Monitoring Of Environmental Parameters Research Articles

Application of Fuzzy Logic and IoT in a small-scale Smart Greenhouse System

This research aims to address the critical challenges caused by global population increase, climate change, and urbanization on food production by means of a Fuzzy Logic controller in a small-scale greenhouse and improving agricultural efficiency and sustainability.The growing global population has stressed the significance of implementing efficient food production techniques. Furthermore, the impact of climate change and extreme weather events complicates the production process. The goal of this study is to combine an innovative small greenhouse design with a fuzzy logic-based control system in order to develop an IoT-enabled self-regulating environment for autonomous plant growing.Continuous advances in technology have been focusing on automating greenhouses to provide self-regulating microclimates and optimal developmental conditions. Current trends focus on automating greenhouses to provide self-regulating microclimates. Looking ahead, the strategy entails combining automated approaches with precisely planned structures via IoT.This study's key findings include the successful integration of a fuzzy logic control system with greenhouse infrastructure, allowing for real-time monitoring and adjustment of environmental parameters, as well as the development of a pilot IoT ecosystem demonstrating the feasibility of self-regulating microclimates for improved plant growth.

Design of smart home environment based on wireless sensor system and artificial speech recognition

In the context of the rapid development of intelligent technology, smart home has become the focus of attention. However, the traditional smart home system has some problems such as complicated operation and inconvenient information interaction. In order to solve these problems and improve the user experience of smart home environment, this study establishes a fast and efficient smart home environment design scheme by introducing wireless sensor system and artificial speech recognition technology. By arranging multiple wireless sensor nodes, the monitoring and data acquisition of environmental parameters are realized, and then the voice control and command interaction of smart home devices are realized by using artificial voice recognition technology. Through the effective combination of wireless sensor system and artificial voice recognition technology, users can realize the control of smart home environment through voice commands, and improve the convenience and intelligence of the system. The experimental results show that the design scheme can accurately and stably realize the recognition of user voice commands and the control of smart home devices, and users' evaluation of the degree of intelligence and user experience has also been significantly improved.

Study on the indoor thermal comfort of cave dwellings in cold areas of China

Cave dwelling, as a common traditional housing form in rural areas, has long been regarded as a low-cost, green, and environmentally friendly living choice. The purpose of this study was to explore and improve the thermal comfort of cave dwellings, and to systematically evaluate and analyse the thermal comfort of cave dwellings through a combination of visit surveys and indoor environmental parameter monitoring. After statistical analysis and comparison of the collected data, the following conclusions were drawn: (1) The thermal neutrality temperature of 25.15 °C determined by Predicted Mean Vote (PMV) in this study was greater than the thermal neutrality temperature of 19.49 °C determined by Thermal Sensation Vote (TSV) model, indicating that cave dwellers prefer a cooler thermal environments under actual conditions. (2) In cold weather, the maximum value of 80% acceptability is 18.12 °C and the minimum value is 12.00 °C. In hot weather, the maximum value of 80% acceptability is 27.17 °C and the minimum value is 23.66 °C. (3) In winter, the average indoor temperature of 9.71 °C is higher than the average outdoor temperature of 7.85 °C; In summer, the average temperature in the naturally ventilated room of 27.44 °C is much lower than the average outdoor temperature of 35.25 °C. The results showed that the building materials and structure of cave houses can provide a certain degree of thermal insulation, which can provide a basis for the design and improvement of low-cost, energy-saving and environmentally friendly houses in rural areas.

