Abstract
In this article, we discuss our experience of realizing a prototype IoT-based food
safety monitoring solution which integrates inexpensive off-the-shelf open source IoT technology for monitoring food deliveries, semantic services for managing and reasoning about food safety provenance records, and private blockchain networks for persistent and secure storage of semantic provenance graphs. We describe how observation of real-world contexts was used to develop a prototype device, and the results of field trials deploying these prototypes as part of the food delivery process. Results indicate that continuous, context sensitive, trustworthy temperature measurement could provide benefits to multiple stakeholders across the delivery pathway. However, close attention has to be paid to the technology used—as cheap multi-functional IoT devices may produce low quality sensor observations which adversely affect the utility of the overall solution. Our experience also suggests that future food safety management systems may need to include machine-processable guidelines to support analysis of raw sensor data for food safety compliance
safety monitoring solution which integrates inexpensive off-the-shelf open source IoT technology for monitoring food deliveries, semantic services for managing and reasoning about food safety provenance records, and private blockchain networks for persistent and secure storage of semantic provenance graphs. We describe how observation of real-world contexts was used to develop a prototype device, and the results of field trials deploying these prototypes as part of the food delivery process. Results indicate that continuous, context sensitive, trustworthy temperature measurement could provide benefits to multiple stakeholders across the delivery pathway. However, close attention has to be paid to the technology used—as cheap multi-functional IoT devices may produce low quality sensor observations which adversely affect the utility of the overall solution. Our experience also suggests that future food safety management systems may need to include machine-processable guidelines to support analysis of raw sensor data for food safety compliance
| Original language | English |
|---|---|
| Article number | 563424 |
| Number of pages | 23 |
| Journal | Frontiers in Sustainable Food Systems |
| Volume | 4 |
| DOIs | |
| Publication status | Published - 17 Nov 2020 |
Bibliographical note
Engineering and Physical Sciences Research Council - The work presented here was supported by an award made by the UKRI, EPSRC funded Internet of Food Things Network+ grant EP/R045127/1.FUNDING:
The work presented here was supported by an award made by the UKRI, EPSRC funded Internet of Food Things Network+ grant EP/R045127/1.
ACKNOWLEDGMENTS:
We would like to thank G. McWilliam and Aberdeen University Services for their cooperation during the pilot deployments of the PROoFD-IT system. We would also like to thank Food Standards Scotland and the Semantic Web Company GmbH for
their valuable comments on the project.
Keywords
- provenance
- loT
- food safety
- HACCP
- blockchain
- ontology
- IoT