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...
Today it is possible to deploy sensor networks in the real world and collect large amounts of raw sensory data. However, it remains a major challenge to make sense of sensor data, ...
Camera sensor networks--wireless networks of low-power imaging sensors--have become popular recently for monitoring applications. In this paper, we argue that traditional vision-ba...
Purushottam Kulkarni, Prashant J. Shenoy, Deepak G...
While a multithreading approach provides a convenient sensor application developing environment with automatic control flow and stack managment, it is considered to have a larger d...
Methods for node localisation in sensor networks usually rely upon the measurement of received strength, time-of-arrival, and/or angle-of-arrival of an incoming signal. In this pap...