In this paper3 , we use Bayesian Networks as a means for unsupervised learning and anomaly (event) detection in gas monitoring sensor networks for underground coal mines. We show t...
X. Rosalind Wang, Joseph T. Lizier, Oliver Obst, M...
This study addresses the problem of k-connectivity of a wireless multihop network consisting of randomly placed nodes with a common transmission range, by utilizing empirical regr...
We propose a Polynomial-based scheme that addresses the problem of Event Region Detection (PERD) for wireless sensor networks (WSNs). Nodes of an aggregation tree perform function ...
Torsha Banerjee, Demin Wang, Bin Xie, Dharma P. Ag...
—In this work, for a wireless sensor network (WSN) of n randomly placed sensors with node density λ ∈ [1, n], we study the tradeoffs between the aggregation throughput and gat...