Real-time Environmental Data Research Articles

The design and implementation of a smart greenhouse control system

Abstract Against the dual backdrop of continuously improving agricultural production levels and the global scarcity of water resources, countries worldwide are actively exploring the topic of water conservation. The development of an intelligent irrigation control system could make a significant contribution to water conservation efforts, while also helping to increase crop yields. This project adopts a modular design approach, utilizing sensors to collect real-time environmental data surrounding the crops. If the data falls outside the predefined optimal range for crop growth, the system automatically initiates irrigation, cooling, and supplemental lighting. The hardware design primarily consists of a data perception module and a control terminal module. After testing, the system runs stably and can accurately obtain information such as soil moisture, light intensity and ambient temperature, and basically realize the digital intelligent control of crop growing environment.

A Hybrid Machine Learning Approach: Analyzing Energy Potential and Designing Solar Fault Detection for an AIoT-Based Solar–Hydrogen System in a University Setting

This research aims to optimize the solar–hydrogen energy system at Kangwon National University’s Samcheok campus by leveraging the integration of artificial intelligence (AI), the Internet of Things (IoT), and machine learning. The primary objective is to enhance the efficiency and reliability of the renewable energy system through predictive modeling and advanced fault detection techniques. Key elements of the methodology include data collection from solar energy production and fault detection systems, energy potential analysis using Transformer models, and fault identification in solar panels using CNN and ResNet-50 architectures. The Transformer model was evaluated using metrics such as Mean Absolute Error (MAE), Mean Squared Error (MSE), and an additional variation of MAE (MAE2). Known for its ability to detect intricate time series patterns, the Transformer model exhibited solid predictive performance, with the MAE and MAE2 results reflecting consistent average errors, while the MSE pointed to areas with larger deviations requiring improvement. In fault detection, the ResNet-50 model outperformed VGG-16, achieving 85% accuracy and a 42% loss, as opposed to VGG-16’s 80% accuracy and 78% loss. This indicates that ResNet-50 is more adept at detecting and classifying complex faults in solar panels, although further refinement is needed to reduce error rates. This study demonstrates the potential for AI and IoT integration in renewable energy systems, particularly within academic institutions, to improve energy management and system reliability. Results suggest that the ResNet-50 model enhances fault detection accuracy, while the Transformer model provides valuable insights for strategic energy output forecasting. Future research could focus on incorporating real-time environmental data to improve prediction accuracy and developing automated AIoT-based monitoring systems to reduce the need for human intervention. This study provides critical insights into advancing the efficiency and sustainability of solar–hydrogen systems, supporting the growth of AI-driven renewable energy solutions in university settings.

Research on Obstacle Avoidance Strategy of Automated Heavy Vehicle Platoon in High-Speed Scenarios

Amidst the rapid progression of the road transport industry, the safety and efficiency of heavy-vehicle platoons have garnered significant attention. The study tackles the challenge of obstacle avoidance presented by vehicles owing to their considerable mass, delayed response times, and line-of-sight impediments, by introducing a cooperative obstacle avoidance system for heavy-vehicle platoons based on deep reinforcement learning. The system comprises three primary modules: perception, decision-making, and control. Initially, the perception module acquires real-time environmental data. Subsequently, the decision-making module formulates obstacle avoidance decisions based on the acquired data. Specifically, it implements a two-stage braking obstacle avoidance strategy under low collision risk scenarios, while employing a fifth-degree polynomial for planning and tracking obstacle avoidance paths under high collision risk conditions suitable for steering maneuvers. The control module utilizes the local multi-agent deep deterministic policy gradient (LADDPG) algorithm to train the heavy-vehicle platoon agents, ensuring the formation’s maintenance while mitigating collisions with other vehicles and obstacles. The effectiveness of the proposed system is substantiated through simulation experiments, demonstrating its adaptability to various traffic conditions, selection of suitable obstacle avoidance strategies, and significant enhancement of obstacle avoidance performance and heavy-vehicle platoon stability.

Enhanced IoT Spectrum Utilization: Integrating Geospatial and Environmental Data for Advanced Mid-Band Spectrum Sharing.

