In recent years, wireless sensor networks have been extensively deployed to collect various data. Due to the effect of harsh environments and the limitation of the computing and communication capabilities of sensor nodes, the quality and reliability of sensor data are affected by outliers. Thus, an effective outlier detection method is essential. The existing outlier detection methods have some drawbacks, such as extra resource consumption introduced by the size growth of a local detector, poor performance of combination methods of local detectors, and the weak adaptability of the dynamic changes of the environment, etc. We propose an isolation-based distributed outlier detection framework using nearest-neighbor ensembles (iNNE) to effectively detect outliers in wireless sensor networks. In our proposed framework, local detectors are constructed in each node by the iNNE algorithm. A new combination method taking advantage of the spatial correlation among sensor nodes for local detectors is presented. The method is based on the weighted voting idea. In addition, we introduce a sliding window to update local detectors, which enables the adaption of dynamic changes in the environment. The extensive experiments are conducted on two classic real sensor datasets. The experimental results show our framework significantly improves the detection accuracy and reduces the false alarm rate compared with other outlier detection frameworks.
INDEX TERMSOutlier detection, wireless sensor networks (WSN), iforest, local outlier factor (LOF), isolation using nearest neighbor ensembles (iNNE), sliding window.
Wireless sensor networks (WSNs) are extensively deployed to collect various data. Due to harsh environments and limitation of computing and communication capabilities of sensor nodes, the quality and reliability of sensor data are compromised by outliers. With the advent of 5G, sensors tend to generate increasingly more complex data. When faced with big data, traditional outlier detection methods relied on sensor nodes and remote cloud are unable to accord satisfactory performance in terms of delay and energy consumption. To address this problem, we propose a mobile edge-cloud collaboration outlier detection framework. Outlier detection is performed by edge nodes between the remote cloud and the underlying WSNs, while the training and updating of detection model are conducted on the cloud. A fast angle-based outlier detection method is developed to obtain training data. The detection model is constructed based on support vector data description. An online learning-based iterative optimization scheme is devised to update the detection model. Besides, a fuzzy concept is incorporated into the detection model to alleviate the problem of loose decision boundary. Extensive experiments are conducted on real-world data set. Simulation results show that our model is superior to three popular methods in terms of delay and energy consumption. In addition, when the percentage of operational nodes is 60%, our proposal prolongs the network lifetime by 14.2% to 69.8% compared to the three methods. 1 How to cite this article: Gao C, et al.: A mobile edge-cloud collaboration outlier detection framework in wireless sensor networks. IET Commun. 1-14 (2021).
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