Innovative Monitoring Research Articles

Chaos-driven detection of methylene blue in wastewater using fractional calculus and laser systems

Abstract Methylene blue (MB) concentrations in residual water were detected using fractional calculus, the Rössler chaotic attractor and laser systems. A Nd:YVO4 nanosecond pulsed laser at 532 nm, with pulse energies ranging from 2 µJ to 7 µJ, was applied to irradiate different water samples containing MB concentrations from 20 µl to 100 µl. Fractional calculus was employed with the purpose of modeling the temperature distribution in the samples, with the Caputo fractional derivative describing photothermal effects induced by laser irradiation. Different MB concentrations were detected by using the Rössler chaotic attractor, it monitored variation on concentrations, associating attractor shapes with MB concentrations. Lower concentrations showed a weaker attractor response, whereas higher concentrations manifest stronger attractor shapes in magnitude. Raman spectroscopy confirmed the detection of MB in residual water from the Requena dam, located in Tepeji del Río de Ocampo, Hidalgo, Mexico. The application of fractional calculus improved the prediction of heat distribution in the samples, by incorporating numerical simulation. The results suggest that this approach is suitable for real-time monitoring, as it associates MB concentrations with distinct chaotic attractor shapes. This technique shows promise for the detection of other contaminants as well. Future research should focus on refining this method and expanding its application to develop innovative monitoring solutions.

Trimodal Watch-Type Wearable Health Monitoring Device

In the domain of healthcare, wearable health monitoring devices have emerged as essential tools for the advancement of patient health tracking. These devices facilitate the provision of real-time medical data to clinicians, enabling early diagnosis, timely intervention, and enhanced management of individual health. This study introduces an innovative trimodal wearable health monitoring device in the form of a wristwatch. The device integrates a breath analyzer for the assessment of gaseous phase biomarkers, a sweat analyzer for the evaluation of aqueous-phase biomarkers, and an infrared sensor for the measurement of body temperature in the optical phase. Engineered on a compact 3 cm × 3 cm printed circuit board, the device has been optimized for wearability, power efficiency, and seamless integration with both wired and wireless charging and communication systems. Furthermore, custom software applications, designed for both Windows and Android platforms, have been developed to facilitate intuitive data visualization and storage on personal computers and smartphones. Empirical results from real-time chemical testing substantiate the device’s efficacy and potential as an advanced solution for wearable health monitoring.

A mental health monitoring system based on intelligent algorithms and biosensors: Algorithm behaviour analysis and intervention strategies

Introduction: Mental health monitoring encompasses the systematic observation and assessment of an individual’s psychological well-being, aiming to detect, understand and manage mental health conditions. It involves various techniques and interventions tailored to support individuals in achieving and maintaining optimal mental wellness. Recent advancements include the use of biosensors and biomechanics to analyze physiological signals that correlate with mental states, providing a more comprehensive understanding of psychological well-being. Aim: The aim of this research is to construct an innovative mental health monitoring system established on intelligent algorithms and biomechanical data through behaviour analysis and intervention strategies. Methodology: We propose a novel Snow Ablation-driven Bi-directional Fine-tuned Recurrent Neural Network (SA-BFRNN) to identify the state of mental health. In addition to behavioural data, biosensors are employed to collect real-time physiological signals such as heart rate variability, skin conductance, and muscle tension, offering objective markers of mental stress and anxiety. These biomechanical inputs are integrated into the system for multi-modal analysis. We employ the SA algorithm, iteratively removing less influential connections and nodes based on their impact on model performance. This process enhances network efficiency and generalization capabilities, refining the BFRNN for mental health state identification. Utilizing a questionnaire with 25 questions, administered to a selected group of 756 individuals, we validate our proposed model. Biosensor data is synchronized with questionnaire responses to improve the precision of mental state identification. Clustering-derived labels are validated with mean opinion score. These labels inform classifiers for individual mental health prediction, aligning with our objective of robust mental health assessment through data-driven approaches. SA-BFRNN integrates both forward and backward temporal information, enhancing its ability to discern subtle patterns in behaviour. Through iterative fine-tuning, our network learns to adapt to diverse datasets, enabling precise identification of mental health states. Research findings: In the result evaluation phase, we thoroughly examine how well our proposed SA-BFRNN model recognizes various states of mental health across different parameters. Our findings also highlight the significance of incorporating biomechanics, where biosensor data showed a strong correlation with mental health indicators, thereby augmenting the accuracy of the system. Our findings emphasize the efficacy of the SA-BFRNN technique, as demonstrated by its overall performance in terms of recall (92.56%), accuracy (90.13%), F1-score (88.16%) and precision (89.23%). Our experimental results unequivocally demonstrate that our proposed model performed better than other traditional approaches in classifying contents from multimodal data, showing notable enhancements in accuracy and robustness, particularly under dynamic conditions.

