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首页> 外文期刊>Parallel and Distributed Systems, IEEE Transactions on >Performance Analysis of Real-Time Detection in Fusion-Based Sensor Networks
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Performance Analysis of Real-Time Detection in Fusion-Based Sensor Networks

机译：基于融合的传感器网络实时检测性能分析

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摘要

Real-time detection is an important requirement of many mission-critical wireless sensor network applications such as battlefield monitoring and security surveillance. Due to the high network deployment cost, it is crucial to understand and predict the real-time detection capability of a sensor network. However, most existing real-time analyses are based on overly simplistic sensing models (e.g., the disc model) that do not capture the stochastic nature of detection. In practice, data fusion has been adopted in a number of sensor systems to deal with sensing uncertainty and enable efficient collaboration among resource-limited sensors. However, real-time performance analysis of sensor networks designed based on data fusion has received little attention. In this paper, we bridge this gap by investigating the fundamental real-time detection performance of large-scale sensor networks under stochastic sensing models. In particular, we consider two basic data fusion schemes, i.e., value fusion and decision fusion. Our results show that data fusion is effective in achieving stringent performance requirements such as short detection delay and low false alarm rates. Moreover, value fusion and decision fusion are suitable for low and high signal-to-noise ratio scenarios, respectively. Our results help understand the impact of data fusion and provide important guidelines for the design of real-time wireless sensor networks for intrusion detection. Our analyses are verified through extensive simulations based on both synthetic data sets and data traces collected in a real deployment for vehicle detection. The results show that data fusion can reduce the network density by about 60 percent compared with the disc model while detecting any intruder within one detection period at a false alarm rate lower than five percent.
机译：实时检测是许多关键任务无线传感器网络应用（例如战场监视和安全监视）的重要要求。由于网络部署成本高昂，因此了解和预测传感器网络的实时检测能力至关重要。但是，大多数现有的实时分析是基于过于简单的感测模型（例如，盘模型），其不能捕获检测的随机性。实际上，数据融合已在许多传感器系统中采用，以处理感测不确定性并实现资源有限的传感器之间的有效协作。但是，基于数据融合设计的传感器网络的实时性能分析很少受到关注。在本文中，我们通过研究随机传感模型下大型传感器网络的基本实时检测性能来弥合这一差距。特别地，我们考虑两种基本的数据融合方案，即值融合和决策融合。我们的结果表明，数据融合可有效满足严格的性能要求，例如较短的检测延迟和较低的误报率。此外，值融合和决策融合分别适用于低信噪比和高信噪比的场景。我们的结果有助于理解数据融合的影响，并为设计用于入侵检测的实时无线传感器网络提供重要指导。我们的分析通过基于合成数据集和实际部署中收集的用于车辆检测的数据迹线的广泛模拟进行了验证。结果表明，与磁盘模型相比，数据融合可以将网络密度降低约60％，同时在一个检测周期内以低于5％的误报率检测到任何入侵者。

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