The anticipated surge of Internet of Things (IoT) devices is expected to intensify the demand for mid-band spectrum resources, posing challenges to traditional spectrum sharing methods. This paper addresses the limitations of static database-assisted spectrum management frameworks and proposes a novel approach integrating high-resolution geospatial and real-time environmental data. Leveraging these inputs, the proposed framework enhances spectrum allocation accuracy, and mitigates interference more effectively, thereby increasing the access opportunities for IoT deployments. A detailed example scenario illustrates the efficacy of the proposed approach, demonstrating significant gains in spectrum sharing efficiency. It shows gains in the number of new entrants accessing the spectrum, ranging from 77% to 140%. These gains occur when moving to less conservative interference conditions and including more complex geospatial information in the propagation environment. These findings underscore the critical role of advanced spectrum sharing techniques in optimizing spectrum utilization for future IoT networks.

Botanic Monitoring of Plant Using Raspberry Pi

The project leverages the Raspberry Pi, a cost- effective board computer, to cultivate an all-encompassing botanic monitoring and control system. By amalgamating sensors, cameras, and actuators with the Raspberry Pi's computing power, real-time environmental data can be gleaned, refined, and tapped remotely via web or cell phone interface. This metaphysical marks the Raspberry Pi's purpose in establishing a diverse, cost-effective panacea for botanic monitoring and control, with benefits including precision, mechanization, and accessibility over agriculture and environmental research. Keywords— Nutrition Detection, Sensor Network, Wireless Communication, Precision agriculture, Design, Plant monitoring.

Risks and Safety of CO2 Pipeline Transport: A Case Study of the Analysis and Modeling of the Risk of Accidental Release of CO2 into the Atmosphere

In the field of CO2 capture and sequestration, ensuring the safety of pipeline infrastructure is paramount to successful climate change mitigation efforts. This study investigates the dynamics of CO2 dispersion from pipeline systems, assessing not only the transport process but also the physical properties and associated hazards. Advanced simulation techniques are used to model how different states of CO2 (gas, liquid, and supercritical) and varying pipeline characteristics—such as perforation sizes, flow rates, and orientations—affect the dispersion patterns in the event of a leak. Simulations cover a range of atmospheric conditions, emphasizing the role of atmospheric stability and wind speed in shaping dispersion and defining potential impact zones. An analysis of historical pipeline accidents is included to inform risk management strategies. The results show that the orientation of the pipeline has a significant effect on dispersion, with downward leaks causing the largest impact zones, particularly under supercritical conditions. The results highlight the need for adaptive safety strategies that take into account real-time CO2 transport conditions and localized environmental data. By integrating these factors, the study recommends refining safety protocols and emergency response strategies to improve pipeline resilience and public safety against potential leaks. Key findings include the quantification of the relationship between leak parameters and dispersion areas, providing a valuable framework for future safety improvements.

Multi-Objective Particle Swarm Optimization for Enhancing Chiller Plant Efficiency and Energy Savings

This study aims to enhance operational efficiency in chiller plants by implementing the Multi-Objective Particle Swarm Optimization (MOPSO) algorithm. The primary objectives are to simultaneously reduce energy consumption and increase cooling efficiency, addressing the challenges posed by variable environmental and operational conditions. Employing the MOPSO algorithm, this research conducts a detailed analysis using real-time environmental data and operational parameters. This approach facilitates a dynamic adaptation to changes in ambient temperature and electricity pricing, ensuring that the algorithm's application remains effective under fluctuating conditions. The application of MOPSO has resulted in significant reductions in energy use and improvements in cooling efficiency. These results demonstrate the algorithm's capacity to optimize chiller plant operations dynamically, adapting to changes in environmental conditions and operational demands. The study finds that MOPSO's adaptability to dynamic operational conditions enables robust energy management in chiller plants. This adaptability is crucial for maintaining efficiency and cost-effectiveness in industrial applications, especially under varying environmental impacts. The paper contributes to the field by enhancing the understanding of how advanced optimization algorithms like MOPSO can be effectively integrated into energy management systems for chiller plants. A novel aspect of this research is the integration of real-time data analytics into the optimization process, which significantly improves the sustainability and operational efficiency of HVAC systems. Furthermore, the study outlines the potential for similar research applications in large-scale HVAC systems, where such algorithmic improvements can extend practical benefits. The findings underscore the importance of considering a broad range of environmental and operational factors in the optimization process and suggest that MOPSO's flexibility and robustness make it a valuable tool for achieving sustainable and cost-effective energy management in industrial settings.

