Efficient localized data modeling techniques in Internet of Things (IoT) applications enable the nodes to change their behavior upon observing events of interest. Additionally, battery‐powered IoT nodes can conserve their energy resources by limiting their data communications to specific events. Despite the real‐time nature of the data collected in the IoT and limited memory and computational resources, most of the current data modeling approaches for the IoT involve batch training. Recently, an online efficient anomaly detection technique called iterative data capture anomaly detection has been proposed for environmental sensing and monitoring applications. However, this approach cannot handle changing environments. So far, efforts in extending this algorithm to adapt to changes in the environment have met with limited success. In this paper, we generalize this algorithm to adapt to changes in the data stream by exponentially weighting past observations. We illustrate the proposed algorithm with numerical results on both real‐life and simulated data sets, which demonstrate the efficiency and accuracy of our approach compared to existing methods.