Advancements in Flexible Electronics Fabrication: Film Formation, Patterning, and Interface Optimization for Cutting-Edge Healthcare Monitoring Devices.

Flexible electronics can seamlessly adhere to human skin or internal tissues, enabling the collection of physiological data and real-time vital sign monitoring in home settings, which give it the potential to revolutionize chronic disease management and mitigate mortality rates associated with sudden illnesses, thereby transforming current medical practices. However, the development of flexible electronic devices still faces several challenges, including issues pertaining to material selection, limited functionality, and performance instability. Among these challenges, the choice of appropriate materials, as well as their methods for film formation and patterning, lays the groundwork for versatile device development. Establishing stable interfaces, both internally within the device and in human-machine interactions, is essential for ensuring efficient, accurate, and long-term monitoring in health electronics. This review aims to provide an overview of critical fabrication steps and interface optimization strategies in the realm of flexible health electronics. Specifically, we discuss common thin film processing methods, patterning techniques for functional layers, interface challenges, and potential adjustment strategies. The objective is to synthesize recent advancements and serve as a reference for the development of innovative flexible health monitoring devices.

Development of an artificial intelligence-enabled non-invasive digital stethoscope for monitoring the heart condition of athletes in real-time

This study investigates the efficacy of AI-enabled digital stethoscopes in enhancing physical performance, increasing student engagement and motivation, and improving psychological well-being among physical culture students. The experimental design involved two groups of 40 students each: the experimental group used AI-enabled stethoscopes for real-time cardiovascular monitoring, while the control group relied on traditional heart rate monitoring methods. The results indicated significant improvements in physical performance, engagement, and psychological well-being for the experimental group. Real-time monitoring facilitated personalized training adjustments, optimizing training loads and preventing overexertion, leading to superior performance outcomes. Additionally, the use of innovative monitoring tools significantly increased student motivation and engagement in physical culture classes, as reflected in higher attendance rates and more enthusiastic participation. Psychological assessments revealed that continuous health monitoring reduced anxiety levels and enhanced overall mental well-being, providing students with a sense of security and proactive health management. These findings underscore the transformative potential of integrating advanced monitoring technologies into physical education and rehabilitation programs, offering precise, real-time data that supports individualized and responsive interventions. The study concludes with a call for further research to explore the long-term impacts and broader applications of AI-enabled health monitoring tools in diverse educational and clinical settings, aiming to maximize their benefits and improve overall student and patient outcomes. Keywords: Sports therapy, Physical culture education, Health monitoring technology, Personalized training, Student engagement, Real-time cardiovascular monitoring, AI-enabled stethoscope.

Artificial Intelligence-Assisted Environmental DNA Metabarcoding and High-Resolution Underwater Optical Imaging for Noninvasive and Innovative Marine Environmental Monitoring

To effectively protect the marine environment, it is crucial to establish effective environ mental monitoring platforms. Traditional marine environmental monitoring methods heavily rely on morphological identification and field expertise, with the sampling process being disruptive and potentially destructive to vulnerable marine environments. In light of emerging biomonitoring needs and biodiversity declines, we reviewed the urgently needed, ongoing advances in developing effective, noninvasive, and innovative monitoring methods and systems to examine the complex marine environment for better strategic conservation and protection, using the coral ecosystem as one of the representative forefront examples in marine protection. This review summarizes current trends and efforts in transitioning into more standardizable and automatable utilizations of environmental DNA metabarcoding-based monitoring strategies and high-resolution underwater optical imaging monitoring systems as two of the promising pillars for the next generation of noninvasive biomonitoring and associated applications. The assistance of artificial intelligence for environmental DNA metabarcoding and high-resolution underwater optical imaging into an empowered, all-rounded monitoring platform for enhanced monitoring capacity is discussed as a highly potent direction for future research exploration. This review will be a cornerstone reference for the future development of artificial intelligence-assisted, noninvasive, and innovative marine environmental monitoring systems.

