Learnviro: An IoT-Based Innovation for Monitoring the Learning Environment

We reviewed the current key techniques of portable mass spectrometer and discussed its application in environmental monitoring. At present, mass spectrometer has two development directions. One direction is high-throughputs, high-sensitivity and high-resolution laboratory mass spectrometer. The other is small, in-situ monitoring portable mass spectrometer. With the rapid development of electronic technology and new materials, portable mass spectrometers are widely used in environmental pollution monitoring because they have the advantages of small size, light weight and low power consumption.

This chapter presents some considerations related to the applications in environment monitoring of some concepts as: estimation, fault detection and diagnosis, theory of distributed parameter systems and artificial intelligence based on the modern technology of wireless sensor networks. All these concepts allow treatment of large, complex, non-linear and multivariable system of the environment by learning and extrapolation. The environment may be seen as a complex ensemble of different distributed parameter systems, described with partial differential equations. Sensor networks (Akyildiz & all, 2002) have large and successful applications in monitoring the environment, they been capable to measure, as a distributed sensor, the physical variables, on a large area, which are characterizing the environment, and also to communicate at long distance the measured values, from the distributed parameter environmental processes. A lot of papers and books have been published in the fields of using sensor networks in environment monitoring in the last years. Some related work is surveyed as follows. The paper (Cuiyun & all., 2006) presents some research consideration related the changes of urban spatial thermal environment, for sustainable urban development, to improve the quality of human habitation environment. The urban thermal phenomenon is revealed using thermal remote sensing imagery, based on the instantaneous radiant temperature of the land surfaces. An architecture of sensor network for environment is presented in (Lan & all, 2008). Environmental pollution and meteorological processes may be studied using various kinds of environmental sensor networks. The modern intelligent sensor networks comprise automatic sensor nodes and communication systems which communicate their data to a sensor network server, where these data are integrated with other environmental information. The paper (Giannopoulos & all, 2009) presents the design and implementaion of a wireless sensor network for monitoring environmental variables and evaluates its effectiveness. It has application in environment variable monitoring such as: temperature, humidity, barometric pressure, soil moisture and ambient light, for research in agriculture, habitat monitoring, weather monitoring and so on. In order to improve the capacity of the environmental sensor networks different techniques may be used. The paper (Talukder & all, 2008) is using a model predictive control for optimal resource management in environment sensor networks, for with application at spatiotemporal events of a coastal monitoring and forecast system. The paper (Dardari & all, 2007)

Simple SummaryEfficient health monitoring in large-scale rabbit farms is essential to reduce economic losses and improve animal welfare. However, traditional manual inspection methods are labor-intensive, prone to error, and struggle to meet the demands of real-time monitoring in high-density farms. In this study, we present a gantry-based inspection system specifically designed for commercial rabbit farming. The system integrates a three-axis motion structure with imaging and environmental sensing modules, enabling automated inspection along fixed paths above the cages. It can acquire high-resolution images and monitor environmental conditions such as temperature, humidity, and light intensity without disturbing the animals. Field experiments show that the system maintains stable movement and achieves complete data collection. A fusion-based network is trained using the collected images and is able to identify dead rabbits with high accuracy, and the network is then deployed to the system for further application. The results demonstrate that the proposed system offers a reliable and non-intrusive solution for real-time monitoring in high-density rabbit houses. It can support early detection of health issues, reduce labor requirements, and promote more intelligent and sustainable farm management practices.The rising global demand for high-quality animal protein has driven the development of advanced technologies in high-density livestock farming. Rabbits, with their rapid growth, high reproductive efficiency, and excellent feed conversion, play an important role in modern animal agriculture. However, large-scale rabbit farming poses challenges in timely health inspection and environmental monitoring. Traditional manual inspections are labor-intensive, prone-to-error, and inefficient for real-time management. To address these issues, we propose Livestock Environmental Monitoring System–Gantry (LIVEMOS-G), an intelligent gantry-based monitoring system tailored for large-scale rabbit farms. Inspired by plant phenotyping platforms, the system integrates a three-axis motion module with multi-source imaging (RGB, depth, near-infrared, thermal infrared) and an environmental sensing module. It autonomously inspects around the farm, capturing multi-angle, high-resolution images and real-time environmental data without disturbing the rabbits. Key environmental parameters are collected accurately and compared with welfare standards. After training on an original dataset, which contains a total of 2325 sets of images (each set includes RGB, NIR, TIR, and depth image), the system is able to detect dead rabbits using a fusion-based object detection model during inspections. LIVEMOS-G offers a scalable, non-intrusive solution for intelligent livestock inspection, contributing to enhanced biosecurity, animal welfare, and data-driven management in high-density, modern rabbit farms. It also shows the potential to be extended to other species, contributing to the sustainable development of the animal farming industry as a whole.

