Lightweight Detection and Classification for Wireless Sensor Networks in Realistic Environments

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Lightweight Detection and Classification for Wireless Sensor Networks in Realistic Environments (2005)

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by Lin Gu , Dong Jia , Pascal Vicaire , Ting Yan , Liqian Luo , Ajay Tirumala , Qing Cao , Tian He , John A. Stankovic , Tarek Abdelzaher , Bruce H. Krogh

Venue:	in SenSys
Citations:	99 - 12 self

BibTeX

@INPROCEEDINGS{Gu05lightweightdetection,
    author = {Lin Gu and Dong Jia and Pascal Vicaire and Ting Yan and Liqian Luo and Ajay Tirumala and Qing Cao and Tian He and John A. Stankovic and Tarek Abdelzaher and Bruce H. Krogh},
    title = {Lightweight Detection and Classification for Wireless Sensor Networks in Realistic Environments},
    booktitle = {in SenSys},
    year = {2005},
    pages = {205--217}
}

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Abstract

A wide variety of sensors have been incorporated into a spectrum of wireless sensor network (WSN) platforms, providing flexible sensing capability over a large number of low-power and inexpensive nodes. Traditional signal processing algorithms, however, often prove too complex for energy-and-cost-effective WSN nodes. This study explores how to design efficient sensing and classification algorithms that achieve reliable sensing performance on energy-andcost-effective hardware without special powerful nodes in a continuously changing physical environment. We present the detection and classification system in a cutting-edge surveillance sensor network, which classifies vehicles, persons, and persons carrying ferrous objects, and tracks these targets with a maximum error in velocity of 15%. Considering the demanding requirements and strict resource constraints, we design a hierarchical classification architecture that naturally distributes sensing and computation tasks at

Keyphrases

wireless sensor network realistic environment lightweight detection flexible sensing capability large number maximum error strict resource constraint classification system cutting-edge surveillance sensor network special powerful node energy-andcost-effective hardware physical environment efficient sensing inexpensive node energy-and-cost-effective wsn node classification algorithm demanding requirement ferrous object hierarchical classification architecture wide variety computation task traditional signal processing algorithm

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