Fixing Data Anomalies with Prediction Based Algorithm in Wireless Sensor Networks

TL;DR

This paper presents a prediction-based algorithm using ARIMA models to detect and correct data anomalies in wireless sensor networks, improving data quality for decision-making.

Contribution

It introduces a novel statistical approach combining ARIMA forecasting and anomaly detection at sink nodes for WSN data cleaning.

Findings

ARIMA effectively detects anomalies in sensor data.

The proposed method improves data accuracy in real WSN deployments.

Validation on real sensor data confirms method's effectiveness.

Abstract

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, we 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. The effectiveness of our methods are validated by data collected over a real WSN application consisting of Crossbow IRIS Motes \cite{Crossbow:2009}.