IoT Monitoring Research Articles

Toward a Blood Sensor for an IoT Monitoring: A New Approach for the Design and Implementation of Blood Light Absorption Systems Based on the Finite Element Method and the Diffusion Equation

Non-invasive blood analysis has the power to completely change how doctors identify and track illnesses. This study presents a novel approach for the non-invasive monitoring of red blood cell (RBC) mobility and concentration within capillaries, using photon absorption as a key diagnostic tool. The research combines optical modeling with the diffusion equation for light propagation, leveraging COMSOL simulations to create a comprehensive framework for understanding RBC dynamics. A two-dimensional geometric model of capillaries with RBCs is developed, where blood flow is modeled as a laminar, incompressible fluid. The Arbitrary Lagrangian–Eulerian (ALE) formulation is employed to account for the fluid–structure interactions, while photon attenuation by the RBCs is analyzed to investigate wavelength-dependent absorption characteristics. The methodology is implemented through a workflow developed with MATLAB’s S-Function builder, consisting of three main components: mesh generation, fluence computing, and Software-in-the-Loop (SIL) verification. The mesh generation process adapts to the target architecture using COMSOL Multiphysics for fluid–structure interaction (FSI) modeling. The fluence computing function solves the diffusion equation to model light intensity attenuation due to RBCs, and the SIL function compares computed results with real-time measurements, ensuring accuracy for potential real-time embedded system applications. The results demonstrate significant wavelength-dependent variations in photon absorption by RBCs, providing insights into the optical behavior of blood in microvascular structures. The findings have important implications for medical imaging, photodynamic therapy, and diagnostic tools, emphasizing the potential of integrating computational models with real-time systems for enhanced performance in biomedical applications.

Intelligent IoT and Deep Learning-Based Soil Monitoring System for Optimizing Tea Cultivation

Intelligent IoT and Deep Learning-Based Soil Monitoring System for Optimizing Tea Cultivation

Real-Time IOT Monitoring and Safety Control in EV Battery Management Systems

Further development of electric vehicle technology (EV) requires efficient and intelligent battery management systems. BMS) Ensure battery, service life and optimal performance safety. The project focuses on developing IoT-based real-time surveillance and security control systems for EV batteries and integrating Arduino and Node MCU microcontrollers with a variety of sensors to monitor voltage, electricity, temperature and fire. This system provides local monitoring via LCD screen and remote access via Ubidots cloud platform, allowing battery health to persecute battery health. To improve security, automated mechanisms are implemented to trigger alarms and isolate batteries with overload, overheating or fire detection. This IoT-enabled BMS has been developed to be inexpensive, scalable, energy efficient, minimize manual inspections, optimize battery life and improve EV security. By using wireless data transmission, real-time analysis and automated security controls, the system contributes to a more reliable and sustainable EV ecosystem, while simultaneously promoting smart energy management in the automotive industry. Key Words: Charge Monitoring, Fire Protection, Arduino, Voltage Sensor, Temperature Sensor

An IoT Monitoring System Based on Artificial Intelligence Image Recognition and EMG Signal Processing for Abdominal Exercise Performance

Correctly executing exercises during training is of vital importance to ensure adequate athletic performance. Sit-ups are among the most frequently performed exercises requiring proper evaluation. This exercise contributes to increasing abdomen strength, having better posture to reduce back problems, and improving overall physical condition and appearance, among other benefits. Existing methods for evaluating the correct execution of sit-ups are manual, subjective, and inefficient in terms of time, cost, and precision. Therefore, there is a need to have technological tools that measure and monitor core abdominal strength while simultaneously verifying, through image processing, the correct execution of the exercise. Since no solutions with these capabilities have been found in the literature, this work proposes a system that performs these functions using electromyographic (EMG) sensors, force signal processing, and biomechanical monitoring based on image processing and the BlazePose algorithm. The results obtained show a very satisfactory performance of the biomechanical monitoring method, where an accuracy of over 95% is obtained in the identification of the correct body posture, while for the estimation of abdominal strength, a sensitivity of over 90% is achieved during the execution of sit-ups.

