An integrated framework for anomaly detection in big data of medical wireless sensors

Abstract

Wireless sensor networks (WSNs) are ubiquitous nowadays and have applications in variety of domains such as machine surveillance, precision agriculture, intelligent buildings, healthcare etc. Detection of anomalous activities in such domains has always been a subject undergoing intense study. As the sensor networks are generating tons of data every second, it becomes a challenging task to detect anomalous events accurately from this large amount of data. Most of the existing techniques for anomaly detection are not scalable to big data. Also, sometimes accuracy might get compromised while dealing with such a large amount of data. To address these issues in this paper, a unified framework for anomaly detection in big sensor data has been proposed. The proposed framework is based on data compression and Hadoop MapReduce-based parallel fuzzy clustering. The clusters are further refined for better classification accuracy. The modules of the proposed framework are compared with various existing state-of-art algorithms. For experimental analysis, real sensor data of ICU patients has been taken from the physionet library. It is revealed from the comparative analysis that the proposed framework is more time efficient and shows better classification accuracy.