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Cold storage is deemed one of the main elements in food safety management to maintain food quality. The temperature, relative humidity (RH), and air quality in cold storage rooms (CSRs) should be carefully controlled to ensure food quality and safety during cold storage. In addition, the components of CSR are exposed to risks caused by the electric current, high temperature surrounding the compressor of the condensing unit, snow and ice accumulation on the evaporator coils, and refrigerant gas leakage. These parameters affect the stored product quality, and the real-time sending of warnings is very important for early preemptive actionability against the risks that may cause damage to the components of the cold storage rooms. The IoT-based control (IoT-BC) with multipurpose sensors in food technologies presents solutions for postharvest quality management of fruits during cold storage. Therefore, this study aimed to design and evaluate a IoT-BC system to remotely control, risk alert, and monitor the microclimate parameters, i.e., RH, temperature, CO2, C2H4, and light and some operating parameters, i.e., the temperature of the refrigeration compressor, the electrical current, and the energy consumption for a modified CSR (MCSR). In addition, the impacts of the designed IoT-BC system on date fruit quality during cold storage were investigated compared with a traditional CSR (TCSR) as a case study. The results showed that the designed IoT-BC system precisely controlled the MCSR, provided reliable data about the interior microclimate atmosphere, applied electrical current and energy consumption of the MCSR, and sent the necessary alerts in case of an emergency based on real-time data analytics. There was no significant effect of the storage time on the most important quality attributes for stored date fruit in the MCSR compared with the TCSR. As a result, the MCSR maintained high-quality attributes of date fruits during cold storage. Based on the positive impact of the designed IoT-BC system on the MCSR and stored fruit quality, this modification seems quite suitable for remotely managing cold storage facilities.

Increasing the efficiency of water supply networks is essential in arid and semi-arid regions to ensure the supply of drinking water to the inhabitants. The cost of renovating these systems is high. However, customized management models can facilitate the maintenance and rehabilitation of hydraulic infrastructures by optimizing the use of resources. The implementation of current Internet of Things (IoT) monitoring systems allows decisions to be based on objective data. In water supply systems, IoT helps to monitor the key elements to improve system efficiency. To implement IoT in a water distribution system requires sensors that are suitable for measuring the main hydraulic variables, a communication system that is adaptable to the water service companies and a friendly system for data analysis and visualization. A smart pressure monitoring and alert system was developed using low-cost hardware and open-source software. An Arduino family microcontroller transfers pressure gauge signals using Sigfox communication, a low-power wide-area network (LPWAN). The IoT ThingSpeak platform is used for data analysis and visualization. Additionally, the system can send alarms via SMS/email in real time using the If This, Then That (IFTTT) web service when anomalous pressure data are detected. The pressure monitoring system was successfully implemented in a real water distribution network in Spain. It was able to detect both breakdowns and leaks in real time.

Water temperature, pH, dissolved oxygen (DO), electrical conductivity (EC), and salinity levels are the critical cultivation factors for freshwater aquaculture. This paper proposes a novel wireless multi-sensor system by integrating the temperature, pH, DO, and EC sensors with an ESP 32 Wi-Fi module for monitoring the water quality of freshwater aquaculture, which acquires the sensing data and salinity information directly derived from the EC level. The information of water temperature, pH, DO, EC, and salinity levels was displayed in the ThingSpeak IoT platform and was visualized in a user-friendly manner by ThingView APP. Firstly, these sensors were integrated with an ESP32 Wi-Fi platform. The observations of sensors and the estimated salinity from the EC level were then transmitted by a Wi-Fi network to an on-site Wi-Fi access point (AP). The acquired information was further transmitted to the ThingSpeak IoT and displayed in the form of a web-based monitoring system which can be directly visualized by online browsing or the ThingView APP. Through the complete processes of pre-calibration, in situ measurement, and post-calibration, the results illustrate that the proposed wireless multi-sensor IoT system has sufficient accuracy, reliable confidence, and a good tolerance for monitoring the water quality of freshwater aquaculture.

