A Supervised Technique to Ensure Data Reliability in Wireless Sensor Networks

In Wireless Sensor Network (WSN), sensors at different locations can generate streaming data, which can be analyzed in real-time to identify events of interest. WSN usually have limited energy and transmission capacity, which cannot match the transmission of a large number of data collected by sensor nodes. So, it is necessary to perform in-network data aggregation in the WSN which is performed by aggregator node. Since, the nodes in WSN are vulnerable to malicious attackers and physical impairment; the data collected in WSNs may be unreliable. So, in this paper, we propose an efficient technique to detect the unreliable data. Earlier, A Principal Component Analysis (PCA) is used to compress the data by reducing the number of dimensions. But as a drawback, it is not robust to outliers. Hence, if the input data is corrupted, an arbitrarily wrong representation is obtained. To overcome this problem, we propose a Robust PCA which is augmented with Minimum Covariance Determinant (MCD), highly robust estimator. In the proposed approach the distributed nature of sensor data is modelled using the sound statistical technique PCA and MCD is employed to design a noise-free data model. The performance of proposed approach is evaluated and compared with previous approaches and found that our approach is effective and efficient.