Supporting the infrastructure operators with customer specific AE-monitoring solutions

Cost-effective and efficient monitoring of infrastructure is crucial for planning maintenance and repairs while ensuring the safety of the structure. Acoustic emission is a method particularly suitable for long-term monitoring. However, the necessary extended monitoring period incurs high costs for the operator, primarily due to the exceptionally high costs of measuring equipment. The need for a tailored monitoring system for infrastructure applications is evident. TÜV AUSTRIA is developing a monitoring system called RISE, which represents a customized solution for steel bridge monitoring. The innovative online monitoring solution RISE assists experts with structural monitoring. Due to its compact design, low power consumption, and easy installation, the measuring system is specifically designed for continuous monitoring. Specifically, RISE is introduced for the hotspot monitoring of railway steel bridges. The passage of trains triggers a material response that can be recorded by the measuring system. This is used to perform crack monitoring on bridges and to have a tool for assessing crack activity and the degree of damage. This enables targeted planning of repairs and maintenance activities. Reducing the operating costs of the structure and ensures it save operation. The system is being developed to function robustly and provide a high level of automation. A demonstration of AE-monitoring of active cracks on a main bridge girder was done during a fatigue test in cooperation with the TU Graz. The girder was removed from a deconstructed bridge.

Accelerable adaptive cepstrum and L2-Dual Net for acoustic emission-based quality monitoring in laser shock peening

Acoustic emission monitoring in laser shock peening facilitates real-time detection of potential quality issues arising from variations in industrial parameters, enabling iterative optimization of the manufacturing process through material behavior analysis. However, existing research still lacks a comprehensive understanding of the time-varying time-frequency characteristics in dynamic acoustic emission and efficient corresponding models. Therefore, this study proposes an innovative monitoring approach that integrates accelerable adaptive cepstrum (AAC) and L2-Dual Net. Specifically, AAC first employs variable frames and filters to map time-varying features in the signal, and then obtains representative frame length distributions and filter weights for different operating conditions based on statistical information. AAC not only unveils time-varying features in signals but also boasts an efficient computational process. L2-Dual Net is a novel quality assessment model with robust feature extraction and local spatial feature interactions. The incorporation of L2 norm equips the model with robust interference immunity, while the dual spatial attention mechanism helps the model to interact with spatial features exhibiting different time-frequencies. Variable process parameter experiments for aluminum alloy 7075 and titanium alloy TC4 were conducted to validate the reliability of the proposed method. Results demonstrate that AAC showcases optimal computational efficiency and higher feature resolution. When compared with state-of-the-art network architectures, L2-Dual Net exhibits superior information flow, along with higher recognition accuracy and robustness. Moreover, various variants of L2-Dual Net are explored and the code is accessible at https://github.com/Qinr1026/L2-Dual-Net. The proposed method holds promising potential for application in other areas of acoustic emission monitoring.

Understanding farmer options, context and preferences leads to the co-design of locally relevant agroecological practices for soil, water and integrated pest management: a case from Kiambu and Makueni agroecology living landscapes, Kenya

Agroecology, as a holistic approach to sustainable food systems, is gaining momentum globally as a key approach to addressing current challenges in agricultural and food production. In sub-Saharan Africa, despite numerous efforts to address declining soil productivity, water scarcity, and increasing pest pressure through agroecological soil, water, and integrated pest management (IPM) practices, the adoption of such practices remains low. As part of the CGIAR Agroecology Initiative, we conducted a collaborative rapid innovation assessment of existing soil, water, and pest management practices in two Agroecological Living Landscapes (ALLs) in Makueni and Kiambu counties, Kenya. The assessment also included an evaluation of the performance of these practices and identified farmer preferences. Using a multi-stage approach, we applied stratified random sampling to identify 80 farmers for farm assessments and in-depth interviews. A total of 31 practices were identified, of which 26 were further evaluated. The evaluation revealed a heterogeneous set of socio-economic and biophysical contextual factors influencing practice performance. Respondents identified 19 strengths, and 13 challenges associated with the practices, highlighting opportunities for innovation to improve or adapt performance. Farmers also expressed preferences for future adoption of 31 practices, 77% of which were listed in one of the three focus areas, namely soil management, water management, or IPM. The other 33% were associated with multiple functions and were listed under two or three of the focus areas. The results of the collaborative assessment informed a broader co-design cycle that included participatory prioritization and selection of innovative practices, experimental design, and monitoring protocols. This collaborative and systematic approach was taken because innovative practices often fail to be adopted due to a lack of co-design and inclusion of local perspectives in innovation design, and a disconnect between science and practice. Our study highlights the importance of integrating stakeholder input and transdisciplinary technical expertise in the co-design and implementation of agroecological innovations. It also emphasizes the importance of using a structured methodology to understand farmers’ options, context, and preferences while co-designing locally relevant agroecological practices, which promotes holistic and inclusive adoption, successful implementation and long-term sustainability of agroecological practices.

