Boltzmann Machine Topology Learning for Distributed Sensor Networks Using Loopy Belief Propagation Inference
Cristina Picus, L. Cambrini, Wolfgang Herzner · 2008
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.