Autonomous Monitoring Research Articles

Development And Testing of Arduino Based Autonomous Environmental Monitoring System

This paper presents the development of an Autonomous Environmental Monitoring System (AEMS) designed to track various environmental parameters. With the growing demand for autonomous monitoring systems in today’s modern era, we propose an Arduino-based real-time hardware solution to measure parameters such as flame, temperature, humidity, and distance. The system leverages advanced and cost-effective electronic sensors and hardware to monitor these environmental factors. We developed individual sensor modules for each parameter and integrated them into a unified multi-sensor system through sensor fusion. The collected data is then displayed on a 16 × 2 LCD module. The result is a fully functional autonomous monitoring system, tested under laboratory environmental conditions using a compact, handheld device.

Autonomous Intelligent Monitoring of Photovoltaic Systems: An In‐Depth Multidisciplinary Review

ABSTRACTThis study presents a comprehensive multidisciplinary review of autonomous monitoring and analysis of large‐scale photovoltaic (PV) power plants using enabling technologies, namely artificial intelligence (AI), machine learning (ML), deep learning (DL), internet of things (IoT), unmanned aerial vehicle (UAV), and big data analytics (BDA), aiming to automate the entire condition monitoring procedures of PV systems. Autonomous monitoring and analysis is a novel concept for integrating various techniques, devices, systems, and platforms to further enhance the accuracy of PV monitoring, thereby improving the performance, reliability, and service life of PV systems. This review article covers current trends, recent research paths and developments, and future perspectives of autonomous monitoring and analysis for PV power plants. Additionally, this study identifies the main barriers and research routes for the autonomous and smart condition monitoring of PV systems, to address the current and future challenges of enabling the PV terawatt (TW) transition. The holistic review of the literature shows that the field of autonomous monitoring and analysis of PV plants is rapidly growing and is capable to significantly improve the efficiency and reliability of PV systems. It can also have significant benefits for PV plant operators and maintenance staff, such as reducing the downtime and the need for human operators in maintenance tasks, as well as increasing the generated energy.

Empowering Indigenous-Led Contaminant Monitoring through Collaborative Partnerships and Two-Way Capacity Sharing

Climate-driven landscape change, legacy waste and ongoing infrastructural investment are leading to concerns about contaminant release in Arctic Indigenous and Local communities. Sustainable development that considers threats posed by accelerating environmental change requires accessible, appropriate, and sustained environmental data to inform strategic decision-making. Collaborative partnerships and capacity sharing are necessary to promote resilient and sustainable environmental monitoring approaches, however effective collaboration has been hindered by mismatches in priorities and timelines between communities and research programs. We outline the development and later evolution of a community-based environmental research program focused on monitoring contamination threats posed by legacy infrastructure sourced from industry, transportation and domestic waste sites. Capacities and insights from diverse knowledge systems guided each stage of our research approach. Reflections provided by an Indigenous and non-Indigenous Early Career Researcher (ECR) share insights into different aspects of the research process. We highlight how cross-cultural partnerships and capacity sharing have enabled evolving and reflexive community-based contaminant monitoring. Our approach facilitates a structural shift from collaborative monitoring with external analysis to autonomous monitoring that supports equity in research outcomes. Appropriately considered and resourced co-development at regular points of the research process has been critical to developing a complete and effective Indigenous-led research project.

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Abstract The proceeding automation of industrial manufacturing processes has reduced human presence on modern shop floors. Since industrial processes are still afflicted with malfunctioning, autonomous monitoring solutions are demanded. This article presents a forming use case in which AI-based image recognition is used to monitor process behaviour and prompt stops when needed. The knowledge base utilized by the AI is obtained by examining descriptive examples of abnormal process behaviour.

Noise-robust automated sudden damage detection using blind source separation enhanced by variational mode decomposition and support vector machine based on shapelet transform

Compared to parametric counterparts, non-parametric (aka, model-free) damage detection methods have no requirements of accurate models, with the potential of autonomous monitoring of various complex structures. However, noises, or low signal-to-noise ratio (SNR), are one of the main challenges. This study is aimed at improving blind source separation (BSS)-based damage detection method, one of the most advanced non-parametric methods, in both aspects of noise robustness and autonomous operation. In particular, the measured acceleration responses are processed by variational mode decomposition (VMD) and wavelet transform (WT) in sequential, acting as the input for a BSS model. The BSS is then solved by independent component analysis (ICA), which approves to be more noise-robust compared to the state-of-the-art counterparts. Furthermore, shapelet transform is applied to extract the universal shape-based spike-like feature from the BSS model for training a support vector machine (SVM) model, applicable to different structures; it finally automates the sudden damage detection process and enables online monitoring. The effectiveness of the proposed method is illustrated by a numerical example and an experimental test, and demonstrated by a real-world seismic-excited structure. The results show that both single and multiple sudden damages can be automatically detected with high accuracy. Compared with the existing BSS methods, the proposed BSS method is more capable to detect small damages at relatively low SNR. In addition, the classification accuracy of SVM is also improved when shapelet-based feature is used for training, which reduces the malfunction of automated damage detection as shown by the numerical example. Therefore, the proposed strategy has the potential for rapid condition assessment of structures during rare/extreme events, before engineers are sent for further post-event inspection.

