Joint event detection & identification: A clustering based approach for Wireless Sensor Networks

Distributed clustering based techniques have been increasingly employed for outlier detection in Wireless Sensor Networks (WSNs). But despite its numerous advantages such as online and efficient computations and incorporation of spatiotemporal & attribute correlations, clustering has not been studied for event detection & identification, which is essential for smooth and reliable operations of large scale WSNs. This paper introduces the significance of clustering based event detection & identification to the research community. Further, it presents an online technique for joint event detection and identification that achieves a very high performance for synthetic and real data sets with a significant reduction in computational complexity as compared to the state-of-the-art techniques. A remarkable advantage of the proposed technique is that it can identify the key attributes in the ascending order of their contribution towards an event without incurring any additional complexity.