Selection of ideal emissivity spectrum for radiative cooling and its application in water harvesting

Radiative cooling, a novel cooling technology for condensation water harvesting without energy input, offers a promising solution to the water scarcity. According to the actual locations and atmospheric conditions, selecting the ideal radiative cooling spectrum is crucial to achieve improved condensation efficiency. In this work, we analyze the condensation rate and condensation power in the southern, middle and northern China with three ideal radiative cooling emitters, namely, 4-infinite, single-window, and double-window emitter. Depending on variations in temperature, relative humidity, or the heat transfer coefficient, an optimal emitter can be selected based on the calculated boundary conditions and differences in condensation rate. Meanwhile, three multilayer design of 4-infinite emitter, double-window emitter and single-window emitter are developed using the memetic algorithm. Their emissivity aligns well with the impedance theory. They are then applied to a case study that assesses condensed water collection in Hangzhou, utilizing real-time atmospheric and environmental data in the whole year. Regarding the all-day dew collection, the average daily water generation per week in Hangzhou can be up to 256.94 mLm−2. For the solar water purification, the average condensation rate throughout the year is 1.956 Lm−2hour−1 withh=10Wm−2K−1. Our findings demonstrate that the multilayer design significantly enhances the water harvesting. Hence, our research provides valuable insights for selecting suitable ideal radiative coolers for water harvesting in specific atmospheric conditions and geographical locations.

The application of artificial intelligence in Unmanned Underwater Vehicle communication systems

Artificial Intelligence (AI) has the potential to significantly enhance decision-making techniques in Unmanned Underwater Vehicle (UUV) communication systems by utilizing real-time sensor feedback and environmental data. To address the challenges posed by high data volume and error rates, we propose an advanced methodology known as the Variational Deep Network (VDN). This method integrates Variational Modal Decomposition (VMD) algorithms with the Deep Convolutional Neural Network (DCNN) to create a realizable AI decision-making system. In conjunction with the DCNN and VMD algorithms, our research focuses on accurately identifying received signals under data constraints. Considering the spectral efficiency and robustness, the Filter-Bank Multicarrier (FBMC) technology have been employed in this paper. Our results demonstrate that the VDN significantly reduces the volume of data needed for neural network training and effectively recognizes FBMC signals. These insights confirm the potential of VDN decision-making systems to drive advancements in UUV communication technologies.

Trustworthy Localization in IoT Networks: A Survey of Localization Techniques, Threats, and Mitigation.

The Internet of Things (IoT) has revolutionized the world, connecting billions of devices that offer assistance in various aspects of users' daily lives. Context-aware IoT applications exploit real-time environmental, user-specific, or situational data to dynamically adapt to users' needs, offering tailored experiences. In particular, Location-Based Services (LBS) exploit geographical information to adapt to environmental settings or provide recommendations based on users' and nodes' positions, thus delivering efficient and personalized services. To this end, there is growing interest in developing IoT localization systems within the scientific community. In addition, due to the sensitivity and privacy inherent to precise location information, LBS introduce new security challenges. To ensure a more secure and trustworthy system, researchers are studying how to prevent vulnerabilities and mitigate risks from the early design stages of LBS-empowered IoT applications. The goal of this study is to carry out an in-depth examination of localization techniques for IoT, with an emphasis on both the signal-processing design and security aspects. The investigation focuses primarily on active radio localization techniques, classifying them into range-based and range-free algorithms, while also exploring hybrid approaches. Next, security considerations are explored in depth, examining the main attacks for each localization technique and linking them to the most interesting solutions proposed in the literature. By highlighting advances, analyzing challenges, and providing solutions, the survey aims to guide researchers in navigating the complex IoT localization landscape.

Proposal of model for prediction of grape processing and spraying time by using IoT smart agriculture sensor data

The grape industry's impact on agriculture and the economy requires precise forecasting for processing and spraying schedules to optimize production. This article introduces an innovative IoT-based model for predicting optimal timings in grape processing and spraying. By integrating real-time environmental and viticultural data, the model improves decision-making, enhancing product quality, reducing energy consumption, and increasing operational efficiency. Crucially, SARIMA predictive algorithms forecast parameters like temperature, humidity, wind speed, and air pressure. This comprehensive model transforms the grape industry, offering advanced decision support and promoting sustainable, resource-efficient production. The research signals a potential shift to precision agriculture, balancing economic viability with environmental stewardship in grapes.

