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TinyML and IoT for cold chain monitoring: applications, challenges and opportunities.
Number: 1139
Author(s) : SISTI E., MARTÍNEZ-BALLESTER S., MINETTO S., ROSSETTI A., MARINETTI S., BEGHI A., RAMPAZZO M.
Summary
Nowadays, cold chain monitoring applications are very important to address an ever-increasing variety of challenges ranging from the issue of temperature and humidity sensitive products spoilage to product security and supply chain energy efficiency. Since it is common practice to collect data from process operation monitoring, the emerging digital technologies such as Internet of Things (IoT), Artificial Intelligence (AI), and Machine Learning (ML) offer new possibilities to dominate the increasing complexity of safer and more efficient cold chain monitoring and management. In particular, this paper gives an overview of an emerging field in AI, i.e. TinyML, applied to cold chain monitoring. TinyML is at the intersection of embedded ML applications, algorithms, hardware, and software. It is a cutting-edge field that brings the transformative power of ML to low-powered devices with scarce computer and memory assets like the sensors deployed in IoT cold chain implementations.
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Details
- Original title: TinyML and IoT for cold chain monitoring: applications, challenges and opportunities.
- Record ID : 30029502
- Languages: English
- Subject: Technology
- Source: 7th IIR International Conference on Sustainability and the Cold Chain (Online). Proceedings: April 11-13 2022
- Publication date: 2022/04/11
- DOI: http://dx.doi.org/10.18462/iir.iccc2022.1139
- Document available for consultation in the library of the IIR headquarters only.
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Indexing
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Themes:
Cold chain, interfaces;
Traceability, metrology - Keywords: Cold chain; Monitoring; Internet of Things; Machine learning; Detection; Failure
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