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Sensors connected to the Internet of Things (IoT) generates billions of data points that can lead to realtime insights. The sensor data stream-in at an interval of one second, which is equivalent to 86400 rows of data per day. A correct predictive analytics of sensor stream information can be used to estimate missing values or to replace incorrect readings captured because of the events of malfunctioning sensors or broken communication channel. It can also be used to anticipate situations that help in various decision makings, including maintenance of a stable internal cold storage temperature and operations. In this paper, a predictive analytics for anomaly detection in the Internet of Things enabled smart cold storage warehouse is proposed using Apache Spark Service and IBM SPSS Modeller which can generate a predicted value for temperature readings. Then a set of decision rules based on both the sensed data and the predicted temperature readings is developed to generate an alert for spikes and dips in temperature. This system can be helpful to cold storage owners to achieve zero wastage of perishable food items during cold storage warehousing.

Predictive Analytics for Anomaly Detection in Internet of Things Enabled Smart Cold Storage Warehousing