Does afforestation increase soil water buffering? A demonstrator study on soil moisture variability in the Alpine Geroldsbach catchment, Austria

This study employed an operational monitoring network to measure soil moisture and runoff behaviour continuously in the Alpine catchment Geroldsbach-Götzens, Austria. We hypothesize that afforestation can have a positive impact on soil water buffering. To analyse the impact of soil properties and vegetation cover changes on soil water dynamics, four experimental plots were established on grassland and monitoring stations were installed in the forest. The rainfall test site is equipped with an automatic weather station to obtain meteorological observations, and weirs to measure surface runoff of natural occurring precipitation events and artificial rainfall simulations. In the plots, 200 soil moisture sensors were installed at five different depths, aimed to track and visualize infiltration and subsurface flow processes. Another twenty sensors monitored soil moisture at different afforestation stages in the forested part of the catchment. The measurements show that soils covered with young and old-growth forest have a higher and more stable soil moisture content than grassland and soils with a lack of vegetation throughout the seasons. We observed large spatial differences at plot scale, where the spatial variability of soil moisture increases with depth and is highest during convective precipitation. The initial conditions and rainfall characteristics play an important role in infiltration processes and soil water storage. Our rainfall test site demonstrated the challenges of innovative monitoring techniques and that it offers opportunities for more experiments to gather evidence-based data as input for flood models. Overall findings confirm the sponge effect of forest soils and indicate that afforestation as Nature-Based Solution reduces the temporal soil moisture variability, buffering soil water during precipitation events, which can be beneficial for runoff reduction in Alpine catchments.

Enhanced Tailings Dam Beach Line Indicator Observation and Stability Numerical Analysis: An Approach Integrating UAV Photogrammetry and CNNs

Tailings ponds are recognized as significant sources of potential man-made debris flow and major environmental disasters. Recent frequent tailings dam failures and growing trends in fine tailings outputs underscore the critical need for innovative monitoring and safety management techniques. Here, we propose an approach that integrates UAV photogrammetry with convolutional neural networks (CNNs) to extract beach line indicators (BLIs) and conduct enhanced dam safety evaluations. The significance of real 3D geometry construction in numerical analysis is investigated. The results demonstrate that the optimized You Only Look At CoefficienTs (YOLACT) model outperforms in recognizing the beach boundary line, achieving a mean Intersection over Union (mIoU) of 72.63% and a mean Pixel Accuracy (mPA) of 76.2%. This approach shows promise for future integration with autonomously charging UAVs, enabling comprehensive coverage and automated monitoring of BLIs. Additionally, the anti-slide and seepage stability evaluations are impacted by the geometry shape and water condition configuration. The proposed approach provides more conservative seepage calculations, suggesting that simplified 2D modeling may underestimate tailings dam stability, potentially affecting dam designs and regulatory decisions. Multiple numerical methods are suggested for cross-validation. This approach is crucial for balancing safety regulations with economic feasibility, helping to prevent excessive and unsustainable burdens on enterprises and advancing towards the goal of zero harm to people and the environment in tailings management.

Utilization of Quality Organic Fertilizer at P4S Sari Sedana, Badung, Bali

This Community Partnership Program (CSP) conducted in Desa Selat, Kecamatan Abiansemal, Kabupaten Badung, has achieved significant advancements in organic agricultural development. The program focused on the implementation of both hard and soft technologies. Hard technologies included the development of high-quality organic fertilizers using local waste materials such as livestock manure, biochar, Trichoderma, NPK fertilizers, and dolomite, processed with simple equipment. These innovations addressed local waste management issues and enhanced agricultural productivity. Soft technologies involved comprehensive training on production management, business operations, and marketing strategies, which empowered the P4S Sari Sedana Group with essential skills for sustainable operation. The program demonstrated high relevance to local needs, effectively transforming waste into valuable resources and introducing environmentally friendly production methods. It also fostered active community participation and generated substantial environmental and economic benefits, including increased soil fertility and reduced reliance on chemical fertilizers. The project produced several key outputs: high-quality organic fertilizer, enhanced community skills, scientific publications, intellectual property registration, and online media coverage. Future sustainability will be supported through institutional capacity building, market expansion, product innovation, and ongoing monitoring and evaluation.