Advancing methodologies for assessing the impact of land use changes on water quality: a comprehensive review and recommendations.

With increasing scholarly focus on the ramifications of land use changes on water quality, although substantial research has been undertaken, the findings demonstrate pronounced spatial variability and the heterogeneity of research methodologies. To address this critical gap, this review offers a rigorous evaluation of the strengths and limitations of current research methodologies, providing targeted recommendations for refinement. It systematically assesses the existing body of literature concerning the influence of land use changes on water quality, with particular emphasis on the spatial heterogeneity of research results and the uniformity of employed methodologies. Despite variations in geographical contexts and research subjects, the methodological paradigms remain largely consistent, typically encompassing the acquisition and analysis of water quality and land use data, the delineation of buffer zones, and the application of correlation and regression analyses. However, these approaches encounter limitations in addressing regional disparities, nonlinear interactions, and real-time monitoring complexities. The review advocates for methodological advancements, such as the integration of automated monitoring systems and IoT technologies, alongside the fusion of deep learning algorithms with remote sensing techniques, to enhance both the precision and efficiency of data collection. Furthermore, it recommends the standardization of buffer zone delineation, the reinforcement of foundational water quality assessments, and the utilization of catchment-scale analyses to more accurately capture the influence of land use changes on water quality. Future inquiries should prioritize the development of interdisciplinary ecological models to elucidate the interaction and feedback mechanisms between land use, water quality, and climate change.

A Digital Twin‐based Intelligent Concrete Placing Technology

ABSTRACTAn intelligent concrete placing control model was created by the authors to achieve this goal. Using the placing and control content as the physical object (PO), a BIM virtual model as the twin carrier (VE), two‐way twin data as the core (DD), coordinated control as the target (WM), and coordinated operational rules (RULE) as the driver. Additionally, they created a “IoT monitoring, BIM digitalization, dynamic analysis, and central control” integrated smart concrete transportation control platform for the construction of an ultra‐high arch dam, as well as a smart monitoring device covering the entire transportation, spreading, and vibration of the entire concrete. As a result, the entire placement procedure can be intelligently managed. These achievements have been used to create excellent, safe, and effective concrete works for the Wudongde and Baihetan dams.

Classification of random forest method on photovoltaic temperature and IoT monitoring for energy conversion

Classification of random forest method on photovoltaic temperature and IoT monitoring for energy conversion

Editorial

Welcome to the inaugural volume of Comm&Optics Connect, a multidisciplinary journal dedicated to advancing communication and networks across the entire electromagnetic spectrum. This first volume embodies our vision to provide a platform where groundbreaking research in radio, mmWave, THz, and optical technologies converges. The future of communication networks lies in the seamless integration of technologies across the electromagnetic spectrum to support ultra-high speed, ultra-low latency, and massive connectivity. However, realizing this vision requires overcoming significant challenges, such as managing the complexity of heterogeneous networks, ensuring robust security and privacy, and developing energy-efficient solutions. Comm&Optics Connect addresses these challenges by offering a platform for radio, mmWave, THz, and optical fields to share their work and collaboratively tackle these issues. The response to our call for papers reflects a strong interest in contributing to the aims and scope of the journal. The five articles featured in this volume represent the breadth and depth of research that Comm&Optics Connect aims to showcase. These articles cover critical areas such as: • Real-Time Cloud-based IoT for Energy Consumption Monitoring: Damien Campbell et al. present a cloud-based IoT platform for monitoring household electricity consumption, providing valuable insights into usage patterns and identifying opportunities for enhancing energy efficiency. This research exemplifies the journal’s focus on practical deployments. • Free Space Optical Inter-Satellite Links: Shivam Bhutiani and Nupur Shah explore the establishment of free space optical inter-satellite links beyond the Karman line. This work highlights the journal’s interest in cutting-edge communication technologies for space applications, aligning with the increasing demand for high-speed and reliable connectivity in satellite networks. • Enhancing Physical Layer Security: Shen Qian and Meng Cheng delve into physical layer security in lossy untrusted relay networks, focusing on the challenges and opportunities presented by finite blocklength transmissions. This research contributes to the journal’s commitment to exploring security and resilience in communication systems, an area of paramount importance as networks become more complex and vulnerable. • Open Fronthaul Technologies for 5G and Beyond: Hamna Shoukat et al. provide a comprehensive overview of technological trends in open fronthauls for beyond 5G and 6G networks. This work highlights the journal's emphasis on next-generation networks and the critical role of open and interoperable architectures in fostering innovation and flexibility. • Multi-Task Learning for Low-Complexity VLC Systems: Saksham Dewan and Hany Elgala explore the application of multi-task learning to reduce the complexity of visible light communication (VLC) systems. This research exemplifies the journal’s dedication to exploring novel data-driven approaches using deep learning (DL) models for enhanced efficiency and performance. As the Editor-in-Chief of Comm&Optics Connect, I am confident that the journal will become an essential resource for researchers in the field of communications and networks. None of this would be possible without the outstanding and diverse members of the editorial board, the hard-working staff at Scifiniti, and the valuable reviewers. I invite you to join us on this exciting journey as we bridge the gap between cutting-edge research and practical applications, shaping the future of a connected world. We look forward to receiving contributions from all over the world.

