A scalable correlation‐based approach for outlier detection in wireless body sensor networks. (31st January 2019) - Saneja, Bharti; Rani, Rinkle
Summary: Outlier detection is one of the prominent research domain in the field of data mining and big data analytics. Nowadays, most of the data in healthcare centers are remotely monitored and are generated from different wireless sensors. The core objective of outlier detection in this domain is the recognition of the true physiologically anomalous data and the anomalies because of faulty sensors. In real healthcare monitoring scenario, various sensors are related to each other. So, while detecting outliers in wireless body sensor networks (WBSNs), correlation among different sensor nodes is of major concern. Most of the existing outlier detection techniques consider the sensors to be linearly correlated, which may not always be the case in real life applications. The traditional techniques for outlier detection are also not scalable to big data. To address the above issues, in this paper, we propose an approach for outlier detection that is scalable to big data and also handles the nonlinearly correlated attributes efficiently. The proposed approach is implemented on Hadoop map reduce framework for the rapid processing of big data. The evaluation results are validated using the simulated dataset of WBSNs taken from the Physionet library. The results are compared with various existing outlier detection approaches and demonstrated that the proposed approach is more effective in spotting the physiological outliers and sensor anomalies accurately. Abstract : The paper proposed an efficient approach for outlier detection based on correlation that is scalable to big data. The proposed approach considers both linear and nonlinear correlations among attributes. For processing of big data, the approach is implemented on Hadoop map reduce framework. The results are compared with various existing approaches available for outlier detection and it is demonstrated that the proposed approach is more efficient in spotting the physiological outliers and sensor anomalies accurately.