The role of IT in sustainable environmental management: A global perspective review

This review offers a comprehensive overview of the intricate relationship between Information Technology (IT) and sustainable environmental management on a global scale. As the world grapples with environmental challenges, understanding the pivotal role of IT in fostering sustainability becomes increasingly imperative. The review begins by acknowledging the pressing environmental issues faced globally, including climate change, resource depletion, and biodiversity loss. It highlights the potential of IT to serve as a transformative force in addressing these challenges and fostering sustainable practices across various industries. The review explores how IT contributes to environmental sustainability through improved monitoring, data collection, and analysis. It delves into the role of technologies such as Internet of Things (IoT) devices, sensors, and satellite imaging in providing real-time environmental data. This information enables better decision-making, resource management, and the implementation of eco-friendly practices. Furthermore, the review examines the role of IT in promoting energy efficiency and reducing carbon footprints. It discusses the adoption of smart grids, energy management systems, and sustainable software solutions that contribute to optimizing energy consumption and reducing environmental impact. The review also explores the concept of "green IT," emphasizing the importance of adopting environmentally friendly practices in the design, production, and disposal of IT equipment. It discusses initiatives aimed at minimizing electronic waste and promoting the circular economy within the IT industry. Additionally, the global perspective of the review sheds light on how IT facilitates collaboration and knowledge sharing among nations and organizations. The review underlines the significance of international cooperation in leveraging IT for sustainable environmental management, emphasizing the role of technology in achieving global environmental goals. In conclusion, this review underscores the multifaceted role of IT in promoting sustainable environmental management globally. It emphasizes the transformative potential of IT in addressing environmental challenges, fostering innovation, and driving collaborative efforts towards a more sustainable and resilient future.

Sound Speed Inversion Based on Multi-Source Ocean Remote Sensing Observations and Machine Learning

Ocean sound speed is important for underwater acoustic applications, such as communications, navigation and localization, where the assumption of uniformly distributed sound speed profiles (SSPs) is generally used and greatly degrades the performance of underwater acoustic systems. The acquisition of SSPs is necessary for the corrections of the sound ray propagation paths. However, the inversion of SSPs is challenging due to the intricate relations of interrelated physical ocean elements and suffers from the high costs of calculations and hardware deployments. This paper proposes a novel sound speed inversion method based on multi-source ocean remote sensing observations and machine learning, which adapts to large-scale sea regions. Firstly, the datasets of SSPs are generated utilizing the Argo thermohaline profiles and the empirical formulas of the sound speed. Then, the SSPs are analyzed utilizing the empirical orthogonal functions (EOFs) to reduce the dimensions of the feature space as well as the computational load. Considering the nonlinear regression relations of SSPs and the observed datasets, a general framework for sound speed inversion is formulated, which combines the designed machine learning models with the reduced-dimensional feature representations, multi-source ocean remote sensing observations and water temperature data. After being well trained, the proposed machine learning models realize the accurate inversion of the targeted ocean region by inputting the real-time ocean environmental data. The experiments verify the advantages of the proposed method in terms of the accuracy and effectiveness compared with conventional methods.

Enhanced Tropical Cyclone Precipitation Prediction in the Northwest Pacific Using Deep Learning Models and Ensemble Techniques

