Impact of Data Fusion on Real-Time Detection in Sensor Networks

—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 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 largescale sensor networks under stochastic sensing models. Our results show that data fusion is effective in achieving stringent performance requirement...

• Type: Conference
• Year: 2009
• Where: RTSS
• Authors: Rui Tan, Guoliang Xing, Benyuan Liu, Jianping Wang
• Keywords: Applied Computing, Data Fusion, RTSS 2009, Sensor Networks, Wireless Sensor Networks