Energy Management System of Luminosity Controlled Smart City Using IoT

INTRODUCTION: With the escalating rates of urbanization, there is a pressing need for enhanced urban services. The concept of smart cities, leveraging digital technologies, offers a promising solution to elevate urban living. The integration of Internet-of-Things (IoT) in urban infrastructure, particularly on highways, opens avenues for novel services and cross-domain applications through Information and Communication Technologies. However, the efficient functioning of an IoT-enabled smart city necessitates careful energy resource management.
 OBJECTIVES: Propose a Highway Lighting System (HWLS) integrating IoT technologies to enhance urban services, focusing on significant energy savings and real-time environmental parameter monitoring.
 METHODS: To achieve the objective of enhancing urban services through the proposed Highway Lighting System (HWLS), the system was designed and implemented by integrating cutting-edge sensors, communication links, and the Blynk IoT app. The deployment involved incorporating IoT technologies for real-time monitoring of air quality, air moisture, and soil moisture, alongside a fault identification system using GSM and GPS modules.
 RESULTS: The proposed HWLS demonstrates significant energy savings, consuming only 37.6% of the original power consumption. The incorporation of IoT technologies facilitates real-time monitoring of environmental parameters, enabling informed decision-making for urban service optimization. The fault-finding system, utilizing GSM and GPS modules, enhances the reliability of the lighting system.
 CONCLUSION: In conclusion, the Highway Lighting System (HWLS) represents a novel approach to smart city infrastructure, particularly in the context of urban lighting. The integration of IoT technologies not only contributes to energy savings but also enhances the overall efficiency of urban services. The proposed system's ability to monitor environmental parameters and identify faults demonstrates its potential for sustainable urban development and improved quality of life.

IoT-based greenhouse technologies for enhanced crop production: a comprehensive study of monitoring, control, and communication techniques

According to various studies, different countries have different climate conditions that affect the quantity and quality of crop production. Farmers want to use novel technologies that can help them produce crops in large quantities with good quality. The greenhouse is one of the rapidly growing technologies used for the production of crops. It protects the crops from changes in external weather conditions and, hence, helps to produce good-quality crops throughout the year. This work proposes a deep study of the technologies used in the greenhouse for the monitoring and control of various environmental parameters that affect the quality and production of the crops. This work also analyses the different communication techniques, relays, and sensors used for monitoring and controlling the different greenhouse parameters. Also, give details on sensors and the advantages and disadvantages of different communication technologies so that farmers can easily understand which technology is most suitable for their greenhouse. This study helps the farmers adopt the best-suited techniques in their green houses that are cost-friendly, easy to use, and have a longer life.

Giving the cells what they need when they need it: Biosensor-based feeding control.

"Giving the cells exactly what they need, when they need it" is the core idea behind the proposed bioprocess control strategy: operating bioprocess based on the physiological behavior of the microbial population rather than exclusive monitoring of environmental parameters. We are envisioning to achieve this through the use of genetically encoded biosensors combined with online flow cytometry (FCM) to obtain a time-dependent "physiological fingerprint" of the population. We developed a biosensor based on the glnA promoter (glnAp) and applied it for monitoring the nitrogen-related nutritional state of Escherichia coli. The functionality of the biosensor was demonstrated through multiple cultivation runs performed at various scales-from microplate to 20 L bioreactor. We also developed a fully automated bioreactor-FCM interface for on-line monitoring of the microbial population. Finally, we validated the proposed strategy by performing a fed-batch experiment where the biosensor signal is used as the actuator for a nitrogen feeding feedback control. This new generation of process control, -based on the specific needs of the cells, -opens the possibility of improving process development on a short timescale and therewith, the robustness and performance of fermentation processes.

Design of Intelligent Security System for Higher Vocational Colleges Based on Internet of Things

Abstract The increase in Internet of Things (IoT) technologies for security in higher vocational institutions provides new solutions to enhance campus security. The challenges faced by campus security management include break-ins, emergency response, and environmental monitoring. Effective security system design can improve the ability to deal with these challenges and ensure the safety and comfort of students and faculty. In this study, an intelligent security system based on IoT technology is designed to realize dynamic management and real-time response to campus security by integrating advanced face recognition, environmental monitoring and data analysis technologies, thus improving campus security and management efficiency. Haar feature recognition, face detection optimized by Adaboost algorithm, and ecological monitoring technologies are some of the technological approaches introduced in the study. The system design integrates multiple aspects such as video surveillance, environmental parameter monitoring, and data analysis to build an efficient and reliable security monitoring system. In this paper, the system can monitor the campus security situation in real time, the face recognition accuracy rate reaches 95%, and the illegal intrusion detection time is reduced to within 2 seconds, effectively improving the response speed and accuracy of campus security management. This study proves the effectiveness and feasibility of IoT technology in designing campus intelligent security systems, which is of great significance to improve campus security management.

