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Accuracy-Aware Interference Modeling and Measurement in Wireless Sensor Networks

机译:无线传感器网络中的精度感知干扰建模和测量

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Wireless sensor networks (WSNs) are increasingly deployed for mission-critical applications such as emergency management and health care, which impose stringent requirements on the communication performance of WSNs. To support these applications, it is crucial to model and measure the effect of wireless interference, which is the major factor that limits WSN performance. Accurate modeling and measurement of interference faces two key challenges. First, as shown in our experimental results, interference yields considerable spatial and temporal variations of WSN performance, which poses a major challenge for measurement at rum-time. Second, in the unlicensed band, the communication of WSN is interfered by coexisting wireless devices such as smartphones and laptops equipped with 802.11 radios, which lead to cross-technology interference that are difficult to characterize due to the heterogeneous PHY. To tackle these challenges, this paper presents a novel approach to interference modeling and measurement for WSNs. First, we propose a new regression-based interference model and analytically characterize its accuracy based on statistics theory. Second, we develop a novel protocol called for measuring the proposed interference model with at run time. Third, building on interference modeling, we propose an algorithm that accurately forecasts the performance of WSNs in the presence of cross-technology interference. Our extensive experiments on a testbed of 17 TelosB motes show that the proposed approaches achieve high accuracy of interference modeling and WSN performance forecasting with significantly lower overhead than state-of-the-art approaches.
机译:无线传感器网络(WSN)越来越多地用于紧急任务和医疗保健等关键任务应用,这对WSN的通信性能提出了严格的要求。为了支持这些应用,建模和测量无线干扰的影响至关重要,这是限制WSN性能的主要因素。干扰的精确建模和测量面临两个关键挑战。首先,如我们的实验结果所示,干扰会产生WSN性能的时空变化,这对朗姆酒时间的测量提出了重大挑战。其次,在无执照频段中,WSN的通信会受到共存的无线设备(如配备802.11无线电的智能手机和笔记本电脑)的干扰,这会导致跨技术干扰,由于异构PHY难以表征。为了解决这些挑战,本文提出了一种新颖的方法来对WSN进行干扰建模和测量。首先,我们提出了一个新的基于回归的干扰模型,并基于统计理论分析性地描述了其准确性。其次,我们开发了一种新颖的协议,用于在运行时测量建议的干扰模型。第三,在干扰建模的基础上,我们提出了一种可以在存在跨技术干扰的情况下准确预测WSN性能的算法。我们在17个TelosB微粒的测试平台上进行的广泛实验表明,与最新方法相比,所提出的方法以较高的开销降低了干扰建模和WSN性能预测的准确性。

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