An Online Adaptive Algorithm for Change Detection in Streaming Sensory Data

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(From the article abstract)
There has been a keen interest in detecting abrupt sequential changes in streaming data obtained from sensors in wireless sensor networks for Internet of Things applications, such as fire/fault detection, activity recognition, and environmental monitoring.
Such applications require (near) online detection of instantaneous changes. This paper proposes an online, adaptive filtering-based change detection (OFCD) algorithm. Our method is basedon a convex combination of two decoupled least mean square windowed filters with differing sizes. Both filters are applied independentlyon data streams obtained from sensor nodes such that their convex combination parameter is employed as an indicator of abrupt changes in mean values. An extension of our method (OFCD) based on a cooperative scheme between multiple sensors (COFCD) is also presented. It provides an enhancement of both convergence and steady-state accuracy of the convex weight parameter.
Our conducted experiments show that our approach can be applied in distributed networks in an online fashion. It also provides better performance and less complexity compared with the state-of-the-art on both of single and multiple sensors.

This article is a contribution from the IoTCrawler partner University of Surrey for IEEE SYSTEMS JOURNAL.

Yasmin Fathy, Payam Barnaghi and Rahim Tafazolli

University College London and University of Surrey

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