Recursive online EM algorithm for adaptive sensor deployment and boundary estimation in sensor networks

More and more sensor networks are required to monitor and track a large number of objects. Since the topology of mass objects is often dynamic in the real world, their boundary estimation and sensor deployment should be conducted in an adaptive manner. The "current" locations of objects detected by sensors are deemed as new observations into stochastic learning process through recursive distributed EM (Expectation-Maximization) algorithm. This. paper first builds a probabilistic Gaussian Mixture model to estimate the mixture distribution of objects locations and then proposes a novel methodology to optimize the sensor deployment and estimate the boundary of objects locations dynamically.