Unsupervised anomaly detection for unlabelled wireless sensor networks data

With the advances in sensor technology, sensor nodes, the tiny yet powerful device are used to collect data from the various domain. As the sensor nodes communicate continuously from the target areas to base station, hundreds of thousands of data are collected to be used for the decision making. Unfortunately, the big amount of unlabeled data collected and stored at the base station. In most cases, data are not reliable due to several reasons. Therefore, this paper will use the unsupervised one-class SVM (OCSVM) to build the anomaly detection schemes for better decision making. Unsupervised OCSVM is preferable to be used in WSNs domain due to the one class of data training is used to build normal reference model. Furthermore, the dimension reduction is used to minimize the resources usage due to resource constraint incurred in WSNs domain. Therefore one of the OCSVM variants namely Centered Hyper-ellipsoidal Support Vector Machine (CESVM) is used as classifier while Candid-Covariance Free Incremental Principal Component Analysis (CCIPCA) algorithm is served as dimension reduction for proposed anomaly detection scheme. Environmental dataset collected from available WSNs data is used to evaluate the performance measures of the proposed scheme. As the results, the proposed scheme shows comparable results for all datasets in term of detection rate, detection accuracy and false alarm rate as compared with other related methods

Self-Orienting Wireless Multimedia Sensor Networks for Maximizing Multimedia Coverage

Abstract—The performance of a wireless multimedia sensor network (WMSN) is tightly coupled with the pose of individual multimedia sensors. In particular, orientation of an individual multimedia sensor (direction of its sensing unit) is of great importance for the sensor network applications in order to capture the entire image of the field. In this paper, we study the problem of self-orientation in a wireless multimedia sensor network, that is finding the most beneficial pose of multimedia sensors to maximize multimedia coverage with occlusion-free viewpoints. We first propose a distributed algorithm to detect a node’s multimedia coverage and then determine its orientation, while minimizing the effect of occlusions and total overlapping regions in the sensing field. Our approach enables multimedia sensor nodes to compute their directional coverage, provisioning self-configurable sensor orientations in an efficient way. Simulations show that using distributed messaging and self-orientation having occlusion-free viewpoints significantly increase the multimedia coverage. I