A Kalman Filter Based Link Quality Estimation Scheme for Wireless Sensor Networks
A Kalman Filter Based Link Quality Estimation Scheme for Wireless Sensor Networks
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DOI：
10.1109/glocom.2007.169
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发表时间：
2007-12
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影响因子：
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通讯作者：
Murat Senel;Krishna Chintalapudi;Dhananjay Lal;Abtin Keshavarzian;E. Coyle
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作者：
Murat Senel;Krishna Chintalapudi;Dhananjay Lal;Abtin Keshavarzian;E. Coyle
采用廉价低功耗收发器的无线传感器节点之间的通信通常对无线信道的变化非常敏感。因此，传感器网络路由协议努力不断地适应无线链路中的时间变化，以避免在低质量链路上的浪费传输。这样的自适应路由协议必须依赖于一个方案，不仅可以准确地估计无线链路的质量的定量措施，如分组成功率（PSR），但也迅速适应时间动态的链路。传统上，PSR是根据测试分组窗口上成功传输的分数来估计的。然而，我们证明，计数为基础的方法不作出反应，在无线信道的变化不够快，解决这个问题的唯一方法是估计PSR的基础上接收机的特性和在接收机的信噪比（SNR）。因此，我们提出了一个计划，使用预先校准的SNR-PSR关系和瞬时SNR估计来计算PSR的链路。在我们的方案中，每个接收机使用卡尔曼滤波器连续跟踪SNR以最小化估计误差，并使用本地可用的SNR-PSR曲线来估计PSR。通过大量的实验，我们证明，我们的计划适应变化的信道比计数为基础的PSR估计，它也提供了更好的PSR估计比这些计数为基础的方法。
Communication among wireless sensor nodes that employ cheap low-power transceivers is often very sensitive to the variations of the wireless channel. Sensor network routing protocols thus strive to continually adapt to temporal variations in wireless links in order to avoid wasteful transmissions over low-quality links. Such adaptive routing protocols must rely on a scheme that can not only accurately estimate the quality of wireless links in terms of a quantitative measure, such as the packet success rate (PSR), but also quickly adapt to temporal dynamics of the links. Traditionally, the PSR is estimated from the fraction of successful transmissions over a window of test- packets. However, we demonstrate that counting based methods do not react to changes in the wireless channel fast enough and that the only way to address this problem is to estimate the PSR based on the receiver's characteristics and on the signal to noise ratio (SNR) at the receiver. We thus propose a scheme that uses a pre-calibrated SNR-PSR relationship and instantaneous SNR estimates to calculate the PSR of the link. In our scheme, each receiver continuously tracks the SNR using a Kalman Filter to minimize the estimation error and uses a locally available SNR- PSR curve to estimate the PSR. Through extensive experiments we demonstrate that our scheme adapts to variations in the channel faster than counting-based PSR estimators and that it also provides better PSR estimates than these counting-based approaches.