Stability Preparedness: The Not-So-Cold Case for Innovations in Vaccine Stability Modelling and Product Release.

The rapid development of equitably accessible vaccines is paramount in addressing emerging global health challenges. The safety and efficacy of vaccines hinge significantly on their ability to remain stable from manufacturing throughout the supply chain and up to administration. Furthermore, the release of vaccines requires sufficient understanding of the stability profile to allow for expiration dating. In the event of a public health crisis, the time to generate the necessary stability data and the need for rapid product release are in direct opposition. Developing manufacturing platforms with thermostable product formulations for rapid response is therefore key to meeting CEPI's 100 Days Mission goal. This Review aims to highlight the need for stability preparedness through developing thermostable vaccine platforms and exploring innovative stability monitoring strategies that leverage advanced technologies, predictive modelling, and adaptive methodologies. By doing so, we seek to enhance the efficiency and effectiveness of stability assessments, supporting rapid development, regulatory approval, and widespread, equal distribution of vaccines-especially in an outbreak scenario. Finally, enhanced thermostability will allow for simplification across the supply chain, which will reduce the financial burden of vaccination programmes and enhance equitable access.

Supervised classification and fault detection in grid-connected PV systems using 1D-CNN: Simulation and real-time validation

Photovoltaic (PV) systems are prone to various faults, including short-circuit, open-circuit, partial shading, and inverter bypass diode issues, which reduce power output and can damage components. This study presents an innovative fault detection and online monitoring technique for grid-connected PV (GCPV) systems, combining Internet of Things (IoT) technology with a one-dimensional convolutional neural network (1D-CNN) deep learning approach. The method involves developing a temperature-dependent PV system model using series resistance and ideality factor, capturing real-time data from a 15kWp GCPV system with optimally placed sensors to minimize sensor count while maintaining data accuracy, and validating the model through MATLAB/Simulink simulations and real-time experiments under various fault scenarios. The collected data is used to train the 1D-CNN model to classify different fault types. The model is then implemented on an IoT platform for real-time monitoring and fault detection, displaying system status and alerts via a dashboard. The proposed system achieves a high fault detection accuracy of 98.15 % and 93.12 % during cyberattacks, with an uncertainty of ±4 %, significantly enhancing fault detection reliability and efficiency compared to existing methods. The IoT dashboard provides an effective tool for monitoring system performance and issuing alerts under abnormal conditions.

An Innovative Geospatial Monitoring and Health Impact Assessment Tool for Sustainable and Healthy City Planning

An Innovative Geospatial Monitoring and Health Impact Assessment Tool for Sustainable and Healthy City Planning

Classification of Temperature and Humidity in Green Open Spaces by Implementing Internet of Things (IoT) using Mamdani Fuzzy Logic

The condition of green open spaces plays a pivotal role in influencing the comfort and well-being of users engaged in various activities within these environments. Hydrological and microclimate factors, particularly temperature and humidity, hold significant sway over the ecological balance in these areas. This study addresses the need for a tool to assess air temperature and humidity, facilitating public access to critical environmental data that impacts the psychological well-being of living organisms. In pursuit of this objective, a comprehensive monitoring system harnessing the power of the IoT was developed. This system integrates DHT22 and MQ-9 sensors to monitor temperature, humidity, and CO2 gas levels within multiple green open spaces. Data from these sensors is transmitted to a central database with minimal time delays: 2 seconds from the sensors to the server, 1 second from the server to the application, and 3 seconds from the hardware to the application. Impressively, this data transmission achieved a 100% success rate. To assess the comfort levels of these green open spaces, the system employs fuzzy Mamdani logic. It processes data obtained from the DHT22 sensor, using predefined temperature and humidity value thresholds. This innovative monitoring system provides valuable insights into the environmental conditions of green open spaces, contributing to the enhancement of the overall quality and suitability of these areas for various activities.

A novel detector for 4D tracking in particle therapy

An innovative beam monitor for particle therapy applications was developed to count protons and carbon ions in clinical beams and was integrated with a Time-to-Digital Converter (TDC) to add the measurement of particles’ crossing time. The detector exploits strip-segmented planar silicon sensors with an active thickness of a few tens of μm and the front-end electronics is based on a 24-channel ASIC (named ABACUS) for the discrimination of the particles’ signals. The proton counting efficiency shows a dependence on the beam energy because of transversal dimension and pile-up effects, whereas an efficiency between 94 and 98 % with lower energy dependence is found for carbon ions. The time measurements with the TDC allow for the study of the time interval between consecutive particles in one strip, which appears to be compatible with the radio-frequency period of the synchrotron. These results indicate that thin silicon sensors and custom front-end readout with high counting rate capability allow for a full 4D tracking of clinical ion beams, opening the way to future technologies for online monitoring systems.

