Decentralized Change Detection in Wireless Sensor Network Using DFT-based Synopsis

Wireless sensor networks are often deployed to detect occurring changes in the environment. Detectability of the changes in the ambient environment contributes to the success of emerging sensor networks. The major challenges in designing change detection algorithms for wireless sensor networks are the restricted resources of sensors such as memory, communication bandwidth, and battery power. We propose a decentralized change detection framework for wireless sensor networks, present a novel algorithm using DFT coefficients as synopsis structures from signal-oriented data streams, which can reduce the amount of local memory required by the change detector while assuring accuracy of local detection. Furthermore, we show how to use a gossip-based framework for transmitting and aggregating the change detection results from the ambient area of events to the sink. This helps to find consensus among nodes in order to improve global detection accuracy while maintaining reasonable communication cost for the wireless sensor networks. We make empirical evaluations to show the effectiveness and the deployment potential of our change detection schemes in wireless sensor networks.