Energy management and monitoring systems are significant difficulties in applying microgrids to smart homes. Thus, further research is required to address the modeling and operational parts of the system’s future results for various applications. This paper proposes a new technique for energy management in a microgrid using a robust control approach and the development of a platform for real-time monitoring. The developed controller is based on a fuzzy logic method used in the energy Internet paradigm with connected distributed generators (DGs) in the microgrid. The developed method regulates the power flow of the microgrid, and frequency/voltage regulation improved the load-management performance and monitoring system using the ThingSpeak platform for real-time data analysis. The MATLAB. simulation results show the feasibility and effectiveness of the proposed strategy and the introduced approach in microgrid control under various operating conditions. Additionally, the results show that the proposed monitoring platform facilitates real-time data analysis.

The use of the internet of things (IoT) in solar photovoltaic (PV) systems is a critical feature for remote monitoring, supervising, and performance evaluation. Furthermore, it improves the long-term viability, consistency, efficiency, and system maintenance of energy production. However, previous researchers' proposed PV monitoring systems are relatively complex and expensive. Furthermore, the existing systems do not have any backup data, which means that the acquired data could be lost if the network connection fails. This paper presents a simple and low-cost IoT-based PV parameter monitoring system, with additional backup data stored on a microSD card. A NodeMCU ESP8266 development board is chosen as the main controller because it is a system-on-chip (SOC) microcontroller with integrated Wi-Fi and low-power support, all in one chip to reduce the cost of the proposed system. The solar irradiance, ambient temperature, PV output voltage and PV output current, are measured with photo-diodes, DHT22, impedance dividers and ACS712. While, the PV output power is a product of the PV voltage and PV current. ThingSpeak, an open-source software, is used as a cloud database and data monitoring tool in the form of interactive graphics. The results showed that the system was designed to be highly accurate, reliable, simple to use, and low-cost.

Air quality is a vital concern globally, and Sri Lanka, according to WHO statistics, faces challenges in achieving optimal air quality levels. To address this, we introduced an innovative IoT-based Air Pollution Monitoring (APM) Box. This solution incorporates readily available Commercial Off-The-Shelf (COTS) sensors, specifically MQ-7 and MQ-131, for measuring concentrations of Carbon Monoxide (CO) and Ozone (O3) ,Arduino and \"ThingSpeak\" platform. Yet, those COTS sensors are not factory-calibrated. Therefore, we implemented machine learning algorithms, including linear regression and deep neural network models, to enhance the accuracy of CO and O3 concentration measurements from these non-calibrated sensors. Our findings indicate promising correlations when dealing with MQ-7 and MQ-131 measurements after removing outliers.

Steep slopes covered by loose unsaturated pyroclastic deposits widely dispersed in Campania, Southern Italy, are often subjected to shallow landslides that turn into fast debris flows causing a large amount of damage and many casualties, triggered by heavy and persistent precipitation. The slope of Cervinara, located around 40 km northeast of Naples, was involved in a destructive flowslide between 15 and 16 December 1999, triggered by a rain event of 325 mm in 48 h. Hydrometeorological monitoring activities have been carried out near the landslide scarp of 1999 since 2017 to assess the water balance and to identify major hydrological processes involving the cover and the shallow groundwater system developing in the upper part of the underlying limestone fractured bedrock. Since 1 December 2022, a remotely accessible low-cost network has been installed to expand the field hydrological monitoring. The use of a network of low-cost capacitive sensors, communicating within the domain of Internet of Things (IoT) technology, aiming at dispersed monitoring of soil moisture, has been tested. Specifically, the tested prototype network allows measurements of the soil water content at two different points, communicating through a Wi-Fi-based IoT system using ESP32 boards. The ThingSpeakTM IoT platform has been used for remote field data visualization. Based on the obtained results, the prototype of this IoT-based low-cost network shows the potential to expand the amount of hydrological data, suitable for setting up early warning systems in landslide-prone areas.