IoT-Enabled crop storage monitoring system

This paper introduces an innovative IoT-enabled crop storage monitoring system designed to revolutionize the preservation and quality assurance of stored agricultural produce. Central to this system are IoT sensors strategically deployed within storage facilities. These sensors are tasked with the continuous tracking and monitoring of crucial environmental parameters, specifically temperature, humidity, and gas levels. Leveraging this real-time data, the system is engineered to promptly detect and respond to any deviations from the prescribed optimal storage conditions. The core strength of this system lies in its ability to generate instantaneous alerts upon detecting irregularities. These alerts serve as preemptive measures, effectively averting potential spoilage and curtailing post-harvest losses by enabling timely interventions and corrective actions. By harnessing IoT technology, this paper aims to create a proactive, automated, and responsive framework that ensures the integrity and safety of stored agricultural produce, ultimately contributing to enhanced food security and sustainability.

Application of Benthic Microbial Fuel Cells in Systems of Year-Round Monitoring of Water Environment Parameters

Application of Benthic Microbial Fuel Cells in Systems of Year-Round Monitoring of Water Environment Parameters

Indoor monitoring and long-term survey to identify the risks of Energy Poverty: the case of social housing in Northern Italy

Low-income families often live in poorly heated houses belonging to social housing programs. Tenants’ wellbeing and health in social housing is typically threatened by problems associated with energy inefficiency and poor Indoor Environmental Quality (IEQ). These conditions are among the symptoms of a social issue known as Energy Poverty (EP), which occurs when residents face difficulty in paying energy bills. In 2020 about 2.1 million households (8% of the total) were suffering from energy poverty in Italy. The risk of EP is mainly favored by three factors: the low income of the family, the high final price of energy and the poor technical construction characteristics of the building which can lead to poor IEQ. Four indicators have been proposed by the European Commission and some others developed in the scientific literature, but often neither the thresholds to assess the status of EP nor the methodology to collect data have been defined. This study aims to assess some of the recommended European indicators on a case study. The risks of EP have been investigated in social housing located in Northern Italy by means of an integrated methodology based on a site inspection, a survey, and the continuous monitoring of the indoor environmental parameters during the winter season. The proposed method allowed detecting the presence of EP in 5 dwellings out of 8.

Genomic diversity of Vibrio spp. and metagenomic analysis of pathogens in Florida Gulf coastal waters following Hurricane Ian.

Changing climatic conditions influence parameters associated with the growth of pathogenic Vibrio spp. in the environment and, hence, are linked to increased incidence of vibriosis. Between 1992 and 2022, a long-term increase in Vibrio spp. infections was reported in Florida, USA. Furthermore, a spike in Vibrio spp. infections was reported post Hurricane Ian, a category five storm that made landfall in Florida on 28 September 2022. During October 2022, water and oyster samples were collected from three stations in Lee County in an area significantly impacted by Ian. Vibrio spp. were isolated, and whole-genome sequencing and phylogenetic analysis were done, with a focus on Vibrio parahaemolyticus and Vibrio vulnificus to provide genetic insight into pathogenic strains circulating in the environment. Metagenomic analysis of water samples provided insight with respect to human health-related factors, notably the detection of approximately 12 pathogenic Vibrio spp., virulence and antibiotic resistance genes, and mobile genetic elements, including the SXT/R391 family of integrative conjugative elements. Environmental parameters were monitored as part of a long-term time series analysis done using satellite remote sensing. In addition to anomalous rainfall and storm surge, changes in sea surface temperature and chlorophyll concentration during and after Ian favored the growth of Vibrio spp. In conclusion, genetic analysis coupled with environmental data and remote sensing provides useful public health information and, hence, constitute a valuable tool to proactively detect and characterize environmental pathogens, notably vibrios. These data can aid the development of early warning systems by yielding a larger source of information for public health during climate change. Evidence suggests warming temperatures are associated with the spread of potentially pathogenic Vibrio spp. and the emergence of human disease globally. Following Hurricane Ian, the State of Florida reported a sharp increase in the number of reported Vibrio spp. infections and deaths. Hence, monitoring of pathogens, including vibrios, and environmental parameters influencing their occurrence is critical to public health. Here, DNA sequencing was used to investigate the genomic diversity of Vibrio parahaemolyticus and Vibrio vulnificus, both potential human pathogens, in Florida coastal waters post Hurricane Ian, in October 2022. Additionally, the microbial community of water samples was profiled to detect the presence of Vibrio spp. and other microorganisms (bacteria, fungi, protists, and viruses) present in the samples. Long-term environmental data analysis showed changes in environmental parameters during and after Ian were optimal for the growth of Vibrio spp. and related pathogens. Collectively, results will be used to develop predictive risk models during climate change.