With the rapid development of the power industry, the safe and stable operation of overhead distribution network lines has become crucial for ensuring reliable electricity supply. Traditional manual inspection methods have limitations in meeting real-time monitoring requirements, particularly in complex environments where real-time equipment status tracking poses significant challenges. The emergence of digital and intelligent technologies has provided new solutions for distribution network monitoring. This paper aims to develop an intelligent digital monitor adaptable to various working environments with multiple monitoring functions, thereby enhancing operational monitoring capabilities for overhead distribution lines. The monitor can achieve real-time temperature monitoring, environmental monitoring, fire warning, and status alerts, significantly improving monitoring efficiency and quality while ensuring secure and stable line operations. Additionally, through the development of supporting application software, monitoring data can be integrated into the digital distribution network platform, enabling remote video surveillance and diversified applications to expand monitoring scope and application scenarios.

Rapid urbanization presents significant challenges in energy consumption, noise control, and environmental sustainability. Smart cities aim to address these issues by leveraging information technologies to enhance operational efficiency and urban liveability. In this context, urban sound recognition supports environmental monitoring and public safety. This study provides a comparative evaluation of three machine learning models—convolutional neural networks (CNNs), long short-term memory (LSTM), and dense neural networks (Dense)—for classifying urban sounds. The analysis used the UrbanSound8K dataset, a static dataset designed for environmental sound classification, with mel-frequency cepstral coefficients (MFCCs) applied to extract core sound features. The models were tested in a fog computing architecture on AWS to simulate a smart city environment, chosen for its potential to reduce latency and optimize bandwidth for future real-time sound-recognition applications. Although real-time data were not used, the simulated setup effectively assessed model performance under conditions relevant to smart city applications. According to macro and weighted F1-score metrics, the CNN model achieved the highest accuracy at 90%, followed by the Dense model at 84% and the LSTM model at 81%, with the LSTM model showing limitations in distinguishing overlapping sound categories. These simulations demonstrated the framework’s capacity to enable efficient urban sound recognition within a fog-enabled architecture, underscoring its potential for real-time environmental monitoring and public safety applications.

Soil Water Content (SWC) is a key information in precision agriculture for obtaining high levels of efficiency and health of crops, while reducing water consumption. In particular, for the case of vineyards, due to the recent extreme temperature fluctuations, the knowledge of the SWC of the entire field becomes crucial to allow a timely intervention with emergency irrigation to preserve plant health and yield.Unlike electromagnetic SWC measurements, that are punctual and gravimetric measurements, that are punctual and also time-consuming, the Proximal Gamma Ray Spectroscopy (PGRS) technique can provide field-scale, non-invasive, and real-time measurements of SWC. This is achievable through an in-situ NaI detector, continuously recording photons resulting from the radioactive decay of 40K in the soil, which are attenuated proportionally based on the amount of stored water. Given the inverse proportionality between soil moisture and photons detected by the gamma ray sensor, the SWC value can be easily obtained.In this study we investigate the performance of PGRS applied to the case of study of a vineyard at the farm “Il Poggione” located in Montalcino (Siena, Italy).The effectiveness of the results obtained is supported by different tests: first the validation allowed to compare the PGRS measurement (5.8 ± 1.5)% with a gravimetric measurement (9.0 ± 2.5)%, highlighting a 1-σ agreement; then by the rainfall recognition capability indeed, in correspondence to the most significant rainfall event (18 mm) the SWC value before and after the rain increased of 7.8%.Moreover, the integration of the in-situ system with an agrometeorological station resulted in a Web App, allowing for real time data storage and thus facilitating data management, spectrum analysis, and display for both gamma ray sensor and agrometeorological station results, enabling comprehensive studies of environmental parameters (e.g., temperature, air humidity).This research underlines the potential of PGRS as a precise, real-time, and field scale SWC monitoring tool not only in vineyards but for cultivated fields in general. Further refinements concerning the gamma ray spectra analysis and broader applications in environmental monitoring are envisaged for improved agricultural practices.This study was supported by the project STELLA (Sistema inTEgrato per Lo studio del contenuto d'acqua in agricoLturA) (CUP: D94E20002180009) funded by the Tuscany region under the program POR FESR 2014/2020.

