大型无线传感器网络寿命和网络质量集成模型

Ermanno Battista, V. Casola, S. Marrone, N. Mazzocca, Roberto Nardone, V. Vittorini
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引用次数: 3

摘要

本文介绍了一种基于网络拓扑结构和监控应用的大型无线传感器网络的设计和评估建模方法,并考虑了由于功耗而导致的性能下降。该模型由节点的随机活动网络(SAN)模型和整个网络的马尔可夫智能体模型(MAM)组成。SAN模型用于对节点进行性能分析(即测量其采样时间)并评估其平均放电时间。MAM用于将SAN模型分析的结果组合成一个复杂的拓扑感知模型,该模型能够评估PDR (Packet Delivery Ratio)和网络功耗。对MAM所具有的空间分布的相互依赖性进行建模的可能性,使集成模型成为评估不同设计选择和执行有意义的假设分析的具体、可扩展的手段。通过将分析结果与实际节点值进行比较,验证了模型的有效性,并给出了搭载TinyOs的TelosB节点的实验结果。
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An integrated lifetime and network quality model of large WSNs
This paper introduces a modeling approach to the design and evaluation of large wireless sensor networks against the topology of the network and the monitoring application and taking into account the performance degradation due to the power consumption. The model is built by composing Stochastic Activity Network (SAN) models of the nodes and a Markovian Agent Model (MAM) of the whole network. The SAN models are used to conduct a performance analysis of the nodes (i.e. to measure their sampling time) and evaluate their mean time to discharge. The MAM is used to compose the results of the SAN model analysis into a complex topology-aware model able to evaluate the Packet Delivery Ratio (PDR) and the power consumption of the network. The possibility to model spatially distributed interdependencies featured by the MAM makes the integrated model a concrete, scalable mean to evaluate different design choices and perform meaningful what-if analyses. The model has been validated by comparing the analysis results with real node values: specifically we present the experimental results obtained by using TelosB nodes equipped with TinyOs.
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