Real-time Environmental Monitoring Research Articles

Experimental analysis of low-duty cycle campus deployed IoT network using LoRa technology

This study emphases on the implementation and evaluation of a LoRa network in indoor environments through an experimental setup at Amity University, Greater Noida (AUGN) campus. Aimed at scrutinizing the link-level performance and reliability of LoRa technology, extensive measurements have been conducted with prototype device integrating the SX1278 LoRa radio module at 433 MHz. The performance has been analysed in terms of Signal-to-Noise Ratio (SNR), Received Signal Strength Indicator (RSSI), and Packet Reception Rate (PRR) across various line-of-sight (LoS) and non-line-of-sight (NLoS) scenarios. The results reveal that network performance is highly sensitive to parameter configurations, such as spreading factor (SF) and coding rate (CR). In LoS environments, SF = 9 and CR = 4/5 provided an RSSI of −89.12 dBm and an SNR of 9.91 dB, while highly obstructive areas saw optimal performance with SF = 7 and CR = 4/7. The study also identified configurations ensuring 100 % packet delivery accuracy and maximum throughput. This work stands out due to its thorough examination of LoRa for indoor IoT applications, notably addressing duty cycle restrictions, a topic often overlooked in existing literature. The valuable insights presented are crucial for maximizing the potential of LoRa in indoor environments with high population density. The findings strongly advocate for the use of LoRa for real-time environmental monitoring and innovative IoT applications within university campuses.

Study on the control effectiveness of relative humidity by various ventilation systems for the conservation of cultural relics

The Dadiwan F901 site, boasting a history of over 5000 years, stands as the largest and most intricately crafted large-scale housing structure from China’s prehistoric era. The early renovation efforts, incorporating a sealed glass curtain wall, led to a continuous rise in relative humidity within the site, triggering outbreaks of microbial diseases. Subsequent measures successfully restored stability to the thermal and humid environment. This paper employs on-site real-time environmental monitoring and numerical simulation methods to assess the ventilation effectiveness and relative humidity changes before and after multiple interior modifications of the Dadiwan F901 site museum. The results indicate that the fully enclosed glass curtain wall can suppress the dependence of indoor humidity fluctuations on external weather fluctuations but has generated unintended consequences, leading to increased air relative humidity and even reaching saturation in the museum space. The strategic deployment of louvered windows and duct fans proved effective in enhancing internal airflow dynamics and overall air exchange capacity. It was possible to ensure that the relative humidity inside the site remained at approximately 70%, meeting the essential requirements for the preservation of cultural relics. This study is of great significance for alleviating the deterioration problem of enclosed exhibition halls of earthen relics.Graphical

Development of a 3D Printed Liquid-Core Hydrogel Platform for Real-Time Carbon Nanotube Sensors: A Breakthrough in Minimally Invasive Health Monitoring

The need for minimally invasive, real-time, and low-cost medical sensors holds great potential to revolutionize the healthcare industry. Single-walled carbon nanotube (SWNT)-based near-infrared (nIR) sensors are a relatively new field, able to provide high-quality spatial and temporal information regarding a wide range of cellular signaling molecules, including nitric oxide (NO), and hydrogen peroxide (H2O2). However, their use for in vitro and in vivo applications has been limited due to challenges related to their compatibility with packaging or immobilization techniques. Approaches like surface immobilization on solid substrates and dialysis bag confinement are costly, unstable, and unsuitable for use in settings with limited resources. SWNT sensors have recently been implanted into a large animal model as a proof-of-concept, but the challenge of retaining SWNT is currently restricting the advancement of hydrogel technology in wearable/implantable biosensors. We have developed and optimized a 3D printed liquid-core self-healing hydrogel platform that encapsulates the (AT)15 wrapped-SWNT, a NO sensor, and studied its mechanical and optical characterizations. Notably, after 90-days at 37 °C, the fluorescence intensity exhibited a negligible decrease. Statistical analysis indicates that the change in intensity from day 1 to day 90 was statistically insignificant, highlighting the sensor's robust and stable performance over the extended testing period. A linear calibration curve was obtained when the percent quenching was plotted against the logarithm of NO concentration. Furthermore, a polyvinyl alcohol (PVA) wrapped SWNT, a non-reactive sensor, was implanted into 3D printed liquid-core hydrogel platform and no quenching was observed when it was exposed to NO. Therefore, 3D printed liquid-core hydrogel platforms offer a solution for on-site detection of small-molecule analytes, overcoming a major challenge in real-time medical diagnostics and environmental monitoring.

