Abstract
The recent introduction of passive RFID tags into leading retailers’ supply chains has had a tremendous impact on their ability to manage the flow of goods. Tags on cases and pallets increase the supply chain’s visibility and allow for accurate tracking and tracing. Currently, additional efforts are being made to use RFID to actively monitor a product’s shipping condition (e.g. temperature, shock-vibration, etc.). This is of substantial interest for perishable goods and pharmaceuticals, whose shipping conditions are regulated. For continuous tracking of environmental data during the process of the movement of goods through the supply chain, RFID tags need to be equipped with sensors. Such sensors require continuous power, which is usually supplied by a battery on the tag. However, a battery not only significantly increases the cost of the hardware and makes it heavy and bulky, but also limits a tag’s lifetime. We propose ambient energy scavenging as a method to power sensors on battery-free RFID tags for continuous temperature monitoring and we show its applicability to the cold chain. Through detailed analysis of typical transport conditions we have identified ambient power sources which allow us to specify chip requirements and to make informed decisions about tag placement and total cost. We conclude that efficient monitoring ability is available at significantly lower cost than comparable implementations with active tags. Due to reduced costs, we predict high market penetration, which will result in more detailed information about multi-echelon supply chains. The fine-grained measurements will reveal failures and inefficiencies in the cold chain at a level of detail that would be hard to achieve with active tags. The elimination of the short-comings in the cold chain will result in reduced shrinkage, better quality and freshness of goods, and an overall reduction of losses of revenue.
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Metzger, C., Michahelles, F., Fleisch, E. (2007). Ambient Energy Scavenging for Sensor-Equipped RFID Tags in the Cold Chain. In: Kortuem, G., Finney, J., Lea, R., Sundramoorthy, V. (eds) Smart Sensing and Context. EuroSSC 2007. Lecture Notes in Computer Science, vol 4793. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-75696-5_16
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DOI: https://doi.org/10.1007/978-3-540-75696-5_16
Publisher Name: Springer, Berlin, Heidelberg
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