Semantic information fusion for coordinated signal processing in mobile sensor networks

Distributed cognition of dynamic processes is commonly observed in mobile groups of animates like schools of fish, hunting lions, or in human teams for sports or military maneuvers. This paper presents methods for dynamic distributed cognition using an ad hoc mobile network of microsensors to detect, identify and track targets in noisy environments. We develop off-line algorithms for aggregating the most appropriate knowledge abstractions into semantic information, which is then used for on-line fusion of relevant attributes observed by local clusters in the sensor network. Local analysis of time series of sensor data yields aggregated semantic information, which is exchanged across nodes for higher level distributed cognition. This eliminates the need for exchanging high volumes of signal data and, thus reduces bandwidth and energy requirements for battery powered microsensors.