Online unsupervised event detection in wireless sensor networks

Event detection applications of wireless sensor networks (WSNs) highly rely on accurate and timely detection of out of ordinary situations. Majority of the existing event detection techniques designed for WSNs have focused on detection of events with known patterns requiring a priori knowledge about events being detected. In this paper, however, we propose an online unsupervised event detection technique for detection of unknown events. Traditional unsupervised learning techniques cannot directly be applied in WSNs due to their high computational and memory complexities. To this end, by considering specific resource limitations of the WSNs we modify the standard K-means algorithm in this paper and explore its applicability for online and fast event detection in WSNs. For performance evaluation, we investigate event detection accuracy, false alarm, similarity calculation (using the Rand Index), computational and memory complexity of the proposed approach on two real datasets.