One of the technological advancements in agricultural production is the tissue culture propagation technique, commonly used for mass multiplication and disease-free plants. The necessity for date palm tissue culture emerged from the inability of traditional propagation methods’ offshoots to meet the immediate demands for significant amounts of planting material for commercial cultivars. Tissue culture plantlets are produced in a protected aseptic in vitro environment where all growth variables are strictly controlled. The challenges occur when these plantlets are transferred to an ex vitro climate for acclimatization. Traditional glasshouses are frequently used; however, this has substantial mortality consequences. In the present study, a novel IoT-based automated ex vitro acclimatization system (E-VAS) was designed and evaluated for the acclimatization of date palm plantlets (cv. Khalas) to enhance their morpho-physiological attributes and reduce the mortality rate and the contamination risk through minimal human contact. The experimental findings showed that the morpho-physiological parameters of 6- and 12-month-old plants were higher when acclimatized in the prototype E-VAS compared to the traditional glasshouse acclimatization system (TGAS). The maximum plant mortality percentage occurred within the first month of the transfer from the in vitro to ex vitro environment in both systems, which gradually declined up to six months; after that, no significant plant mortality was observed. About 6% mortality was recorded in E-VAS, whereas 18% in TGAS within the first month of acclimatization. After six months of study, an overall 14% mortality was recorded in E-VAS compared to 41% in TGAS. The proposed automated system has a significant potential to address the growing demand for the rapid multiplication of tissue culture-produced planting materials since the plant survival rate and phenotype quality were much higher in E-VAS than in the conventional manual system that the present industry follows for commercial production.

Hair salons use many hair products that have toxic chemicals in them. These toxic chemicals include volatile organic compounds, formaldehyde, and particulate matter. Daily exposure to these pollutants causes severe health issues in the long run. This study aims to find the concentration of the air pollutants such as PM1, PM2.5, PM10, TVOC, CO2, and formaldehyde in four hair salons located in Coimbatore, Tamil Nadu, India. In this paper, we propose an IoT-based air quality monitoring system with integrated sensors to monitor the concentration of air pollutants remotely via ThingSpeak data analytics cloud platform in hair salons. The maximum 15 min average concentration values of PM1, PM2.5, and PM10 were 128, 154, and 169 µg/m3 respectively. The TVOC levels exhibited a rapid increase of about 80–90% during facials and hair gel application and a peak value of about 5248.25 ppb was measured at salon 2. Also, weekend and weekday comparison is done. It was found that the weekend concentrations of the measured pollutants are comparatively higher than weekday concentrations. After analyzing the pollutant concentration, the effects of primary health parameters such as blood pressure and pulse rate of the hairdressers are measured. One-third of the hairdressers displayed high blood pressure values with a maximum of 161/104 which falls under stage 2 hypertension. Also, secondary parameters such as temperature, humidity, ventilation type, and number of customers are also measured. From the overall analysis, it is suggested that adequate ventilation and regulated product usage are said to reduce the effects of indoor air pollution.

The market for smart greenhouses has been valued at USD 1.77 billion in 2022 and is expected to grow to 3.39 billion by 2030. In order to make this more efficient, with the help of Internet of Things (IoT) technology, it is desired to eliminate the problem of traditional agriculture, which has poor monitoring and accuracy control of the parameters of a culture. Climate control decisions in a greenhouse are made based on parameter monitoring systems, which can be remotely controlled. Instead of this adjustment of the measured parameters, it would be preferable from the point of view of energy consumption that they should be calculated at optimal values from the design phase of the greenhouse. For this reason, it would be better to perform an energy simulation of the greenhouse first. For the study carried out in this work, a small greenhouse (mini-greenhouse) was built. It was equipped with an IoT sensor system, which measured indoor climate parameters and could send data to the cloud for future recording and processing. A simplified mathematical model of the heat balance was established, and the measured internal parameters of the mini-greenhouse were compared with those obtained from the simulation. After validating the mathematical model of the mini-greenhouse, this paper aimed to find the optimal position for placing a normal-sized greenhouse. For this, several possible locations and orientations of the greenhouse were compared by running the mathematical model, with which the most unfavorable positions could be eliminated. Then, some considerably cheaper “mini-greenhouses” were made and placed in the locations with the desired orientations. Using sensor systems and technologies similar to those presented in this work, the parameters from all mini-greenhouses can be monitored in real time. This real-time monitoring allows for the simultaneous analysis of all greenhouses, without the disadvantages of data collection directly in the field, with all data being recorded in the cloud and other IoT-specific advantages being made use of. In the end, we can choose the optimal solution for the location of a real-size greenhouse.