Advancing micro-electrometric techniques for the detection of organophosphate and carbamate residues using cricket cholinesterase.

The widespread use of organophosphate (OP) and carbamate (CM) pesticides requires efficient and cost-effective detection methods. This study introduces a micro-electrometric method using cricket cholinesterase (ChE) to detect OP and CM residues, providing a rapid and economical alternative to conventional chromatographic techniques. The parameters of the method, including the substrate concentration, incubation temperature, and incubation time, were optimized. By leveraging the sensitivity of cricket ChE to OP and CM inhibition, this approach translates enzyme inhibition into an electrical signal to quantify pesticide levels, achieving an impressive limit of detection (LOD) from 0.036 to 0.086 parts per million (ppm). This method demonstrated reproducibility and stability, making it suitable for field applications and on-site testing across various environmental matrices. This research represents a significant advancement in pesticide residue analysis with potential applications in the development of portable biosensor devices for real-time environmental monitoring and public health protection.

Evaluation of low-cost sensors to integrate in a water quality monitor for real-time measurements.

Low-cost sensors integrated with the Internet of Things can enable real-time environmental monitoring networks and provide valuable water quality information to the public. However, the accuracy and precision of the values measured by the sensors are critical for widespread adoption. In this study, 19 different low-cost sensors, commonly found in the literature, from four different manufacturers are tested for measuring five water quality parameters: pH, dissolved oxygen, oxidation-reduction potential, turbidity, and temperature. The low-cost sensors are evaluated for each parameter by calculating the error and precision compared to a typical multiparameter probe assumed as a reference. The comparison was performed in a controlled environment with simultaneous measurements of real water samples. The relative error ranged from 0.33 to 33.77%, and most of them were 5%. The pH and temperature were the ones with the most accurate results. In conclusion, low-cost sensors are a complementary alternative to quickly detect changes in water quality parameters. Further studies are necessary to establish a guideline for the operation and maintenance of low-cost sensors.

PH-Responsive, Wide Color Gamut Dynamic Color Display Enabled by PDMAEMA Brush-Based Fabry-Perot Resonant Cavity.

Dynamic color-changing materials have attracted broad interest due to their widespread applications in visual sensing, dynamic color display, anticounterfeiting, and image encryption/decryption. In this work, we demonstrate a novel pH-responsive dynamic color-changing material based on a metal-insulator-metal (MIM) Fabry-Perot (FP) cavity with a pH-responsive poly(N,N-dimethylaminoethyl methacrylate) (PDMAEMA) brush layer as the responsive insulating layer. The pH-responsive PDMAEMA brush undergoes protonation at a low pH value (pH < 6), which induces different swelling degrees in response to pH and thus refractive index and thickness change of the insulator layer of the MIM FP cavity. This leads to significant optical property changes in transmission and a distinguishable color change spanning the whole visible region by adjusting the pH value of the external environment. Due to the reversible conformational change of the PDMAEMA and the formation of covalent bonds between the PDMAEMA molecular chain and the Ag substrate, the MIM FP cavity exhibits stable performance and good reproducibility. This pH-responsive MIM FP cavity establishes a new way to modulate transmission color in the full visible region and exhibits a broad prospect of applications in dynamic color display, real-time environment monitoring, and information encryption and decryption.

A Portable Agriculture Environmental Sensor with a Photovoltaic Power Supply and Dynamic Active Sleep Scheme

A portable environmental sensor for agricultural applications is proposed that addresses key challenges in power supply, data transmission, and monitoring efficiency. The sensor features a photovoltaic power supply and a PID-based dynamic active–sleep scheme for sustainable energy management, maintaining optimal battery levels under varying solar conditions. Its compact, waterproof, and dustproof design (90 mm × 90 mm × 150 mm, 844 g) ensures robust and reliable operation in diverse agricultural environments. High-precision digital sensors monitor temperature, humidity, light intensity, and CO2 concentration. Equipped with low-power NB-IoT technology, the sensor supports real-time remote environmental monitoring. Our experimental results show effective continuous operation, accurate environmental measurements, and performance comparable to established data loggers. The advanced power management and precise sensing capabilities make this sensor a competitive solution for improving smart agriculture practices, particularly in resource-limited or off-grid settings.

