The energy scaling laws of multihop data fusion networks for distributed inference are considered. The fusion network consists of randomly located sensors independently distributed...
Animashree Anandkumar, Joseph E. Yukich, Lang Tong...
Classical dynamic Bayesian networks (DBNs) are based on the homogeneous Markov assumption and cannot deal with heterogeneity and non-stationarity in temporal processes. Various ap...
Recommender systems are important to help users select relevant and personalised information over massive amounts of data available. We propose an unified framework called Prefer...
Sensor network applications face continuously changing environments, which impose varying processing loads on the sensor node. This paper presents an online control method which a...
We present a formal framework to evaluate stochastic properties of MANET protocols. It captures the interplay between stochastic behavior of protocols deployed at different network...