life: scaling properties and eigenvalue spectra," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 314(1), pages 25-34.
    13. Tachimori, Yutaka & Iwanaga, Hiroaki & Tahara, Takashi, 2013. "The networks from medical knowledge and clinical practice have small-world, scale-free, and hierarchical features," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(23), pages 6084-6089.
    14. Guillaume, Jean-Loup & Latapy, Matthieu, 2006. "Bipartite graphs as models of complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 371(2), pages 795-813.
    15. Chen, Mu & Yu, Boming & Xu, Peng & Chen, Jun, 2007. "A new deterministic complex network model with hierarchical structure," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 385(2), pages 707-717.
    16. Zheng, Xiaolong & Zeng, Daniel & Li, Huiqian & Wang, Feiyue, 2008. "Analyzing open-source software systems as complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 387(24), pages 6190-6200.
    17. Ye, Dandan & Dai, Meifeng & Sun, Yu & Su, Weiyi, 2017. "Average weighted receiving time on the non-homogeneous double-weighted fractal networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 473(C), pages 390-402.
    18. Dai, Meifeng & Shao, Shuxiang & Su, Weiyi & Xi, Lifeng & Sun, Yanqiu, 2017. "The modified box dimension and average weighted receiving time of the weighted hierarchical graph," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 475(C), pages 46-58.
    19. Sylvan Katz, 2005. "Indicators for Complex Innovation Systems," SPRU Working Paper Series 134, SPRU - Science Policy Research Unit, University of Sussex Business School.
    20. Dangalchev, Chavdar, 2004. "Generation models for scale-free networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 338(3), pages 659-671.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    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:sae:intdis:v:13:y:2017:i:6:p:1550147717713626. 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: SAGE Publications (email available below). General contact details of provider: .

    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.