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Artificial Intelligence Enabled Smart Refrigeration Management System Using Internet of Things Framework

Published: 31 May 2020 Publication History

Abstract

Design of an intelligent refrigeration management system using artificial intelligence and Internet of Things (IoT) technology is presented in this paper. This system collects the real-time temperature inside the refrigeration implement, record the information of products and enhance function of refrigerators through the application of Internet of Things technology to facilitate people in managing their refrigerated and frozen groceries smartly. The proposed system is divided into two parts, On-board sub-system and Internet based sub-system. An Arduino Leonardo board is used in onboard sub-system to control other components including low power machine vision OpenMV module, temperature & Humidity sensor, and GY-302 light intensity sensor. OpenMV camera module is used for recognizing types of food, reading barcodes and OCR (optical character recognition) through convolution neural network (CNN) algorithm and tesseract-ocr. The food type identification model is trained by the deep learning framework Caffe. GY-302 light intensity sensor works as a switch of camera module. DHT11 sensor is used to monitor the environmental information inside the freezer. The internet based sub-system works on the things network. It saves the information and uploads it from onboard sub-system and works as an interface to food suppliers. The system demonstrates that the combination of existing everyday utility systems and latest Artificial Intelligence (AI) and Internet of Things (IoT) technologies could help develop smarter applications and devices.

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Cited By

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  • (2024)Using IoT and AI to replenish household food supplies: A systematic reviewJournal of Smart Cities and Society10.3233/SCS-2300223:1(23-62)Online publication date: 1-Mar-2024
  • (2023)A framework for low cost, ubiquitous and interactive smart refrigeratorMultimedia Tools and Applications10.1007/s11042-023-15544-183:5(13337-13368)Online publication date: 5-Jul-2023

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cover image ACM Other conferences
CNIOT '20: Proceedings of the 2020 International Conference on Computing, Networks and Internet of Things
April 2020
234 pages
ISBN:9781450377713
DOI:10.1145/3398329
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

In-Cooperation

  • University of Salamanca: University of Salamanca
  • The University of Adelaide, Australia
  • Edinburgh Napier University, UK: Edinburgh Napier University, UK
  • University of Sydney Australia

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 31 May 2020

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Author Tags

  1. Artificial Intelligence (AI)
  2. Convolution Neural Network
  3. Internet of Things (IoT)
  4. Machine Vision
  5. Smart City
  6. Smart Home Application
  7. The Things Network

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CNIOT2020

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CNIOT '20 Paper Acceptance Rate 39 of 82 submissions, 48%;
Overall Acceptance Rate 39 of 82 submissions, 48%

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Cited By

View all
  • (2024)Using IoT and AI to replenish household food supplies: A systematic reviewJournal of Smart Cities and Society10.3233/SCS-2300223:1(23-62)Online publication date: 1-Mar-2024
  • (2023)A framework for low cost, ubiquitous and interactive smart refrigeratorMultimedia Tools and Applications10.1007/s11042-023-15544-183:5(13337-13368)Online publication date: 5-Jul-2023

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