Beyond Compliance: Leveraging Regulatory Compliance to Drive Innovation in Food Supply Chain Traceability

Purpose: The Food Safety Modernization Act (FSMA) 204, effective January 2026, represents a transformative shift in global food supply chain management, emphasizing proactive over reactive traceability. This research investigates FSMA 204's regulatory framework, associated challenges, and its potential to drive innovation. Methodology: Through qualitative analysis and case studies of Trustwell and Campbell Soup Company, the study identifies the dual role of FSMA 204 as both a compliance mandate and a catalyst for operational advancements. Key challenges include high compliance costs, technological integration barriers, and limited timelines. However, the regulation incentivizes the adoption of advanced technologies such as blockchain for secure record-keeping, IoT for real-time monitoring, and AI/ML for predictive analytics, which collectively enhance transparency, efficiency, and competitive differentiation. These innovations turn regulatory obligations into opportunities for businesses to optimize supply chain processes, improve consumer trust, and achieve sustainability. Case studies demonstrate how strategic planning and the use of traceability technologies enable compliance while promoting ESG goals. Findings: The findings underscore the importance of proactive strategies and technological investments to navigate FSMA 204's requirements effectively. Unique Contribution to Theory, Policy and Practice: This research contributes to the discourse by framing FSMA 204 compliance as a strategic enabler for businesses to foster growth and sustainability, positioning it as a global benchmark for traceability standards. By leveraging these insights, food industry stakeholders can transform compliance challenges into pathways for innovation and resilience.

Developing Cost-Effective AI Algorithms for Resource-Constrained Devices

Resource-restrained gadgets pose sizable challenges for deploying synthetic intelligence (AI) packages, which include restricted computational power, reminiscence, and electricity resources. This research pursuits to broaden value-effective AI algorithms that cope with these limitations whilst retaining high overall performance and accuracy. The examine leverages superior optimization strategies, such as version pruning, quantization, and dynamic strength control, to layout light-weight models appropriate for low-strength environments. Experiments conducted on gadgets like the Raspberry Pi 4 and NVIDIA Jetson Nano screen giant improvements in inference time, electricity efficiency, and accuracy compared to conventional processes. The proposed algorithms acquire up to 50% reduction in energy consumption and 20% improvement in accuracy at the same time as lowering typical computational charges. These findings reveal the feasibility of deploying green AI solutions on constrained hardware without compromising on functionality or nice. The practical implications of this paintings make bigger to various applications, along with real-time healthcare monitoring, clever agriculture, and commercial IoT systems. The have a look at concludes by means of highlighting areas for destiny studies, which includes improving algorithmic adaptability and expanding trying out to embody diverse eventualities. This work gives a sturdy basis for advancing the deployment of AI in resource-restricted settings, bridging the gap between technological innovation and practical implementation.

