Cost-Effective Implementation of a Temperature Traceability System Based on Smart RFID Tags and IoT Services

[EN] This paper presents the design and validation of a traceability system, based on radio frequency identification (RFID) technology and Internet of Things (IoT) services, intended to address the interconnection and cost-implementation problems typical in traceability systems. The RFID layer integrates temperature sensors into RFID tags, to track and trace food conditions during transportation. The IoT paradigm makes it possible to connect multiple systems to the same platform, addressing interconnection problems between different technology providers. The cost-implementation issues are addressed following the Data as a Service (DaaS) billing scheme, where users pay for the data they consume and not the installed equipment, avoiding the big initial investment that these high-tech solutions commonly require. The developed system is validated in two case scenarios, one carried out in controlled laboratory conditions, monitoring chopped pumpkin. 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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

IoT platform for seafood farmers and consumers

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Usability of Real Time Data for Cold Chain Monitoring Systems

One in every nine people on earth do not have enough food to lead a healthy life, according to The World Food Programme. That\u27s nearly 800 million people. In addition to that, billions of tons of perishable food products are wasted during transportation and logistics before it reaches the end consumers as thousands of people die every day due to hunger related causes. Perishable foods, medicine and other goods impose severe challenges on inventory management. Businesses debate on whether to keep limited stock just to meet demand and fear losing additional customers or keep excess stock and face the risk of expiry of goods. Unlike the transportation of other goods, perishable food products and medicines undergo tremendous degradation in quality as a function of environmental conditions over time. Perishable food products are usually stored in frozen and refrigerated condition at the distribution centers, supermarkets and during the transit in order to preserve the quality of food and extend the shelf life. Even though, temperature controlled supply chain in the food retail sector has become commonplace, there is one major limitation of the current practice in the chilled food chain management. The printed \u27sell-by-date\u27 is not a true indicator of the quality of the product as it does not reflect the temperature variations during distribution at the different stages of the food supply chain. The food quality is severely compromised when actual environmental conditions deviate from the expected conditions. This research proposes the use of real-time sensor data to support supply chain decisions and describe a model for gauging and improving usability on the real-time sensor data. Data reported through the wireless sensor networks could help in predicting the shelf-life of perishable food products and preventing them from spoilage. Use of sensor data would encourage data driven decision making rather than intuition. The findings would encourage businesses operating in the cold chain environment in exploring value added innovation opportunities through internet of things use cases and improve the usability experience and competitiveness of their supply chains via warehouse workers and truck drivers

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A Smart and Secure Logistics System Based on IoT and Cloud Technologies

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Internet of Things (IoT) is an integrated part of Future Internet including existing and evolving Internet and network developments and could be conceptually defined as a dynamic global network infrastructure with self configuring capabilities based on standard and interoperable communication protocols where physical and virtual “things” have identities, physical attributes, and virtual personalities, use intelligent interfaces, and are seamlessly integrated into the information network

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Traceability technologies have great potential to improve sustainable performance in cold food supply chains by reducing food loss. In existing approaches, traceability technologies are selected either intuitively or through a random approach, that neither considers the trade-off between multiple cost–benefit technology criteria nor systematically translates user requirements for traceability systems into the selection process. This paper presents a hybrid approach combining the fuzzy Analytic Hierarchy Process (AHP) and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) with integer linear programming to select the optimum traceability technologies for improving sustainable performance in cold food supply chains. The proposed methodology is applied in four case studies utilising data collected from literature and expert interviews. The proposed approach can assist decision-makers, e.g., food business operators and technology companies, to identify what combination of technologies best suits a given food supply chain scenario and reduces food loss at minimum cost.Cambridge Trust and Commonwealth Scholarship Commission