AUTONOMOUS MONITORING ROBOT SYSTEM THAT MEASURES AIR POLLUTION

Air pollution poses a significant threat to human health, ecosystems, and climate stability, necessitating effective monitoring and control measures. Traditional air quality monitoring stations, while accurate, are static, expensive, and limited in coverage. The Autonomous Monitoring Robot System (AMRS) provides a dynamic solution, offering real-time air quality monitoring through mobile robotic platforms. Equipped with advanced sensors, these robots can measure various pollutants, such as particulate matter (PM), nitrogen dioxide (NO2), carbon monoxide (CO), and volatile organic compounds (VOCs), across large and complex areas. By employing AI-based navigation and mapping technologies, AMRS can autonomously traverse urban and industrial environments, collecting high-resolution pollution data and creating real-time air quality maps. This approach allows for better spatial coverage, cost-efficiency, and access to hard-to-reach locations compared to traditional methods. The system can be deployed in diverse use cases, including urban air quality monitoring, industrial pollution control, and disaster management. While challenges such as battery life, sensor calibration, and data processing remain, AMRS represents a promising technological advancement in environmental monitoring and pollution control.

Smart and autonomous monitoring of cracking in concrete structures with PZT sensor array-based hybrid sensing

Detecting microcracks in concrete structures is crucial for maintaining structural integrity; however, current methods often lack the precision, objectivity, and sensitivity needed for early-stage detection and monitoring. This study introduces a pioneering hybrid sensing approach that combines coherent and incoherent ultrasonic wave fields with surface-mounted PZT-based sensors, significantly advancing the state-of-the-art. The sensors are positioned at specified locations on a notched concrete beam and operated in the actuator-receiver (AR) mode, allowing for the manipulation of fracture width at the notch to achieve distinct degrees of damage. By evaluating the ultrasonic waves received from various AR configurations, we developed robust damage indices: the attenuation factor and diffusivity ratio. These indices provide objective metrics for evaluating the degree of damage, addressing the limitations of existing techniques. Key results demonstrate that the attenuation factor can detect crack widths as small as 10 µm at a 120 kHz frequency, surpassing traditional methods. Nevertheless, its sensitivity diminishes if the crack width exceeds 100 µm. Additionally, the diffusivity ratio remains sensitive to cracks beyond 200 µm and shows a substantial 40 % decrease even for non-visible fractures, offering a comprehensive assessment of fracture propagation. These advancements not only enhance early detection and monitoring precision but also offer a deeper understanding of fracture mechanics, paving the way for improved structural health monitoring and maintenance strategies.

An Autonomous Monitoring System with Microwatt Technology for Exploring the Lives of Arctic Subnivean Animals

An Autonomous Monitoring System with Microwatt Technology for Exploring the Lives of Arctic Subnivean Animals

System for continuous metabolic monitoring of mechanically ventilated patients.

In clinical settings, due largely to the cost, size and calibration complexity of existing indirect calorimetry systems, there is seldom instrumentation available to provide reliable, continuous tracking of a mechanically ventilated patient's metabolic output in support of proper nutrition. The atypical metabolisms associated with critically ill patients are difficult to predict and both underfeeding and overfeeding lead to negative impacts on both mortality and the recovery and healing processes. With these issues in mind, a novel ventilator-agnostic indirect calorimetry sensor design, prototype development, and validation were undertaken with the goal of enabling 24/7 metabolic monitoring of mechanically ventilated patients by means of a passive, rate-proportional side-stream sampling scheme and miniature mixing chamber. The miniature mixing chamber enables the use of small, low-cost gas concentration and flow sensing components to ensure the affordability of commercial design-for-manufacture implementations of the prototype sensor. In addition to continuous measurement of patient metabolism, the prototype sensor also enables autonomous monitoring and detection of calibration drift in the gas measurement sensors without disrupting the patient ventilation.

