# Real-Time Anomaly Detection in Cold Chain Transportation Using IoT Technology
## 6 Conclusions  
This work demonstrated the application of an energy-efficient battery-powered IoT-based alerting solution for the temperature anomaly detection of refrigerated food products during transport. The system was further validated in a real-world scenario by utilising the delivery vehicles of a last-mile grocery distributor in northern Ireland in which the loggers were installed. The alerting system was specially configured so that it could effectively notify staff members in a timely manner of anomalies occurring in the refrigerators but only under the following circumstances: when the vehicles were in operation and moving, and once they had appropriately cooled down and reached the desired temperatures of refrigeration, which is achieved after 35 min. An algorithm was developed to configure the dedicated alerting system so that it was only triggered once the conditions were met. To improve user technology acceptance as well as maintenance requirements and mitigate environmental impact related to resource consumption, another algorithm was designed and implemented to reduce the battery consumption of the IoT devices. This was carried out by means of enabling a trip detection mechanism based on acceleration and GPS information.  
Battery life analysis was performed to determine the impact of the trip detection algorithm on trip-detection enabled loggers versus loggers without it. Results estimate a life expectancy performance increase of roughly 162% using the logger with trip detection enabled versus a logger without trip detection, increasing usage between battery changes from 68 to 179 days. The logger deployed in the refrigerated van with trip detection enabled was exposed to lower temperatures which may have negatively affected the battery voltage and capacity, and if the bench testing on trip detection had occurred in the same conditions, the results could have shown a higher difference in battery consumption.  
The trip-detection algorithm was effective in reducing battery consumption and enabling loggers to remain operative for longer without a replacement, thus decreasing the total economic resources and environmental impact of the system in the long run. Additionally, the trip detection algorithm was also utilised to provide intelligent alerting for the anomaly detection system.  
Future work will validate the battery usage of loggers without trip detection in a real-case refrigerated scenario to account for difference in temperature. Additionally, the sensor setup can be expanded upon to incorporate a door contact sensor so that alarms can be sent to driver and warehouse staff if it has been left open during a delivery. This will be especially relevant for multi-drop grocery distributors with perishable stock remaining in their vans as they perform multiple deliveries before returning to the warehouse.  
Conceptualization, J.G., T.C. and R.G.; methodology, J.G., T.C. ([PERSON]) and R.G.; software, J.G. and M.K.; formal analysis, J.G.; writing--original draft preparation, J.G., T.P.d.C., X.C.-M. and T.C. ([PERSON]); writing--review and editing, J.G., T.P.d.C., X.C.-M., T.C. ([PERSON]), J.C., T.C. ([PERSON]), E.R., F.M., M.K., R.G. and R.R.; visualization, J.G.; funding acquisition, R.R. All authors have read and agreed to the published version of the manuscript.  
This research was conducted as part of the REAMIT project, funded by the Interreg North-West Europe grant number NWE831.  
Not applicable.  
The data presented in this study are available on request from the corresponding author. The data are not publicly available due to privacy restrictions of the pilot company.  
**Conflicts of Interest**: The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.