TY - GEN
T1 - Artificial Intelligence Enabled Smart Refrigeration Management System Using Internet of Things Framework
AU - Dong, Zhongxu
AU - Abdulghani, Amir M.
AU - Imran, Muhammad A.
AU - Abbasi, Qammer H.
N1 - Publisher Copyright:
© 2020 ACM.
PY - 2020/4/24
Y1 - 2020/4/24
N2 - 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.
AB - 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.
KW - Artificial Intelligence (AI)
KW - Convolution Neural Network
KW - Internet of Things (IoT)
KW - Machine Vision
KW - Smart City
KW - Smart Home Application
KW - The Things Network
UR - http://www.scopus.com/inward/record.url?scp=85086243892&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85086243892&partnerID=8YFLogxK
U2 - 10.1145/3398329.3398338
DO - 10.1145/3398329.3398338
M3 - Conference contribution
AN - SCOPUS:85086243892
T3 - PervasiveHealth: Pervasive Computing Technologies for Healthcare
SP - 65
EP - 70
BT - Proceedings of the 2020 International Conference on Computing, Networks and Internet of Things, CNIOT 2020
PB - Institute for Color Science and Technology (ICST)
T2 - 2020 International Conference on Computing, Networks and Internet of Things, CNIOT 2020
Y2 - 24 April 2020 through 26 April 2020
ER -