Robust estimation of incorrect data using relative correlation clustering technique in wireless sensor networks

Abstract

Data inaccuracy is an important problem in wireless sensor networks, since the accuracy is affected by harsh environments and malicious nodes. The reason for this data inaccuracy is the improper identification of outliers. To detect exact outliers in the wireless sensor networks, we propose the relative correlation based clustering (RCC) technique with high data accuracy and low computational overhead. Identifying spatial, temporal correlation and attribute correlation is the first phase of the proposed algorithm. The second phase is optimal cluster formation and outlier classification based on two correlation levels. The inference of the proposed idea shows high outlier detection rate with different outlier corruption level. Moreover, our results when compared with previous approach taking the same data into consideration clearly outperform them, identifying high level of detection rate (99.87%) in the top-line with near to the ground false alarm rate.