This study focuses on optimizing precipitation forecast induced by tropical cyclones (TCs) in the Northwest Pacific region, with lead times ranging from 6 to 72 h. The research employs deep learning models, such as U-Net, UNet3+, SE-Net, and SE-UNet3+, which utilize precipitation forecast data from the Global Forecast System (GFS) and real-time GFS environmental background data using a U-Net structure. To comprehensively make use of the precipitation forecasts from these models, we additionally use probabilistic matching (PM) and simple averaging (AVR) in rainfall prediction. The precipitation data from the Global Precipitation Measurement (GPM) Mission serves as the rainfall observation. The results demonstrate that the root mean squared errors (RMSEs) of U-Net, UNet3+, SE-UNet, SE-UNet3+, AVR, and PM are lowered by 8.7%, 10.1%, 9.7%, 10.0%, 11.4%, and 11.5%, respectively, when compared with the RMSE of the GFS TC precipitation forecasts, while the mean absolute errors are reduced by 9.6%, 11.3%, 9.0%, 12.0%, 12.8%, and 13.0%, respectively. Furthermore, the neural network model improves the precipitation threat scores (TSs). On average, the TSs of U-Net, UNet3+, SE-UNet, SE-UNet3+, AVR, and PM are raised by 12.8%, 21.3%, 19.3%, 20.7%, 22.5%, and 22.9%, respectively, compared with the GFS model. Notably, AVR and PM outperform all other individual models, with PM’s performance slightly better than AVR’s. The most important feature variables in optimizing TC precipitation forecast in the Northwest Pacific region based on the UNet-based neural network include GFS precipitation forecast data, land and sea masks, latitudinal winds at 500 hPa, and vertical winds at 500 hPa.

Wireless Sensor Networks for Green Cities: A Comprehensive Review of Environmental Pollution Monitoring

This research paper investigates the design and implementation of a Wireless Sensor Network (WSN)-Based Data Acquisition System tailored for collecting environmental pollution factors with a specific focus on contributing to the realization of a Green City. Leveraging various literature surveys on WSN and its applications with different techniques, a comprehensive analysis of the existing body of knowledge in the field. The paper explores the deployment of WSN technology as a cost-effective and scalable solution for real-time monitoring of pollution-related parameters, including air and water quality, noise levels, and other relevant environmental factors.By synthesizing insights from diverse literature sources, propose an intelligent and adaptable WSN framework capable of capturing, analyzing, and transmitting real-time environmental data. The research highlights the importance of integrating advanced sensor technologies within the WSN infrastructure to ensure a holistic monitoring of pollution factors. This analysis also delves into various data acquisition methodologies and explores the potential for advanced data analytics and visualization techniques to derive meaningful insights

Forecasting greenhouse air and soil temperatures: A multi-step time series approach employing attention-based LSTM network

Greenhouses stand as key infrastructural components in contemporary agriculture, facilitating the perennial availability of vegetables. Harnessing accurate, real-time environmental data, particularly air and soil temperature, is pivotal to promoting the facility’s efficiency in pest and disease alarming and vegetable production. Nevertheless, despite consistent long-term temperature trends, short-term fluctuations prove to be considerably nonlinear, time-lagged, and multivariate-coupled due to the unique microclimate of the greenhouse. Addressing this, we propose a novel short-term multi-step prediction model that assimilates a range of environmental data including air temperature, humidity, soil temperature, and soil moisture content at diverse greenhouse heights. This model utilizes an Attention-LSTM based time series approach to accurately predict multi-step short-term temperature variations within the greenhouse. By employing a modest amount of historical data (approximately 48 h), the model provides future temperature forecasts within a range of 30 to 480 min with high accuracy. When benchmarked against renowned models such as RNN, GRU, and LSTM networks, our proposed models display impressive performance. It achieved R2 values of 0.93, 0.94, 0.95, and 0.86 for 1, 4, 8, and 16-step predictions, respectively, in the domain of air temperature forecasting. The corresponding MSE values stood at 0.42, 0.32, 0.27, and 0.81. In parallel, for soil temperature forecasts, our model recorded R2 values of 0.96, 0.94, 0.92, and 0.89 for 1, 4, 8, and 16-step predictions, respectively, alongside MSE values of 0.36, 0.48, 0.73, and 0.99 for the equivalent steps. To conclude, these results reveal the exceptional predictive aptitude of our model for both air and soil temperature forecasts within greenhouse environments, validating its standing as an effective tool for the optimization of vegetable farming practices. Consequently, our model demonstrates its ability to yield precise predictions of environmental variables within greenhouses, offering crucial early warning mechanisms for the enhancement of vegetable cultivation processes.

