The sensor network paradigm uses multiple autonomous sensor nodes to cooperatively construct an ad hoc computational entity. The authors have implemented applications using this approach with a network of sensor prototypes. In the paper, entity tracking is described as a two-tier process: ( i) clusters of nodes locally estimate parameters used in entity tracking (i.e. time, class, position, and heading), and ( ii) local parameter association forms inter-cluster tracks. Ad hoc routing primitives are used. This supports self-organization at all levels. Derivations of entity tracking applications are given along with preliminary results.
Autonomous networks of sensor platforms can be designed to interact in dynamic and noisy environments to determine the occurrence of specified transient events that define the dynamic process of interest. For example, a sensor network may be used for battlefield surveillance with the purpose of detecting, identifying, and tracking enemy activity. When the number of nodes is large, human oversight and control of low-level operations is not feasible. Coordination and self-organization of multiple autonomous nodes is necessary to maintain connectivity and sensor coverage and to combine information for better understanding the dynamics of the environment. Resource conservation requires adaptive clustering in the vicinity of the event. This paper presents methods for dynamic distributed signal processing using an ad hoc mobile network of microsensors to detect, identify, and track targets in noisy environments. They seamlessly integrate data from fixed and mobile platforms and dynamically organize platforms into clusters to process local data along the trajectory of the targets. Local analysis of sensor data is used to determine a set of target attribute values and classify the target. Sensor data from a field test in the Marine base at Twentynine Palms, Calif, was analyzed using the techniques described in this paper. The results were compared to "ground truth" data obtained from GPS receivers on the vehicles.
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.
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