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Data inconsistencies are present in the data collected over a large wirelesssensor network (WSN), usually deployed for any kind of monitoring applications.Before passing this data to some WSN applications for decision making, it isnecessary to ensure that the data received are clean and accurate. In thispaper, we have used a statistical tool to examine the past data to fit in ahighly sophisticated prediction model i.e., ARIMA for a given sensor node andwith this, the model corrects the data using forecast value if any data anomalyexists there. Another scheme is also proposed for detecting data anomaly atsink among the aggregated data in the data are received from a particularsensor node. The effectiveness of our methods are validated by data collectedover a real WSN application consisting of Crossbow IRIS Motes\cite{Crossbow:2009}.

Fixing Data Anomalies with Prediction Based Algorithm in Wireless Sensor Networks