Particle filters (PFs) are powerful samplingbased inference/learning algorithms for dynamic Bayesian networks (DBNs). They allow us to treat, in a principled way, any type of prob...
Arnaud Doucet, Nando de Freitas, Kevin P. Murphy, ...
—Dual descent methods are commonly used to solve network optimization problems because their implementation can be distributed through the network. However, their convergence rat...
Michael Zargham, A. Ribeiro, Ali Jadbabaie, Asuman...
— We recently showed for peer-to-peer networks, that having the number of replicas of each object proportional to the request rate for these objects has many per-node advantages....
We present an algorithm that makes an appropriate use of a Kalman filter combined with a geometric computation with respect to the localisation of a pollutant-emitting point sourc...
In this paper, we propose a novel Geographic Multicast routing Protocol (GMP) for wireless sensor networks1 . The proposed protocol is fully distributed and stateless. Given a set...