Performance analysis of real-time detection in fusion-based sensor networks

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journal

20 Scopus Citations
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Detail(s)

Original languageEnglish
Article number5703078
Pages (from-to)1564-1577
Journal / PublicationIEEE Transactions on Parallel and Distributed Systems
Volume22
Issue number9
Publication statusPublished - 2011

Abstract

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. © 2006 IEEE.

Research Area(s)

  • Data fusion, performance limits, real-time intrusion detection, wireless sensor networks