Adaptive calibration for fusion-based wireless sensor networks
INFOCOM'10 Proceedings of the 29th conference on Information communications
Maintaining data temporal consistency in distributed real-time systems
Real-Time Systems
An efficient method for cleaning dirty-events over uncertain data in WSNs
Journal of Computer Science and Technology - Special issue on Natural Language Processing
Adaptive calibration for fusion-based cyber-physical systems
ACM Transactions on Embedded Computing Systems (TECS)
Fusion-based volcanic earthquake detection and timing in wireless sensor networks
ACM Transactions on Sensor Networks (TOSN)
| Hi-index | 0.00 |
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 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 signal-to-noise ratios (SNRs). Data fusion can reduce the network density by about 60% compared with the disc model while detecting any intruder within one detection period at a false alarm rate lower than 2%. In contrast, the disc model is only suitable when the 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.