Accuracy-Aware Interference Modeling and Measurement in Wireless Sensor Networks
Accuracy-Aware Interference Modeling and Measurement in Wireless Sensor Networks
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DOI：
10.1109/icdcs.2011.47
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发表时间：
2011-06
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影响因子：
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通讯作者：
Jun Huang;Shucheng Liu;G. Xing;Hongwei Zhang;Jianping Wang;Liusheng Huang
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作者：
Jun Huang;Shucheng Liu;G. Xing;Hongwei Zhang;Jianping Wang;Liusheng Huang
无线传感器网络（WSNs）越来越多地用于紧急管理和医疗保健等关键任务应用。为了满足对通信性能的严格要求，了解传感器节点之间复杂的无线干扰是至关重要的。最近的经验研究表明，分组级干扰模型，也被称为分组接收比（PRR）对SINR模型或PRR-SINR模型，提供了比诸如盘模型的其他简单模型显著改进的真实性。然而，如我们的实验结果所示，PRR-SINR模型在现实中产生相当大的空间和时间变化，这对运行时的准确测量构成了重大挑战。本文提出了一种新的精度感知的方法来干扰建模和测量的无线传感器网络。首先，我们提出了一种新的基于回归的PRR-SINR模型，并分析了其准确性的基础上统计理论。其次，我们开发了一种新的协议，称为精度感知的干扰测量（AIM），用于测量所提出的PRR-SINR模型，在运行时具有保证的精度。AIM还采用了新的时钟校准和网内聚合技术，以减少干扰测量的开销。我们在17节点的TelosB微尘测试平台上进行的大量实验表明，AIM实现了PRR-SINR建模的高精度，并且开销比最先进的方法低得多。
Wireless Sensor Networks (WSNs) are increasingly available for mission-critical applications such as emergency management and health care. To meet the stringent requirements on communication performance, it is crucial to understand the complex wireless interference among sensor nodes. Recent empirical studies suggest that the packet-level interference model, also referred to as the packet reception ratio (PRR) versus SINR model or PRR-SINR model, offers significantly improved realism than other simplistic models such as the disc model. However, as shown in our experimental results, the PRR-SINR model yields considerable spatial and temporal variations in reality, which poses a major challenge for accurate measurement at run time. This paper presents a novel accuracy-aware approach to interference modeling and measurement for WSNs. First, we propose a new regression-based PRR-SINR model and analytically characterize its accuracy based on statistics theory. Second, we develop a novel protocol called accuracy-aware interference measurement (AIM) for measuring the proposed PRR-SINR model with assured accuracy at run time. AIM also adopts new clock calibration and in-network aggregation techniques to reduce the overhead of interference measurement. Our extensive experiments on a 17-node testbed of TelosB motes show that AIM achieves high accuracy of PRR-SINR modeling with significantly lower overhead than state of the art approaches.