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Boltzmann Machine Topology Learning for Distributed Sensor Networks Using Loopy Belief Propagation Inference

Distributed sensor networks, as opposed to centralized networks, offer several advantages in terms of versatility and increased safety, which make their use particularly relevant for applications of security surveillance. A challenge of such systems is how to build autonomously a global description of the sensed environment without supervision of a central processing unit and with minimal configuration effort. We present an approach to ubiquitous computing, based on a semantic representation of the world view in terms of correlation of local information learned at the local level. There, a statistical description of the sensed activity is provided. Correlations of events among nodes are learned using a Boltzmann machine approach and used in order to establish neighborhood correspondences. Moreover, the communication between nodes is used to enrich the local description of the sensed environment by approximating the a-posterior distributions by marginal distributions computed with the loopy belief propagation algorithm. We present results of simulations emulating a security surveillance environment in which the sensors are cameras and activity is learned by processing video data.