Fully Printed Flexible Polystyrene/Graphite-Based Temperature Sensor with Excellent Properties for Potential Smart Applications.

This study presents an innovative temperature sensor based on a thermistor nanocomposite of graphite (Gt) and polystyrene (PS). The sensor exhibited notable thermal stability and film integrity, offering two distinct linear response regions within the tested temperature range of -10 to 60 °C. It demonstrated a sensitivity of 0.125% °C-1 between -10 and 10 °C, followed by another linear response with a sensitivity of 0.41% °C-1 from 20 to 60 °C. Furthermore, it exhibited a response/recovery time of 0.97/1.3 min at a heating/cooling rate of 60 °C min-1. The sensor maintained minimal baseline drift even when subjected to varying humidity levels. We assessed its mechanical flexibility and stability for hundreds of bending cycles at a bending angle of 30°, adapting to dynamic environmental conditions. The sensor's thermomechanical test (response to mechanical stress under temperature fluctuations) underscored its adaptability over a temperature range of -10 to 60 °C. Notably, it displayed excellent chemical stability, maintaining consistent performance when subjected to harsh environmental conditions like exposure to corrosive gases and prolonged immersion in tap water. Real-world tests demonstrated its practical utility, including precise temperature measurements in solid objects and breath temperature monitoring. These findings suggest promising applications in healthcare, environmental monitoring, and various IoT applications.

An autonomous IoT monitoring unit for radiological and nuclear emergency management

An autonomous IoT monitoring unit for radiological and nuclear emergency management

Development of low-cost IoT monitoring for system precision farming in a broiler house

Precision farming uses modern production technologies and techniques that impact farming processes and practices, contributing to the sustainability of the production process. Conventional farming production is highly inefficient in terms of resource management and has a high associated environmental impact, and production techniques such as precision farming are needed because of the increased demand for food. Nevertheless, the equipment and tools used are usually expensive, reducing the opportunities for small and medium producers. Therefore, a low-cost open-source agriculture IoT tool for monitoring animal production facilities was developed. The tool was integrated with three devices for measuring internal and external environmental conditions and for monitoring physiological and behavioural animal variables via a low-cost thermal camera. The external module sends the data wirelessly to the internal modules via the ESP-Now communication protocol. The measurements of all devices are stored in microSD and sent wirelessly via the MQTT communication protocol through the open-source message broker Eclipse Mosquitto. The developed sensor network was tested in the monitoring of broiler production. The environmental and physiological conditions of the birds were monitored in three production cycles, allowing the analysis of the relationships between the degree of thermal comfort and the weight gain of the animals, among other variables. The developed sensor network provides information of productive and scientific interest and can be used to support decision-making and technique processes in farming production in real time.

A review of graph-powered data quality applications for IoT monitoring sensor networks

A review of graph-powered data quality applications for IoT monitoring sensor networks

IoT monitoring to evaluate energy and water consumption in laboratory environment

Resumo Os consumos de energia e água operacionais de edificações são informativos e podem não refletir adequadamente os impactos ambientais. Para amenizar este problema, o monitoramento por Internet das Coisas (IoT) pode auxiliar na coleta de dados em tempo real e análise, de modo a oferecer subsídios para uma Avaliação do Ciclo de Vida (ACV) mais precisa. O principal objetivo deste artigo é compreender as necessidades, dificuldades e benefícios da implantação de monitoramento IoT no auxílio à avaliação do consumo de energia e água operacional de ambiente laboratorial. A condução deste artigo seguiu a metodologia de pesquisa experimental. A arquitetura IoT utilizada possui 4 camadas, sendo estas: de percepção, dados, serviços e aplicações. Como necessidade, a interdisciplinaridade da equipe é um aspecto importante a ser considerado. Como dificuldades observaram-se problemas de comunicação com os dispositivos e latências na rede da internet local. Esta pesquisa confirmou pesquisas anteriores que afirmaram que o monitoramento por IoT traz maior precisão à ACV por considerar diferentes horizontes de tempo, e assim incorporar à variação no comportamento dos ocupantes, fatores característicos dinâmicos e fatores de ponderação dinâmica.