Examining Occupant-Comfort Responses to Indoor Humidity Ratio in Conventional and Vernacular Dwellings: A Rural Indian Case Study

Optimum indoor humidity is often associated with comfort and overall well-being. Occupant comfort is often evaluated with a focus on “thermal comfort” using the PMV (predicted mean vote), PDD (predicted percentage of dissatisfied), and adaptive thermal comfort models. Humidity-determined comfort parameters, like skin and respiratory comfort, are well acknowledged in the scientific community, but strangely not considered for indoor comfort computations. This study proposes a new computational approach for describing and evaluating humidity-related skin comfort in buildings using skin temperature, evaporative loss, and skin wettedness as critical parameters. The Development and validation of the computational model was demonstrated through a case study in a rural Indian context. The case study involves real-time monitoring of indoor environmental parameters and humidity-determined occupant comfort votes recorded through a novel aggregated humidity comfort vote method. The simulation results were compared with the community comfort/health survey. It was observed that, even at neutral skin temperatures, an increase in skin wettedness increases the thermal sensation vote. Clothing varies according to gender, community, and personal preferences, influencing physiological parameters which determine comfort. The acceptable humidity ratio was found to be in the range of 17.4 to 22.6 g-wv/kg-da for Indian participants. Including humidity-related comfort parameters in building simulation tools would aid in selecting building materials for improved indoor comfort.

Monitoring water quality metrics of ponds with IoT sensors and machine learning to predict fish species survival

Aquaculture involves cultivating various marine and freshwater aquatic creatures within regulated environments. Monitoring the aquatic environmental conditions in real-time is crucial for successful fish farming. The Internet of Things (IoT) offers significant potential for real-time monitoring, and this paper introduces an IoT framework designed for efficient monitoring and effective control of various water-related aquatic environmental parameters. The proposed system is implemented as an embedded system utilizing sensors and an Arduino microcontroller. In cultivating pond water, diverse sensors such as pH, temperature, and turbidity sensors are deployed, with each sensor connected to an Arduino Uno-based microcontroller board. These sensors collect data from the water, which is then stored as a CSV file in an IoT cloud platform called ThingSpeak through the Arduino microcontroller. To gather data for analysis, we conducted measurements across five ponds, varying in size and environmental conditions. After getting the real-time data, we compared our experimental results with the standard reference values. As a result, we could take the decision of whether a pond is suitable for cultivating fish or not. After that, we labeled the data with 11 fish categories: Katla, sing, prawn, shrimp, rui, tilapia, pangas, karpio, magur, silver carp, and koi. The data was analyzed using 10 machine learning (ML) algorithms, including J48, Random Forest, K Nearest Neighbors (K-NN), K*, Logistic Model Tree (LMT), Reduced Error Pruning Tree (REPTree), Jumping Rule Inference with Pruned Search (JRIP), Partial Decision Trees (PART), Decision Table, and Logit boost. After experimental analyses, it was discovered that only three of the five ponds were ideal for fish farming, and those three ponds only met the required standards for pH, Temperature, Turbidity, and Conductivity. Among the state-of-art machine learning algorithms, Random Forest achieved the highest score of performance metrics as accuracy 94.42%, kappa statistics 93.5%, and Avg. TP Rate 94.4%. In addition, we calculated the Biochemical Oxygen Demand (BOD), Chemical Oxygen Demand (COD), and Dissolved Oxygen (DO) for one scenario. This study includes prototype hardware details of the proposed IoT system.

Under the Background of Industry-Education Integration--Real-Time Environmental Inspection--Exploration and Practice of Teaching Mode

This article mainly discusses the application of real-time environmental monitoring technology in educational practices. It begins by introducing the basic principles of this technology, which involves real-time monitoring of environmental parameters through sensors and the use of data analysis and processing for real-time evaluation and feedback. It then delves into the specific applications of real-time environmental monitoring technology in teaching, including classroom instruction and laboratory practices. The article analyzes the advantages of this technology in teaching, such as real-time monitoring, objectivity, and personalization, while also addressing challenges such as sensor reliability and accuracy, teacher training, and strategy improvement. Finally, it provides methods and strategies to address the challenges in the application of real-time environmental monitoring technology, including improving sensor technology, enhancing teacher training, and refining teaching strategies. Overall, real-time environmental monitoring technology has significant potential for application in education, but it also requires addressing various technological and educational challenges.

