Convergence of Desynchronization Primitives in Wireless Sensor Networks: A Stochastic Modeling Approach
Convergence of Desynchronization Primitives in Wireless Sensor Networks: A Stochastic Modeling Approach
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DOI：
10.1109/tsp.2014.2369003
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发表时间：
2014-11
影响因子：
5.4
通讯作者：
D. Buranapanichkit;N. Deligiannis;Y. Andreopoulos
中科院分区：
文献类型：
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作者：
D. Buranapanichkit;N. Deligiannis;Y. Andreopoulos
在无线传感器网络中的解扰方法收敛到时分多址（TDMA）的共享介质，而不需要在无线传感器之间的时钟同步，或实际上存在的一个中央（协调器）节点。所有这些方法都是基于周期性“火灾”或“脉冲”广播的反应性监听的原理：每个节点基于从共享给定频谱的剩余节点中的一些接收到的火灾消息来更新其火灾消息广播的时间。在本文中，我们提出了一种新的框架，估计所需的迭代收敛到公平的TDMA调度。我们的估计是从根本上不同于以前的acquitures或界在文献中发现的，第一次，收敛到TDMA的定义在随机意义上。我们的分析结果适用于去同步算法和抑制耦合的脉冲耦合振荡器算法。通过iMote 2 TinyOS节点（基于IEEE 802.15.4标准）以及通过计算机模拟进行的实验评估表明，对于绝大多数设置，我们的随机模型与实验观察到的收敛迭代在一个标准差内。因此，所提出的估计，以表征designization收敛迭代显着优于现有的边界或界限。因此，它们有助于分析理解如何descruization-based系统预计将从随机初始条件发展到descruizized稳定状态。
Desynchronization approaches in wireless sensor networks converge to time-division multiple access (TDMA) of the shared medium without requiring clock synchronization amongst the wireless sensors, or indeed the presence of a central (coordinator) node. All such methods are based on the principle of reactive listening of periodic “fire” or “pulse” broadcasts: each node updates the time of its fire message broadcasts based on received fire messages from some of the remaining nodes sharing the given spectrum. In this paper, we present a novel framework to estimate the required iterations for convergence to fair TDMA scheduling. Our estimates are fundamentally different from previous conjectures or bounds found in the literature as, for the first time, convergence to TDMA is defined in a stochastic sense. Our analytic results apply to the Desync algorithm and to pulse-coupled oscillator algorithms with inhibitory coupling. The experimental evaluation via iMote2 TinyOS nodes (based on the IEEE 802.15.4 standard) as well as via computer simulations demonstrates that, for the vast majority of settings, our stochastic model is within one standard deviation from the experimentally-observed convergence iterations. The proposed estimates are thus shown to characterize the desynchronization convergence iterations significantly better than existing conjectures or bounds. Therefore, they contribute towards the analytic understanding of how a desynchronization-based system is expected to evolve from random initial conditions to the desynchronized steady state.