An Online Adaptive Algorithm for Change Detection in Streaming Sensory Data

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 based on a convex combination of two decoupled least mean square windowed filters with differing sizes. Both filters are applied independently on 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.