인공지능(AI)을 활용한 테러 예방 프로그램 구축 방안

The study explores the development of an effective counterterrorism program utilizing Artificial Intelligence (AI) technologies. Emphasizing the integration of various technical elements such as big data analysis, natural language processing, image and video analysis, and the incorporation of Internet of Things (IoT) and sensor technologies, the research investigates comprehensive approaches. Firstly, the utilization of big data analysis aims to collect and analyze data from diverse sources, identifying patterns associated with terrorism. The application of natural language processing and text mining extracts meaningful information from textual data, while image and video analysis detect unusual patterns or behaviors in visual information. Moreover, the study highlights the use of IoT and sensor technologies to establish an intelligent security system, collecting real-time environmental data to detect anomalous indicators. The implementation of an automated alert and emergency response system is also suggested, facilitating swift and effective responses upon detecting abnormal signs. In conclusion, the integrated use of diverse technologies in the proposed AI-based counterterrorism program enables rapid and accurate threat detection, contributing to the enhancement of public safety and national security.

Perception and Decision-Making for Multi‑Modal Interaction Based on Fuzzy Theory in the Dynamic Environment

The projection-augmented reality assembly assistance system, which supports natural multi-modal human–computer interaction, has the potential to improve interaction efficiency. However, the effectiveness of the interaction method is compromised by the interference caused by the dynamically changing industrial environment. To address this issue, this study proposed a multi-model interaction model based on fuzzy theory and developed an environment perception decision-making interaction system. This system continuously monitors real-time environment data, evaluates interaction performance, and visually recommends the optimal method to assist users in completing their tasks. A comparative experiment was conducted between the proposed system and a non-environment perception interaction system. The collected data, including interaction success rate, task time, system usability, and user experience, were analyzed. The results demonstrated that the proposed system effectively enhances the adaptability and comfort of computer systems in complex environments and improves communication efficiency between humans and machines.

Real-Time Air Density Measurement with IoT Integration

This paper presents the design and implementation of an IoT-based air density measurement system that integrates BMP180 and DHT22 sensors with an Arduino Uno microcontroller and ESP8266 Wi-Fi module. The system measures temperature, humidity, and atmospheric pressure to calculate air density in real-time, displaying the results on an LCD screen and transmitting the data to a smartphone app (Blynk) for remote monitoring. The goal of this research is to create a reliable, automated system capable of providing continuous air density measurements without the need for manual intervention. To evaluate the system’s accuracy, data were collected and compared with reference values from the Korea Meteorological Administration (KMA) for August 2023. The comparison revealed that the system produced air density measurements with an average error of less than 0.2%, demonstrating its high level of accuracy and reliability. The system is particularly suited for laboratory environments, where real-time and accurate air density measurements are essential. The use of IoT technology allows for remote data access and continuous monitoring, making the system convenient for various applications, including environmental monitoring and industrial settings where air density plays a crucial role. Future improvements could include sensor calibration enhancements and the integration of additional environmental parameters, such as CO2 levels or particulate matter, to broaden the system’s functionality. Overall, the IoT-based air density measurement system offers a cost-effective and scalable solution for real-time environmental data monitoring.

Adaptive Smart eHealth Framework for Personalized Asthma Attack Prediction and Safe Route Recommendation

Recently, there has been growing interest in using smart eHealth systems to manage asthma. However, limitations still exist in providing smart services and accurate predictions tailored to individual patients’ needs. This study aims to develop an adaptive ubiquitous computing framework that leverages different bio-signals and spatial data to provide personalized asthma attack prediction and safe route recommendations. We proposed a smart eHealth framework consisting of multiple layers that employ telemonitoring application, environmental sensors, and advanced machine-learning algorithms to deliver smart services to the user. The proposed smart eHealth system predicts asthma attacks and uses spatial data to provide a safe route that drives the patient away from any asthma trigger. Additionally, the framework incorporates an adaptation layer that continuously updates the system based on real-time environmental data and daily bio-signals reported by the user. The developed telemonitoring application collected a dataset containing 665 records used to train the prediction models. The testing result demonstrates a remarkable 98% accuracy in predicting asthma attacks with a recall of 96%. The eHealth system was tested online by ten asthma patients, and its accuracy achieved 94% of accuracy and a recall of 95.2% in generating safe routes for asthma patients, ensuring a safer and asthma-trigger-free experience. The test shows that 89% of patients were satisfied with the safer recommended route than their usual one. This research contributes to enhancing the capabilities of smart healthcare systems in managing asthma and improving patient outcomes. The adaptive feature of the proposed eHealth system ensures that the predictions and recommendations remain relevant and personalized to the current conditions and needs of the individual.