A Novel Distributed Online Anomaly Detection Method in Resource-Constrained Wireless Sensor Networks

In this paper, a novel distributed online anomaly detection method in resource-constrained WSNs was proposed. Firstly, the spatiotemporal correlation existing in the sensed data was exploited and a series of single anomaly detectors were built in each distributed deployment sensor node based on ensemble learning theory. Secondly, these trained detectors were broadcasted to the member sensor nodes in the cluster, combining with its trained detector, and the initial ensemble detector was built. Thirdly, considering resources-constrained WSNs, ensemble pruning based on biogeographical based optimization (BBO) was employed in the cluster head node to obtain an optimized subset of ensemble members. Further, the pruned ensemble detector coded by the state matrix was broadcasted to each member sensor nodes for the distributed online global anomaly detection. Finally, the experiments operated on a real WSN dataset demonstrated the effectiveness of the proposed method.