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Choi, Young-Ri
System Software Lab
Research Interests
  • Computer systems, system software, cloud computing, virtualization

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A line in the sand: a wireless sensor network for target detection, classification, and tracking

Cited 260 times inthomson ciCited 397 times inthomson ci
Title
A line in the sand: a wireless sensor network for target detection, classification, and tracking
Author
Arora, ADutta, PBapat, SKulathumani, VZhang, HNaik, VMittal, VCao, HDemirbas, MGouda, MChoi, Young-RiHerman, TKulkarni, SArumugam, UNesterenko, MVora, AMiyashita, M
Keywords
Reliability; Stabilization; Smart dust; Target classification and tracking; Wireless sensor networks
Issue Date
2004-12
Publisher
ELSEVIER SCIENCE BV
Citation
COMPUTER NETWORKS, v.46, no.5, pp.605 - 634
Abstract
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.
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DOI
10.1016/j.comnet.2004.06.007
ISSN
1389-1286
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EE_Journal Papers
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