IMD-MP: Imputation of Missing Data in IoT Based on Matrix Profile and Spatio-temporal Correlations

Data in the Internet of Things (IoT) domain may be missing due to connectivity errors, environmental extremes, sensor malfunctions, and human errors. Despite the many approaches for imputing missing values, the most significant difficulty in terms of imputation precision or compute complexity for larger missing sub-sequences in uni-variate series is still being explored. This work introduced IMD-MP (Imputation of Missing Data using Matrix Profile), a new technique that improves imputation accuracy for big data analysis in IoT applications based on spatial-temporal correlations using a novel distance metric Matrix Profile Distance (MPD). Our method preserves spatial correlation by grouping the sensors present in the network (using grouping algorithm-GA) to impute the missing data of the failed sensor node. After grouping, similar sensor nodes to the failed sensor node are identified using the Node Similarity Algorithm (NSF). From its similar sensor data, a certain number of sub-sequences that are most similar to the one preceding the failed node&amp;rsquo;s missing values are gathered. These sub-sequences heights are optimized to ensure temporal correlation in the imputed data. To find the optimal imputation sequence, the current research uses MPD and similarity scores. Numerical findings using sensor data from real-time environmental mon-itoring and Intel data sets demonstrate the algorithm&amp;rsquo;s effectiveness compared to other benchmarks.

Digital and Sustainable Transition in Textile Industry through Internet of Things Technologies: A Pakistani Case Study

The textile industry, a vital contributor to Pakistan’s economy, faces pressing challenges in transitioning towards sustainability amid global environmental concerns. This manuscript presents a comprehensive case study on the implementation of IoT-driven strategies in the Pakistani textile sector to achieve digital and sustainable transformation. The findings reveal that the implementation of IoT technologies facilitated real-time environmental monitoring, enabling compliance with regulatory standards, and fostering sustainable manufacturing practices. Ultimately, this manuscript offers valuable insights into the transformative potential of IoT technologies in driving sustainable practices in the textile industry. The case study serves as a benchmark for other textile-producing regions aiming to embark on a digital and sustainable journey. These findings hold significant implications for the ongoing dialogue on sustainable industrial development, providing valuable direction for policymakers and stakeholders in shaping a more resilient and ecologically conscious future. Future research should prioritize addressing issues like data confidentiality and interoperability while adhering to standard requirements. Additionally, exploring analytics and machine learning methods for predictive maintenance, optimized performance, and operational improvement is crucial.

Rapid and reliable electrochemical detection of bisphenol S in thermal paper

Bisphenol S (BPS) is a common, persistent, and mobile chemical found in everyday products such as thermal paper. BPS can easily enter the body by migrating from the paper to the fingers, disrupting the endocrine system by mimicking the oestrogen hormone, thus negatively influencing human health. Assessing BPS levels in daily life is of great importance. This study introduces a rapid and reliable approach for detecting BPS in thermal paper and tap water by developing an electrochemical analytical method. This method allows for in-situ, real-time measurements. We present a simple, low-cost electrochemical sensor for detecting BPS using screen-printed electrodes based on carbon (SPEC) and single-wall carbon-nanotube (SPE-SWCNT) working electrodes. BPS was detected over a wide linear range from 1 to 400 μM. The detection limits were 0.73 μM and 0.87 μM for the SPE-C and SPE-SWCNT electrodes, respectively. Good repeatability was observed for both sensors when using one electrode 16 times, which demonstrates its potential for real-time environmental monitoring. Additionally, traditional chromatographic methods, high-performance liquid chromatography with a diode-array detector (HPLC-DAD), and liquid chromatography-mass spectrometry triple quadrupole (LCMS), were incorporated to enhance analytical capabilities. HPLC-DAD achieved a detection limit of 3 nM after solid-phase extraction preconcentration, while LCMS triple quadrupole demonstrated a detection limit of 10 pM without preconcentration. Electrochemical screen-printed electrodes can be employed for on-site analysis and health-risk assessments in everyday settings, such as shops. However, for detecting very low concentrations where time is not a constraint, LCMS quadrupole remains the preferred technique.