Internet of things based smart photovoltaic panel monitoring system

<p>Solar energy has the potential to meet the entire world's energy requirements. Previous study revealed that photovoltaic (PV) panel monitoring methods are far more difficult and exclusive. This study examines and proposes an automated internet of things (IoT)-based PV panel monitoring system that allows autonomous monitoring of solar panel properties such as voltage, temperature, humidity, and sun irradiation from anywhere over the internet. Excessive human demand can be reduced by collecting data from solar panels. Many data sets can be collected frequently; for example, thousands of data points can be collected each minute. Continuous monitoring is achievable by gathering real-time data. Because there is no possibility of human error, highly accurate data can be generated.</p>

Piezoresistive response of MWCNTs/Epoxy mixtures with load-sensing capability

The impact of automobiles on pavement infrastructure is escalating with the expansion of economic activity and road networks. Overloading of freight trucks has emerged as a significant challenge in the realm of heavy vehicle control and management, primarily due to the adverse effects on road surfaces and traffic safety. Self-sensing pavement materials offer the prospect of autonomous monitoring of forces exerted on the pavement without relying on embedded sensors. These materials are engineered by integrating conductive fillers into a polymer matrix to enhance their ability to detect stress or strain while preserving their mechanical properties. Nano-conductive materials possess the potential to revolutionize traditional paving materials in terms of performance and applications. In this study, epoxy resin was selected as the base material due to its favorable physical and chemical characteristics. Multi-walled carbon nanotubes (MWCNTs) were chosen as the conductive filler to formulate a MWCNTs/Epoxy mixture. The dispersion impact of the high-speed shear method on MWCNTs was verified. Subsequently, a hybrid resistance measuring method was developed using conductive copper foil as an electrode. An exploratory investigation was conducted to evaluate the piezoresistive response of MWCNTs/Epoxy mixtures under varying temperatures and loading frequencies. The study revealed that the MWCNTs content near the percolation threshold yields an improved piezoresistive response. The gauge factor of MWCNTs/Epoxy mixtures is up to 150, which is much higher than that of conductive asphalt mixture. Furthermore, the mixture exhibited robust and consistent piezoresistivity across different temperatures (ranging from 0°C to 60°C) and loading frequencies (ranging from 1 Hz to 10 Hz).

Long-Term Statistical Process Monitoring of an Ultrafiltration Water Treatment Process.

As water treatment technology has improved, the amount of available process data has substantially increased, making real-time, data-driven fault detection a reality. One shortcoming of the fault detection literature is that methods are usually evaluated by comparing their performance on hand-picked, short-term case studies, which yields no insight into long-term performance. In this work, we first evaluate multiple statistical and machine learning approaches for detrending process data. Then, we evaluate the performance of a PCA-based fault detection approach, applied to the detrended data, to monitor influent water quality, filtrate quality, and membrane fouling of an ultrafiltration membrane system for indirect potable reuse. Based on two short case studies, the adaptive lasso detrending method is selected, and the performance of the multivariate approach is evaluated over more than a year. The method is tested for different sets of three critical tuning parameters, and we find that for long-term, autonomous monitoring to be successful, these parameters should be carefully evaluated. However, in comparison with industry standards of simpler, univariate monitoring or daily pressure decay tests, multivariate monitoring produces substantial benefits in long-term testing.

SMART technologies in older adult care: a scoping review and guide for caregivers

PurposeA wide gap exists between the innovation and development of self-monitoring, analysis and reporting technology (SMART) technologies and the actual adoption by older adults or those caring for them. This paper aims to increase awareness of available technologies and describes their suitability for older adults with different needs. SMART technologies are intelligent devices and systems that enable autonomous monitoring of their status, data analysis or direct feedback provision.Design/methodology/approachThis is a scoping review of SMART technologies used and marketed to older adults or for providing care.FindingsFive categories of SMART technologies were identified: (1) wearable technologies and smart tools of daily living; (2) noninvasive/unobtrusive technology (i.e. passive technologies monitoring the environment, health and behavior); (3) complex SMART systems; (4) interactive technologies; (5) assistive and rehabilitation devices. Technologies were then linked with needs related to everyday practical tasks (mainly applications supporting autonomous, independent living), social and emotional support, health monitoring/managing and compensatory assistance rehabilitation.Research limitations/implicationsWhen developing, testing or implementing technologies for older adults, researchers should clearly identify concrete needs these technologies help meet to underscore their usefulness.Practical implicationsOlder adults and caregivers should weigh the pros and cons of different technologies and consider the key needs of older adults before investing in any tech solution.Social implicationsSMART technologies meeting older adult needs help support both independent, autonomous life for as long as possible as well as aiding in the transition to assisted or institutionalized care.Originality/valueThis is the first review to explicitly link existing SMART technologies with the concrete needs of older adults, serving as a useful guide for both older adults and caregivers in terms of available technology solutions.

