A line in the sand: a wireless sensor network for target detection, classification, and tracking
A line in the sand: a wireless sensor network for target detection, classification, and tracking
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DOI：
10.1016/j.comnet.2004.06.007
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发表时间：
2004-12-05
影响因子：
5.6
通讯作者：
Miyashita, M
中科院分区：
文献类型：
--
作者：
Arora, A;Dutta, P;Miyashita, M
入侵检测是一个非常适合于无线传感器网络的具有实际意义的监控问题。本文研究了传感器网络在入侵检测中的应用以及与之相关的目标分类和跟踪问题。我们的方法是基于一个密集的，分布式的，无线网络的多模态资源贫乏的传感器结合成松散的相干传感器阵列，执行原位检测，估计，压缩和渗出。我们接地我们的研究在一个安全的情况下，称为“一条线在沙子”，并相应地定义目标，系统，环境和故障模型。基于场景的性能要求和传感器网络的感知、通信、能量和计算能力，我们探索了传感器、信号处理算法、通信、网络和中间件服务的设计空间。我们引入的影响领域，它可以从一个网络的二进制传感器，作为一种新的分类器的基础估计。我们的工作的一个贡献是，我们不假设一个可靠的网络，相反，我们定量分析网络的不可靠性对应用程序性能的影响。我们的工作包括在佛罗里达州坦帕的麦克迪尔空军基地部署90多个传感器节点的多个实验，以及其他规模相当的现场实验。基于这些经验，我们确定了一组关键的经验教训，并阐明了极端扩展到数万或数十万个传感器节点所面临的一些挑战。(C)2004 Elsevier B.V.保留所有权利。
Intrusion detection is a surveillance problem of practical import that is well suited to wireless sensor networks. In this paper, we study the application of sensor networks to the intrusion detection problem and the related problems of classifying and tracking targets. Our approach is based on a dense, distributed, wireless network of multi-modal resource-poor sensors combined into loosely coherent sensor arrays that perform in situ detection, estimation, compression, and exfiltration. We ground our study in the context of a security scenario called "A Line in the Sand" and accordingly define the target, system, environment, and fault models. Based on the performance requirements of the scenario and the sensing, communication, energy, and computation ability of the sensor network, we explore the design space of sensors, signal processing algorithms, communications, networking, and middleware services. We introduce the influence field, which can be estimated from a network of binary sensors, as the basis for a novel classifier. A contribution of our work is that we do not assume a reliable network; on the contrary, we quantitatively analyze the effects of network unreliability on application performance. Our work includes multiple experimental deployments of over 90 sensor nodes at MacDill Air Force Base in Tampa, FL, as well as other field experiments of comparable scale. Based on these experiences, we identify a set of key lessons and articulate a few of the challenges facing extreme scaling to tens or hundreds of thousands of sensor nodes. (C) 2004 Elsevier B.V. All rights reserved.