Embedded networked sensors: signal search engine for signal classification

Networked sensors (ENS) provide a distributed monitoring approach for defense systems situational awareness, machine condition based maintenance, health care, transportation, and other applications. The ENS architecture is based on compact, intelligent, networked low-power sensor nodes. Wireless networking enables rapid distribution of sensor nodes in diverse environments. For the general applications considered here, it is energy usage drawn from fixed energy sources that limit sensor node lifetime, and since wireless network interface operations dominate energy usage, it is essential to reduce the demand for high energy wireless data transport. Thus, identification of events and the evaluation of the utility for event data transmission must be performed locally. This, in turn, requires that methods be developed for local signal processing and event detection at the node. The signal search engine (SSE) method reported here, has been developed to enable identification of target type at the site of wireless sensor nodes using acoustic and seismic signal sources and algorithms that are compatible with low power embedded systems. The SSE is "trained" with and relies on data directly collected from the field. The SSE operates with both time domain template matching and wavelet methods for target identification. These methods are evaluated and compared here in the application to data collected in the field. Results obtained from the time domain signal classification scheme and the "wavelet" method give error rates of less than 15% with minimal signal preprocessing.