Innovative educational and research center for monitoring forest resources of Siberia based on laser and microwave aerospace imaging

The possibilities of creating an innovative educational and scientific center for monitoring forest resources in Siberia on the basis of the Department of Space Facilities and Technologies of the Siberian State University of Science and Technology named after Mikhail Fedorovich Reshetnev are discussed, with the aim of training highly qualified engineering personnel and conducting promising scientific research in the field of monitoring, modeling, forecasting and management of forest resources. Methodological solutions and algorithms for three-dimensional modeling of forest structure and dynamics based on laser scanning data, digital aerial and space photography are proposed. These methods contribute to operational monitoring and can significantly reduce the cost of monitoring the condition and use of forest resources over the vast territory of Siberia. Remote sensing data is presented in the form of a geotransformed database and digital photo map, compatible in formats with computer-aided design systems and with the main geographic information systems – ArcView, ArcINFO, MapINFO. The innovative monitoring center will be used for operational state control and monitoring of forest management, the state of forest lands, forest management and forest inventory, solving problems of ecology and environmental management, geoecology, formation of a forest resource inventory, aerospace methods for studying natural resources and territories, information technology. Solving these problems will allow for the training of highly qualified specialists. The center's specialists plan to create information technologies for remote sensing of natural objects with the aim of import substitution of foreign software products. The main scientific directions of the created center: development and research of methods for system analysis of large-scale multidimensional remote sensing data based on nonparametric decision-making algorithms and parallel computing technologies; testing hypotheses about the distributions of large-volume remote sensing data based on nonparametric nuclear-type pattern recognition algorithms; detection of compact groups of large-volume remote sensing data corresponding to unimodal fragments of the joint probability density of multivariate random variables.

Exploring Biodiversity through the Lens of Knautia arvensis Pollinators: Knautia Pollinator Walks as a Monitoring Method.

Declining populations of native pollinators, especially wild bees, underline the urgent need for effective monitoring within agricultural ecosystems. This study aims to (i) establish the 'Knautia Pollinator Walk' as an innovative pollinator monitoring method, (ii) examine the link between pollinator richness/density and land cover, and (iii) assess if specialist solitary bees indicate pollinator abundance and morphogroup richness. The approach involves surveying 500 Knautia arvensis inflorescences per site thrice per season. Observations of 11,567 pollinators across 203 taxa showed significant correlations between pollinator diversity and land use. Pollinator populations fluctuated with land cover type, increasing in open areas but decreasing or stabilising in forested and shrubby regions. Noteworthy differences in pollinator types were seen between Russia (solitary bees, small Diptera, Lepidoptera) and Sweden (bumblebees, beetles, furry Diptera). The "Knautia Pollinator Walk" shows promising signs of being an effective tool for monitoring spatiotemporal biodiversity trends. The method offers a scalable approach to pollinator monitoring, which is essential for developing conservation strategies and supporting pollinator populations.

IOT Based Air Conditioner Monitoring System

In the context of rising urbanization, the surge in air conditioner usage has become a significant contributor to energy consumption. To address this, our research introduces an innovative Air Conditioner Monitoring System (ACMS) designed to optimize energy usage, enhance system efficiency, and ensure optimal indoor conditions. The proposed system employs cutting-edge sensors to monitor crucial parameters, including current, voltage, power consumption, and room temperature in real-time. By integrating these metrics into a unified framework, the ACMS facilitates proactive management and enables users to make informed decisions for energy conservation. The collected data is processed through advanced algorithms to predict potential faults or inefficiencies, allowing for preemptive maintenance and reducing downtime. Furthermore, the system supports remote monitoring and control, providing users with the flexibility to manage air conditioning settings from any location. Practical implementation and performance evaluations demonstrate the system's effectiveness in improving energy efficiency, reducing operational costs, and enhancing overall sustainability. This research contributes to the development of intelligent solutions for air conditioning systems, addressing the growing need for energy conservation in the face of expanding urbanization. The modularity of the proposed system allows for seamless customization and integration of additional features, paving the way for future advancements in intelligent climate control systems.