The demand for rapid, portable, and cost-effective analytical tools has driven advances in smartphone-based microfluidic sensors. By combining microfluidic precision with the accessibility and processing power of smartphones, these devices offer real-time and on-site diagnostic capabilities. This review explores recent developments in smartphone-integrated microfluidic sensors, focusing on their design, fabrication, smartphone integration, and analytical functions with the applications in forensic science, agriculture, and environmental monitoring. In forensic science, these sensors provide fast, field-based alternatives to traditional lab methods for detecting substances like DNA, drugs, and explosives, improving investigation efficiency. In agriculture, they support precision farming by enabling on-demand analysis of soil nutrients, water quality, and plant health, enhancing crop management. In environmental monitoring, these sensors allow the timely detection of pollutants in air, water, and soil, enabling quicker responses to hazards. Their portability and user-friendliness make them particularly valuable in resource-limited settings. Overall, this review highlights the transformative potential of smartphone-based microfluidic sensors in enabling accessible, real-time diagnostics across multiple disciplines.

In this research, a wireless remote environmental monitoring system is presented, which is designed for government to monitor the parameters of environment in factories. The system could collect data related to environment and transfer them by GPRS or Ethernet if condition is allowed. This paper elaborates the system by the order of “analyze-design-realize”. The embedded system is introduced. After some discussion, we chose ARMS based S3C2410 as microprocessor, Linux as the operation system. Then overall design solution and framework of the system is proposed. Finally, this paper gives the test experiment in the factories. The experiment results demonstrate the feasibility and stability of the system.

This study investigates the use of neuromorphic computing, particularly spiking neural networks (SNNs) and advanced neuromorphic hardware, to model and forecast climate patterns. Our neuromorphic system achieves high prediction accuracy, maintaining a Mean Squared Error (MSE) as low as 0.08, even with increasing data volumes. The system operates with notable energy efficiency, consuming just 0.15 J per inference at higher data loads. This efficiency, coupled with a throughput of 800 inferences per second, underscores the system’s capability to handle large-scale data effectively. The neuromorphic approach addresses key challenges in scalability and energy consumption, presenting a robust solution for real-time climate data analysis. By continuously adapting to new data inputs, the system ensures accurate and timely predictions, essential for applications in environmental monitoring and decision-making. The integration of artificial intelligence algorithms with neuromorphic architectures not only reduces computational costs but also enhances the interpretability of complex climate dynamics. These findings highlight the transformative potential of brain-like computing in environmental modeling, offering a scalable, efficient, and adaptable tool for climate prediction and analysis.

The real-time monitoring and analysis of multicomponent gas have high potential applications in environmental monitoring and industrial production control. This letter introduces a portable fiber-enhanced gas Raman sensing system equipped with a hollow-core anti-resonant fiber, which can significantly enhance the interaction strength between the pump light and the gas sample. The gas-filling device is designed to fill the hollow-core fiber with gas within ten seconds. By measuring the Raman spectra of common industrial gases, the analytical performance of the system for multicomponent gases is demonstrated. At the same time, the detection limit can be as low as sub 100 ppm level. The natural isotope 13CO2 in the main components of 12CO2 can be measured. The promised linear relationship between Raman signal intensity and gas concentration enables the gas sensing device to monitor multicomponent gas concentrations dynamically.

