Real-Time Anomaly Detection in Cold Chain Transportation Using IoT Technology

There are approximately 88 million tonnes of food waste generated annually in the EU alone. Food spoilage during distribution accounts for some of this waste. To minimise this spoilage, it is of utmost importance to maintain the cold chain during the transportation of perishable foods such as meats, fruits, and vegetables. However, these products are often unfortunately wasted in large quantities when unpredictable failures occur in the refrigeration units of transport vehicles. This work proposes a real-time IoT anomaly detection system to detect equipment failures and provide decision support options to warehouse staff and delivery drivers, thus reducing potential food wastage. We developed a bespoke Internet of Things (IoT) solution for real-time product monitoring and alerting during cold chain transportation, which is based on the Digital Matter Eagle cellular data logger and two temperature probes. A visual dashboard was developed to allow logistics staff to perform monitoring, and business-defined temperature thresholds were used to develop a text and email decision support system, notifying relevant staff members if anomalies were detected. The IoT anomaly detection system was deployed with Musgrave Marketplace, Ireland's largest grocery distributor, in three of their delivery vans operating in the greater Belfast area. Results show that the LTE-M cellular IoT system is power efficient and avoids sending false alerts due to the novel alerting system which was developed based on trip detection.

Automated summary

Approximately 88 million tonnes of food waste is generated annually in the EU, with food spoilage during distribution being a major contributor. This study proposes a real-time IoT anomaly detection system to minimize food wastage by detecting equipment failures and providing decision support options to warehouse staff and delivery drivers. The system was implemented with Musgrave Marketplace, Ireland's largest grocery distributor, and showed efficient and accurate performance in reducing waste through the use of an LTE-M cellular IoT system and a novel alerting system based on trip detection.