Monitoring of Temperature in Retail Refrigerated Cabinets Applying IoT Over Open-Source Hardware and Software

Ramírez-Faz, J.; Fernández-Ahumada, Luis Manuel; Fernández-Ahumada, Elvira; López-Luque, R.

doi:10.3390/s20030846

Cited by 30 publications

(16 citation statements)

References 51 publications

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“…This system performs realtime monitoring of location information using a remote network. Faz et al [8] proposed a low-cost IoT system for monitoring temperature in freezers using a Wi-Fi microcontroller along with temperature sensors. Authors used clod platform to store and analyze data and helps in supervising various parameters such as light, energy consumption etc.…”

Section: Related Workmentioning

confidence: 99%

A Smart IoT System for Remote Refrigeration Monitoring

Padmaja¹,

Patro³

et al. 2021

Preprint

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The realm of refrigeration industry has marked its significance for storing food and beverages. Refrigeration warehouses are extensively used to store various combinations of large fresh food stock at favourable temperatures to maintain its quality. Any deviations from these favourable temperatures pose negative impacts on the cold stock. Hence, it is essential to minimize the margin of these potential risks that can incur huge loss to the cold-storage industries. On-site evaluation can be reliable to address these risks, but it is time consuming and an inefficient process as human intervention can be prone to manual errors. The paper, therefore, proposes a methodology to build a smart IoT system to remotely monitor and evaluate the refrigeration warehouses seamlessly at one’s fingertips using a mobile or laptop through dashboards. Furthermore, this paper addresses the difficulties in managing and analysing the large sensory data which are the result of seamless monitoring by unleashing the power of edge computing and cloud computing.

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Section: Related Workmentioning

confidence: 99%

A Smart IoT System for Remote Refrigeration Monitoring

Padmaja¹,

Patro³

et al. 2021

Preprint

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“…It also has a camera to keep monitoring the fridge content and this can be done from remote locations. Articles in [ 12 – 16 ] worked on a refrigerator that is based on IoT for remotely monitoring for different parameters in real-time mode. Articles in [ 17 – 19 ] proposed different methods related to the smart refrigerator for avoiding food wastage in a fridge.…”

Section: Related Workmentioning

confidence: 99%

A Data-Driven Predictive Machine Learning Model for Efficiently Storing Temperature-Sensitive Medical Products, Such as Vaccines: Case Study: Pharmacies in Rwanda

Habiyaremye

Zennaro

Mikeka

et al. 2021

Journal of Healthcare Engineering

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Temperature control is the key element during medicine storage. Pharmacies sell some medical products which are kept in fridges. The opening and closing of the fridge while taking some medicine makes the outside hot air enter the fridge, which will increase the inner fridge temperature. When the frequency of opening and closing of the fridge is increased, the temperature may go beyond the allowed storage temperature range. In this paper, we are proposing a model with the help of machine learning that will be used in multiple chambers fridges to keep indicating the time remaining for the inner temperature to go beyond the allowed range, and if the time is short, the system will propose to the pharmacist not to open that particular room and proposes a room that has enough time slots (time to reach the upper limit temperature). By using training data got from a thermoelectric cooler-based fridge, we constructed a multiple linear regression model that can predict the required time for a given room to reach the cut-off temperature in case that room is opened. The built model was evaluated using the coefficient of determination R2 and is found to be 77%, and then it can be used to develop a multiple room smart fridge for efficiently storing highly sensitive medical products.

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“…Furthermore, we believe that these issues are not sufficiently considered by the existing literature, and temperature readings provided by these technologies are too often taken at face value. For example, the DS18B20 3 temperature probe (reported accuracy ±0.5 • C and also used in our project) has been used in other food related research due to its low cost; examples include monitoring of refrigeration units (Ramírez-Faz et al, 2020), and freshwater aquaculture production (Shi et al, 2018). However, authors did not report any calibration or evaluation of sensor accuracy and the temperature readings seem to be taken at their face value.…”

Section: Food Safety Monitoring and Iotmentioning

confidence: 99%