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A Reliability-Oriented Local-Area Model for Large-Scale Wireless Sensor Networks

机译:面向大规模无线传感器网络的面向可靠性的局部区域模型

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摘要

Large-scale wireless sensor networks (WSNs) have demonstrated some complex features which are similar to those of other types of complex networks, such as social networks. Based on these complex features, evolution process and characteristic of WSNs, we represent a WSN topologically by building a suitable model, which is named as the reliability-oriented local-area model (ROLM) and aimed at improving the performance of WSNs. For analyzing the performance of the ROLM, we define the reliability as the probability of that the relative error between the measurement and the true value is equal to or less than epsilon (epsilon >= 0) and proposed a parameter eta to measure the reliability of the network. Based on them, we use eta to analyze the influence of network structure on the reliability, and compared the reliabilities of the ROLM and the existing WSNs. Experiment results prove that the large-scale WSN follows a power-law distribution, and it has scale-free characteristic and small world characteristic. And it also shows that, comparing with existing model, ROLM not only balances energy consumption by limiting the connectivity of each node to prolong the lifetime of the network, but also improves the reliability substantially. And the ROLM can be used to express the topology of reliability-oriented WSNs and analyze the structure preferably.
机译:大规模无线传感器网络(WSN)展示了一些复杂的功能,这些功能与其他类型的复杂网络(如社交网络)相似。基于这些复杂的特征,无线传感器网络的演进过程和特征,我们通过构建合适的模型来拓扑表示无线传感器网络,该模型被称为面向可靠性的局域网模型(ROLM),旨在提高无线传感器网络的性能。为了分析ROLM的性能,我们将可靠性定义为测量值与真实值之间的相对误差等于或小于epsilon(epsilon> = 0)的概率,并提出了一个参数eta来测量ROLM的可靠性。网络。基于它们,我们使用eta分析网络结构对可靠性的影响,并比较ROLM和现有WSN的可靠性。实验结果表明,大规模无线传感器网络遵循幂律分布,具有无标度特征和小世界特征。并且还表明,与现有模型相比,ROLM不仅通过限制每个节点的连接性来平衡能耗,从而延长了网络的寿命,而且还大大提高了可靠性。 ROLM可以用来表示面向可靠性的WSN的拓扑结构,并优选地分析其结构。

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  • 来源
    《Mathematical Problems in Engineering》 |2015年第6期|923692.1-923692.17|共17页
  • 作者单位

    Univ Waterloo, Waterloo, ON N2L 3G1, Canada.;

    Northeastern Univ, Shenyang 110819, Liaoning, Peoples R China.;

    Northeastern Univ, Shenyang 110819, Liaoning, Peoples R China.;

    Northeastern Univ, Shenyang 110819, Liaoning, Peoples R China.;

    Northeastern Univ, Shenyang 110819, Liaoning, Peoples R China.;

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