Fixing Data Anomalies With Prediction Based Algorithm in Wireless Sensor Networks

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Data inconsistencies are present in the data collected over a large Wireless Sensor Network (WSN), usually deployed for any kind of monitoring applications. Before passing this data to some WSN applications for decision making, it is necessary to ensure that the data received are clean and accurate. In this paper, the authors have used a statistical tool to examine the past data to fit in a highly sophisticated prediction model, i.e., ARIMA for a given sensor node and with this, the model corrects the data using forecast value if any data anomaly exists there. Another scheme is also proposed for detecting data anomaly at sink among the aggregated data in the data are received from a particular sensor node.