Adaptive statistical sampling methods for decentralized estimation and detection of localized phenomena

— Sensor networks (SNETs) for monitoring spatial phenomena has emerged as an area of significant practical interest. We focus on the important problem of detection of distributed events, sources, or abnormalities that are localized, i.e., only a small number of sensors in the vicinity of the phenomena are in the field of observation. This problem complements the standard decentralized detection problem, where noisy information about an event is measured by the entire network. For localized phenomena the main difficulty arises from the coupling of: a) noisy sensor observations that lead to local false positives/negatives; and b) limited energy, which constrains communication among sensor nodes. Together these difficulties call for reaching a decentralized statistical ordering based on limited collaboration. We are then led to the following fundamental problem: determine the most probable event locations while minimizing communication cost. Our objective in this paper is to charact...

• Type: Conference
• Year: 2005
• Where: IPSN
• Authors: Erhan Baki Ermis, Venkatesh Saligrama
• Keywords: Communication Cost, IPSN 2005, Sensor, Sensor Network, Sensor Networks