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WDEM: Weighted dynamics and evolution models for energy-constrained wireless sensor networks

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  • Jiang, Nan

Abstract

Network dynamics and energy consumption are two important performance issues for energy-constrained Wireless Sensor Networks (WSNs). Current state-of-the-art research is limited to either modeling un-weighted network under some energy constraint or assuming fixed/static network model. In this paper, we offer a systematic study on the relationship between the two performance objectives. By introducing the edge weight and vertex strength, we propose two important evolution models for WSNs. We find a number of important properties associated with weighted dynamics of WSNs. For the case study, we consider two models where the networks are organized in an energy-efficient way, and the weight distribution can be balanced among the sensor nodes. For both of two models, we show that weighted dynamics can be precisely characterized via numerical analysis, and also produce scale-free properties, which can improve fault tolerance of the sensor nodes. Our results offer important references on the performance issues depending on specific application scenarios of WSNs.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:phsmap:v:404:y:2014:i:c:p:323-331
    DOI: 10.1016/j.physa.2014.02.061
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    References listed on IDEAS

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    1. Jo~ao P. da Cruz & Pedro G. Lind, 2010. "Self-organized criticality in a network of economic agents with finite consumption," Papers 1009.5830, arXiv.org, revised Dec 2012.
    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. H. Jeong & S. P. Mason & A.-L. Barabási & Z. N. Oltvai, 2001. "Lethality and centrality in protein networks," Nature, Nature, vol. 411(6833), pages 41-42, May.
    4. 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.
    5. Li, Xiang & Chen, Guanrong, 2003. "A local-world evolving network model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 328(1), pages 274-286.
    6. da Cruz, João P. & Lind, Pedro G., 2012. "Self-organized criticality in a network of economic agents with finite consumption," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(4), pages 1445-1452.
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