Rui Tan and Guoliang Xing and Benyuan Liu and Jianping Wang
May, 2009
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 (\eg, 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 the collaboration among 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. Our results show that data fusion is effective in achieving stringent performance requirements such as short detection delay and low false alarm rates, especially in the scenarios with low \acp{snr}. Compared with the disc model, data fusion can reduce the network density by about $60\%$ while the false alarm rate is lower than $1\%$ and any intruder can be detected within one detection period. The disc model is only suitable when the \ac{snr} is sufficiently high. 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.
You are granted permission for the non-commercial reproduction, distribution, display, and performance of this technical report in any format.