Edge HPC Architectures for AI-Based Video Surveillance Applications

The introduction of artificial intelligence (AI) in video surveillance systems has significantly transformed security practices, allowing for autonomous monitoring and real-time detection of threats. However, the effectiveness and efficiency of AI-powered surveillance rely heavily on the hardware infrastructure, specifically high-performance computing (HPC) architectures. This article examines the impact of different platforms for HPC edge servers, including x86 and ARM CPU-based systems and Graphics Processing Units (GPUs), on the speed and accuracy of video processing tasks. By using advanced deep learning frameworks, a video surveillance system based on YOLO object detection and DeepSort tracking algorithms is developed and evaluated. This study thoroughly assesses the strengths, limitations, and suitability of different hardware architectures for various AI-based surveillance scenarios.

Lightweight underwater image adaptive enhancement based on zero-reference parameter estimation network

Underwater images suffer from severe color attenuation and contrast reduction due to the poor and complex lighting conditions in the water. Most mainstream methods employing deep learning typically require extensive underwater paired training data, resulting in complex network structures, long training time, and high computational cost. To address this issue, a novel ZeroReference Parameter Estimation Network (Zero-UAE) model is proposed in this paper for the adaptive enhancement of underwater images. Based on the principle of light attenuation curves, an underwater adaptive curve model is designed to eliminate uneven underwater illumination and color bias. A lightweight parameter estimation network is designed to estimate dynamic parameters of underwater adaptive curve models. A tailored set of non-reference loss functions are developed for underwater scenarios to fine-tune underwater images, enhancing the network’s generalization capabilities. These functions implicitly control the learning preferences of the network and effectively solve the problems of color bias and uneven illumination in underwater images without additional datasets. The proposed method examined on three widely used real-world underwater image enhancement datasets. Experimental results demonstrate that our method performs adaptive enhancement on underwater images. Meanwhile, the proposed method yields competitive performance compared with state-of-the-art other methods. Moreover, the Zero-UAE model requires only 17K parameters, minimizing the hardware requirements for underwater detection tasks. What’more, the adaptive enhancement capability of the Zero-UAE model offers a new solution for processing images under extreme underwater conditions, thus contributing to the advancement of underwater autonomous monitoring and ocean exploration technologies.

Artificial Q-Grader: Machine Learning-Enabled Intelligent Olfactory and Gustatory Sensing System.

Portable and personalized artificial intelligence (AI)-driven sensors mimicking human olfactory and gustatory systems have immense potential for the large-scale deployment and autonomous monitoring systems of Internet of Things (IoT) devices. In this study, an artificial Q-grader comprising surface-engineered zinc oxide (ZnO) thin films is developed as the artificial nose, tongue, and AI-based statistical data analysis as the artificial brain for identifying both aroma and flavor chemicals in coffee beans. A poly(vinylidene fluoride-co-hexafluoropropylene)/ZnO thin film transistor (TFT)-based liquid sensor is the artificial tongue, and an Au, Ag, or Pd nanoparticles/ZnO nanohybrid gas sensor is the artificial nose. In order to classify the flavor of coffee beans (acetic acid (sourness), ethyl butyrate and 2-furanmethanol (sweetness), caffeine (bitterness)) and the origin of coffee beans (Papua New Guinea, Brazil, Ethiopia, and Colombia-decaffeine), rational combination of TFT transfer and dynamic response curves capture the liquids and gases-dependent electrical transport behavior and principal component analysis (PCA)-assisted machine learning (ML) is implemented. A PCA-assisted ML model distinguished the four target flavors with >92% prediction accuracy. ML-based regression model predicts the flavor chemical concentrations with >99% accuracy. Also, the classification model successfully distinguished four different types of coffee-bean with 100% accuracy.

A Raspberry Pi-Powered IoT Smart Farming System for Efficient Water Irrigation and Crop Monitoring

Water irrigation remain as a challenge to supply adequate amount of water to sustain the growth of plant and crops yield along the year in certain part of the world which are heavily affected by climate change. This scenario creates a huge risk toward the world food supply chain. Hence, the application of smart farming system is crucially important now to pave the way for a better the agriculture monitoring system to replace traditional manual monitoring labour by farmers. The smart farming system are usually equipped with environmental stimuli sensing system such as temperature, humidity, soil moisture, light intensity sensing sensors coupled with automation actuators to control the water irrigation rate for the crops in order to save water and at the same time provide adequate water supply for plant growth. The aim of using such smart farming system is to enable higher crops production and less human labour at the same time optimising resources available to minimize cost of farming. Hence, this paper aims to introduce a novel approach of a Raspberry Pi powered IoT smart farming system (ISFS) which can incorporate autonomous monitoring of plant irrigation, temperature, humidity, soil moisture and light intensity, to design a smartphone app that allows users to monitor plantation-related conditions in a user-friendly manner, and to enable automatic control of a drip irrigation system for plants based on data obtained on soil moisture, temperature and sunlight intensity. The proposed prototype with the functionality mentioned is aim to resolve the existing problem and to meet the demand of smart farming application in current era.