The quality of water, air, and soil has significantly deteriorated, and the physiological processes of organisms have been affected due to negligent human activities, such as the direct discharge of industrial waste into the environment. Cyclodextrin derivatives are emerging as sensor probes for detecting environmentally threatened pollutants. The disclosed CD-based adsorbents exhibit high adsorption efficiency for various pollutants. The hydrophobic cavity in cyclodextrins (CDs) is also crucial in capturing organic contaminants. The unique cavity structure of cyclodextrin has been utilised to detect pollutants through CD-based molecules, enabling self-assembly. Cyclodextrins enhance the detection sensitivity of sensors that rely on electrochemical detection, surface acoustic waves, Raman scattering, luminescence, or fluorescence. It also discusses the wide range of contaminants in various industries, including pharmaceutical, textile, and chemical industries. This review explores the recent advancements in cyclodextrin-based sensor design and their potential applications in environmental monitoring.

This article presents a review of many types of SERS sensors for food safety and environmental pollution monitoring based on detecting rhodamine. It introduces the basic concepts of substrates, enhancement factors, and mechanisms, devices’ sensors integrated with the microstructure. Here, we review the state-of-the-art research in the field of rhodamine monitoring and highlight the applications of SERS sensors. The trends in the development of substrates for different applications have been mentioned with the aim of providing an overview of the development of different SERS substrates. Thus, an efficient approach for rhodamine detection has a good perspective for application in environmental monitoring.

The existing barcode is only for identifying an item in our lives and environment monitoring applications. This paper proposes a new kind of barcode (D&S barcode), which can not only identify items, but can also sense particular environmental state change. The D&S barcode is supposition of several barcodes with the same length. The D&S barcode is printed by environmental sensible materials, and then it can show different information at different environment state, which makes the barcode has sensing ability. As an example, a temperature sensitive D&S barcode is presented, which is printed by themochromic inks and common inks cooperatively. The D&S barcode can be used in many applications, such as in IoTs and other environment monitoring, which can substitute some sensors in these applications.

Regarding the data collection process for a comprehensive automated environmental monitoring and precaution platform, this paper first analyses the key issues and challenges, including setting of monitoring targets, heterogeneity of monitoring equipments, diversity of communication methods, and online monitoring. Based on this, an embedded real-time environmental data collecting system is proposed. The contributions of the paper are as follows: we have designed an embedded environmental data collector which supports three collecting modes; we have proposed a novel integration technique for heterogeneous monitoring devices and an adaptive internetworking approach. The proposed system is a component of a distributed environmental monitoring platform which will be deployed in Guangzhou city.

The natural environment is essential for human survival and development since it provides water resources, land resources, biological resources and climate resources etc. As a developing country, China has witnessed a significant change in the natural environment in recent decades; and therefore, monitoring and mastering the status of the environment is of great significance. Due to the characteristics of large-scale and dynamic observation, remote sensing technology has been an indispensable approach for environmental monitoring. This paper reviews the satellite resources, institutions and policies for environmental monitoring in China, and the advances in research and application of remote sensing from five aspects: ecological index retrieval, environmental monitoring in protected areas, rural areas, urban areas and mining areas. The remote sensing models and methods for various types of environmental monitoring, and the specific applications in China are comprehensively summarized. This paper also points out major challenges existing at the current stage: satellite sensor problems, integrated use challenges of datasets, uncertainty in the retrieval process of ecological variables, scaling effect problems, a low degree of automation, the weak ability of forecasting and comprehensive analysis, and a lack of computational power for massive datasets. Finally, the development trend and future directions are put forward to direct the research and application of environmental monitoring and protection in the new era.