Outlier Detection in Wireless Sensor Networks Data by Entropy Based K-NN Predictor

Abstract

Anomaly (outlier) detection is plays very significant role in ESN based monitoring application using on large data used for biomedical and defence. Wireless Sensor network monitor environmental parameters (temperature, humidity, pressure, vibration etc). Group of sensor nodes forms a (WSN) and observations collected from these sensor produces low data quality and reliability due to the limited energy, memory, computation capability and bandwidth. The dynamic environment of network and roughness of the working condition are also responsible to generate inaccuracy in measurements. In this paper, an approach for outliers detection based entropy value of received sensor voltages is applied using KNN prediction model .The algorithm development and analysis involves a real time database generated on 14 sets of MICA2 wireless sensor kit with anomaly inserted by real time motion based intrusion in the lab by volunteers from Intel Berkeley lab. On each sensor data pair segmentation is applied by fixed window size in order get large outliers’ measurements training dataset. The analysis demonstrates the measurement accuracy in detection of number of outliers that its 86%. Moreover, the algorithm also provides an analysis in terms of impact of variation in distance types and number of nearest neighbours in the KNN prediction model. This work is helpful in the application in the situations where high amount of noise or distortions are present. The outlier part from distorted data can be figured out and recollected to enhance application accuracy.