Programmable flow injection: a versatile technique for benchtop and autonomous analysis of phosphate and silicate in seawater

High-resolution, autonomous monitoring of phosphate and silicate in the marine environment is essential to understand their complex dynamics and implications for the functioning of marine ecosystems. In the absence of dependable reagent-less sensors for these nutrients, leveraging established colorimetric techniques using miniaturized analyzers, such as programmable Flow Injection (pFI), offers the best immediate solution to meet oceanographic accuracy and precision standards. In this work, we further optimize the phosphomolybdate and silicomolybdate assays recently adapted for use with pFI, laying the groundwork for the technique’s use for long-term, autonomous operations. For both assays, we show that only a narrow range of acidities and molybdate concentrations can maximize sensitivity while minimizing salt effects. In addition, we demonstrate the stability of our optimized colorimetric reagent formulations, ensuring that analytical sensitivity remains within 10% of initial levels for at least 35 days of continuous use. We then applied our optimized protocols to produce oceanographically consistent phosphate and silicate profiles at the Hawaii Ocean Time Series (HOTS) and Southern Ocean Time Series (SOTS), respectively, which compared favorably against a reference method and historical data. Using certified reference materials for nutrients in seawater, we show that our pFI protocols, optimized for long-term operations, achieve a shipboard precision better than 6% and a relative combined uncertainty (k=1) of 4.5% for phosphate (0.45 - 2.95 µmol L-1) and 6.2% for silicate (2.2 to 103 µmol L-1). To demonstrate pFI’s potential as a versatile tool for autonomous monitoring, we report a five-day hourly phosphate time series at a coastal shore station in central California (n=121 analyses), examine phosphate uptake by seaweed at five-minute intervals at a seaweed aquaculture facility (n=103), and discuss a unique, high-resolution surface silicate transect spanning multiple frontal zones in the Australian sector of the Southern Ocean (n=249). These data, obtained using a commercially available pFI analyzer, confirm that pFI is a viable technology for autonomous monitoring of phosphate and silicate, paving the way for more ambitious, long-term deployments in a variety of settings.

Passive sea ice thickness inference using cryophones.

Mechanical properties of Arctic sea ice can be inferred by observations of in-ice propagation of compressional, shear, and flexural waves. During the 1980s, impulsive signals were generated by a lead ball or sledgehammer dropped onto the sea ice, and the inference required observation of wave speeds. During ICEX20 and ARCEX23, passive cryophone observations were made of naturally occurring compressional wave resonances. Average first-year ice thicknesses during ICEX20 and ARCEX23 were inferred to be 1.3 and 1.6 m, respectively; these are consistent with independent observations and indicate the potential for remote, autonomous monitoring of sea ice thickness.

Polymer Hydrogel‐Based Multifunctional Theranostics for Managing Diabetic Wounds

AbstractChronic, non‐healing wounds pose significant challenges for public health, particularly in the context of diabetes, and carry significant economic consequences. This article introduces a new solution in the form of a wireless theranostic patch, developed to meet the critical need for real‐time monitoring and targeted treatments to facilitate optimal healing. The patch incorporates advanced materials that are both multifunctional and electro‐responsive, leveraging a sophisticated blend of smart hydrogels and wearable bioelectronics to support diabetic wound management with unparalleled efficacy. With electro‐responsive multifunctional polymer hydrogels at its core, the patch delivers a stretchable, antimicrobial, and moist environment for the wound, with added benefits such as conductivity and visibility. The materials also allow for continuous and autonomous monitoring of glucose and pH levels, providing precise and personalized treatments like insulin delivery via iontophoresis and electrical stimulation. Animal models have demonstrated that this integrated system is highly adaptable, effectively promoting wound closure and healing. Overall, the wireless theranostic system offers an exciting prospect for personalized healthcare solutions, adopting a patient‐centric approach that prioritizes real‐time, targeted care for chronic wounds. Its incorporation of advanced materials and electro‐controlled treatments paves the way for new and innovative approaches to wound management.