THE INTEGRATION OF REMOTE SENSING AND GEOGRAPHIC INFORMATION SYSTEM (GIS) IN MANAGING URBAN ECOSYSTEMS

Abstract. Urban ecosystems face numerous challenges due to rapid urbanization and population growth. Effective management of these ecosystems is crucial to ensure their sustainability and the well-being of urban residents. Remote sensing (RS) and Geographic Information Systems (GIS) have emerged as valuable tools for understanding and managing urban ecosystems. The integration of remote sensing and GIS technologies facilitate the monitoring and assessment of urban biodiversity, aiding in the conservation and restoration of ecological habitats. With this mind, the objective of this study was to investigate the integration of remote sensing and GIS technologies for real-time monitoring and assessment of environmental parameters in urban ecosystems, and their role in supporting sustainable urban ecosystem conservation efforts. Landsat 8 Operational Land Imager (OLI) images were acquired between January 2nd and April 5th 2020 to assess and monitor the dynamics in urban ecosystems in Abidjan, Accra, and Lagos. The Normalized Difference Built-up index was used to detect areas covered with concrete structures and impervious surfaces, while the Normalized Difference Vegetation Index and Normalized Difference Water Index were used to detect areas covered with vegetation and water bodies, respectively. Results of the study show that Abidjan, Accra, and Lagos experienced increased built-up areas at the expense of other land uses such as forests. Remote Sensing and GIS technologies provide valuable insights into the spatial and temporal dynamics of urban environments, supporting evidence-based decision-making and sustainable urban planning and development.

Design and implementation of regional integrated environmental measurement for China space station

The monitoring of cabin air pressure, temperature, humidity, microgravity and other environmental parameters is very important for the long-term and reliable operation of large manned spacecraft. Based on the characteristics of 1553B bus, a design method of regional integrated environmental measurement for the space station was proposed, which realized the high precision and reliable measurement of environmental parameters such as pressure, temperature, humidity and microgravity of china space station. The measurement data can accurately reflect the environmental changes of astronauts’ in-orbit living, working and extravehicular activities, which verifies the effectiveness of the design. It provides the necessary new principle application verification for higher precision pressure measurement in lunar landing missions.

Integrating blockchain and deep learning for intelligent greenhouse control and traceability

This research presents a solution that combines deep learning-based image processing, blockchain technology, and the Internet of Things (IoT) to achieve smarter control and traceability in greenhouse operations within the agricultural sector. By integrating these technologies, the aim is to overcome challenges posed by climate change, plant growth, limited agricultural land, and water scarcity, while enhancing crop yields and ensuring efficient and secure operations. The proposed system automates image capture, measurement, storage, and monitoring of environmental parameters in greenhouses, utilizing highly accurate image processing techniques with a 98% success rate. The integration of blockchain technology establishes an immutable and transparent record of transactions and data points, thereby improving traceability across the agricultural supply chain. This comprehensive approach fosters accountability, transparency, and trust, empowering consumers to make well-informed decisions regarding the products they purchase. Ultimately, this research contributes to advancing efficient and sustainable agricultural practices.

A Sustainable Indoor Air Quality Monitoring Approach through Potable Living Wall for Closed Confined Spaces: A Way Forward to Fight Covid19

The COVID-19 pandemic has greatly influenced various aspects of life, part of which has consequently paved the way toward improvements in building design criteria, especially for closed confined spaces. The closed confined spaces are directly proportional to the quantity and quality of the volatile organic compounds (VOCs) present in the atmosphere, from which human beings breathe. In managing the impact produced by VOCs, a practical, sustainable, economical and environmentally friendly concept of indoor living walls has become a prominent feature for improving the indoor air quality (IAQ) of closed confined spaces to efficiently reduce sick building syndrome (SBS) factors. In modification of common practice of ventilation systems, living wall technology leverages the natural ability of plants to purify indoor air quality by reducing air pollutants and allows the recycling of indoor air and the creation of a productive and inspiring environment. In this paper, the concept of a portable living wall through the use of a native plant species locally available in Sindh, Pakistan is introduced. Herein, the portable living concept was assessed by means of the design, construction, and data collection (testing and monitoring) of various environmental parameters carried out before and after the installation of the living wall. The study was monitored for 90 days, and analyses for various types of air pollutants were carried out in the environmental laboratory. During the monitoring period, the parameters humidity, VOCs, hazardous chemicals of concern (HCOC), CO2 and CO showed reductions in their values, with changes observed ranging from 61.5 to 58%, 0.66 to 0.01 ppm, 0.2 to 0.01 ppm, 1070 to 528 ppm and 0.2 to 0.01 ppm, respectively. The outcomes showed noticeable changes in air pollutants coupled with reductions in heating, ventilation and air conditioning (HVAC) energy consumption by up to 25%, mainly due to limited air requirements for ventilation.