Biogas production using cheese whey in a bioreactor controlled by an Arduino-based system

Implementation of a bioreactor with control and data capture to produce biogas from cheese whey in a dark fermentation process is presented. Sensors MQ8 for hydrogen and MQ4 for methane (to determine composition), ultrasound (for biogas volume), pressure, temperature and pH, were integrated into a single Arduino card, plus two peristaltic pumps for pH control and feeding. A single block programming allows data capture and pH control, while Excel interface allows a single screen visualization of response graphs in real time. For start-up and testing, granular activated sludge was used as inoculum, under anaerobic conditions, at pH 5.5 and 35 °C. 240 h batch operation gave a production of 73 L H2 Kg-1 CODlactose. In later stages, the H2 production kinetics will be carried out with the aim of optimizing, as well as the use of IoT for remote monitoring of the system.

IoT-Based Monitoring and Optimization of Nutrient Film Technique Aquaponic Systems: Influence of Bio-Mechanical Filtration on Growth Rates of Oreochromis Niloticus and Brassica Rapa

This study investigates the effects of bio-mechanical filtration on the growth of Oreochromis Niloticus and Brassica Rapa in a Nutrient Film Technique (NFT) aquaponic system, focusing on water quality and system monitoring. Aquaponics integrates fish farming and hydroponics into a symbiotic system that requires precise nutrient and waste management. Bio-mechanical filtration, essential for removing waste and maintaining water quality, was paired with an IoT-based monitoring system that tracked ammonia, nitrate, pH, and temperature in real-time. This enabled timely filtration adjustments to sustain optimal conditions for both fish and plants. Growth rates were analyzed across various filtration configurations, revealing that enhanced filtration and monitoring significantly reduced ammonia and nitrate levels, resulting in improved growth rates. These findings highlight the potential of combining bio-mechanical filtration and IoT monitoring to optimize aquaponic systems, making them more efficient, scalable, and resource-conscious. This study contributes to aquaponic design advancements, aligning with Sustainable Development Goal 2 (SDG 2) by promoting sustainable food production and food security.

Integrating IoT and AI for Real-Time Patient Monitoring

Integrating IoT and AI for Real-Time Patient Monitoring

IoT and AI for Real-time Water Monitoring and Leak Detection

Water is essential for ecological sustainability and human survival, necessitating effective management to meet rising global demands and address climate change. Traditional water supply monitoring methods are labor-intensive and slow, limiting real-time data acquisition and issue resolution. This paper presents QoW-Pro, an IoT-based water monitoring system that leverages AI algorithms to significantly enhance water quality assessments and leak detection. QoW-Pro enables real-time data collection, predictive modeling, and anomaly detection, leading to improved decision-making in water resource management. The system demonstrates quantitative improvements in leak detection accuracy and water quality prediction, offering a scalable solution adaptable to both urban and agricultural settings. By combining IoT and AI, this research contributes to the sustainable management of water resources, ensuring their availability and quality for future generations.

Bridge Safety for a Secure Society Using AI and IoT for Infrastructure Monitoring

The Bridge and Boat Monitoring System is intended to improve safety and operational efficiency of both bridges and waterways, through two integrated modules: the Bridge and the Boat. It is a Bridge Module made by Arduino, equipped with IR sensors for detecting any vehicle approaching, a load cell to monitor weight, a water level sensor and motorized gates to gain access under control. The Boat Module, which is also Arduino-based, contains a wet sensor and a tilt sensor (ADXL345) to detect water entry and ensure stability. For crack detection and structural health assessment of the bridge, the system utilizes YOLO v5, which is a state-of-the-art deep learning algorithm that can accurately and timely identify problems. Zigbee technology provides seamless wireless communication, allowing for real-time data sharing to support proactive maintenance and informed decision-making. Key Words: Crack detection, YOLOv5, Structural health, Zigbee, Arduino.