Quantitative relationships among high-throughput sequencing, cyanobacteria toxigenic genotype abundance and microcystin occurrence in bathing waters

Toxin-producing cyanobacteria pose significant threats to human and animal health if exposed during recreational activities in bathing waters. To better safeguard public health and reduce health risks during the bathing season, an effective monitoring and management strategy is required. Molecular tools used to monitor toxigenic cyanobacteria have been evaluated on the basis of the efficiency and applicability of the method used to (i) establish an early-warning monitoring strategy for EU bathing water sites using both targeted quantitative polymerase chain reaction (qPCR) and non-targeted high-throughput sequencing (HTS) genotype analysis and (ii) to compare the toxigenic potential of cyanobacteria with actual microcystin (MC) occurrence and concentrations. For this purpose, 16 bathing water sites were monitored according to the bathing water directive (BWD) of the European Union (EU) during the bathing season of the summer of 2020 in eastern Austria. The cyanobacterial community composition was analyzed through HTS and qPCR by targeting the microcystin synthetase B gene (mcyB), which indicates MC synthesis within the genera Microcystis and Planktothrix. Within the genus Microcystis, which was identified as the primary MC producer, the mcyB genotypes formed stable subpopulations that increased linearly in correlation with the total Microcystis population. Notably, the HTS cell equivalents assigned to Microcystis and Planktothrix correlated with the corresponding qPCR estimates of genotype abundance, which serves as a confirmation of the suitability of (semi)-quantitative sequencing through HTS. In addition to the elevated trophic state, reduced transparency, increasing water temperatures, as well as cyanobacterial HTS read numbers and Microcystis cell number equivalents per mL estimated through qPCR, were associated with positive MC samples. Therefore, in combination with the monitoring of standard environmental parameters, the use of HTS and qPCR techniques is considered highly useful to ensure the timely identification of health risks to recreational users, as mandated by the BWD.

A secured industrial wireless iot sensor network enabled quick transmission of data with a prototype study

The Wireless IoT sensor network (IWSN) has significant potential in industrial settings, but to fully realize its benefits, a robust and scalable computer system is required to handle the continuous influx of data from various applications. In this research study, we propose an IoT sensor-cloud architecture that integrates WSN with cloud technology, providing a unique data analytics framework for highly secure analysis of sensor data. The proposed architecture emphasizes effective interoperability mechanisms in the cloud, and provides an IPv6 extensible enterprise WSN design and simulation technique. To demonstrate the effectiveness of our proposed architecture, we track the pH, resistivity, and dissolved oxygen levels of industrial effluents that are discharged into water sources. We use AT instructions in conjunction with the HTTP GET technique to gather and upload detector data to the ThingSpeak cloud through a GPRS internet connection, enabling real-time online monitoring and control using IoT functionality. The proposed architecture uses a distributed approach to handle high volumes of incoming data from the IoT sensors, storing the data in a scalable and accessible way for analysis. Real-time analysis is performed using a combination of batch and stream processing frameworks and machine learning algorithms, and the results are visualized using a web-based dashboard that provides real-time updates on key metrics and allows users to explore the data in different ways. Security is a top priority in our proposed architecture, and we use encryption technologies such as SSL/TLS and access control mechanisms such as OAuth2 to ensure the secure transmission and storage of sensitive industrial IoT data. The architecture is designed to be scalable and adaptable to handle a wide range of IoT use cases in industrial settings. The proposed IoT sensor-cloud architecture provides a robust and scalable solution for the collection, analysis, and exchange of significant amounts of IoT sensor information, enabling real-time monitoring and control of critical environmental parameters in industrial settings.