NIKKY THE HUMANOID ROBOT FOR HEALTHY AND SAFETY LIVING

As the global population ages, the demand for effective elderly care solutions has become increasingly urgent. With advancements in technology, there arises an opportunity to revolutionize the way we approach elderly care, moving beyond traditional models towards more innovative and integrated approaches. This project introduces an integrated smart home automation system designed to enhance the well-being and independence of seniors while providing invaluable support to caregivers. Leveraging cutting-edge technologies such as AI integration, IoT devices, and voice recognition, the system offers a seamless and intuitive user experience. Features include automated task management, real-time environmental monitoring, medication management, and emergency response capabilities. By streamlining daily routines, promoting social engagement, and ensuring safety, the system aims to improve the overall quality of life for seniors while alleviating stress and burden for caregivers. Additionally, this project explores the potential of humanoid robot technology, exemplified by robot Nikky, to further augment elderly care through human-like interactions and assistance. This integrated approach not only addresses physical needs but also focuses on cognitive stimulation and emotional well-being, fostering a sense of companionship and purpose for seniors. Through these efforts, this project seeks to address the evolving needs of aging populations and contribute to the advancement of technology-driven solutions in elderly care and support services. Key Words: Home automation, Robotics, Smart and healthy living, Humanoid Robot for personal interaction

Printed Self-Healing Stretchable Electronics for Bio-signal Monitoring and Intelligent Packaging.

Integrating self-healing capabilities into printed stretchable electronic devices is important for improving performance and extending device life. However, achieving printed self-healing stretchable electronic devices with excellent device-level healing ability and stretchability while maintaining outstanding electrical performance remains challenging. Herein, a series of printed device-level self-healing stretchable electronic devices is achieved by depositing liquid metal/silver fractal dendrites/polystyrene-block-polyisoprene-block-polystyrene (LM/Ag FDs/SIS) conductive inks onto a self-healing thermoplastic polyurethane (TPU) film via screen printing method. Owing to the fluidic properties of the LM and the interfacial hydrogen bonding and disulfide bonds of TPU, the as-obtained stretchable electronic devices maintain good electronic properties under strain and exhibit device-level self-healing properties without external stimulation. Printed self-healing stretchable electrodes possess high electrical conductivity (1.6×105Sm-1), excellent electromechanical properties, and dynamic stability, with only a 2.5-fold increase in resistance at 200% strain, even after a complete cut and re-healing treatment. The printed self-healing capacitive stretchable strain sensor shows good linearity (R2 ≈0.9994) in a wide sensing range (0%-200%) and is successfully applied to bio-signal detection. Furthermore, the printed self-healing electronic smart label is designed and can be used for real-time environmental monitoring, which exhibits promising potential for practical application in food preservation packaging.

Real-time Fishpond Water Temperature Monitoring with Internet of Things (IoT) Technology

The quality of pond water is an important factor in ensuring the health and sustainability of the organisms within it. Internet of Things (IoT) technology has enabled the development of sophisticated and effective monitoring systems to monitor water quality in real-time. One important parameter that needs to be monitored is the temperature of the pond water, as unstable temperatures can harm aquatic organisms. To obtain good quality and disease-free fish at normal times, the optimal temperature for catfish fry is around 28˚C - 30˚C. Manual monitoring or using thermometer measuring instruments is still very traditional and limited to the ability of supervisors to carry out continuous monitoring and may cause delays in detecting significant temperature changes. This research aims to analyze the monitoring system of pool water quality based on temperature by utilizing IoT technology. The proposed system uses a DS18B20 temperature sensor that is wirelessly connected to the IoT network. The temperature data collected in real time will be uploaded to Firebase for storage and further analysis. Through the development of this IoT-based pond water quality monitoring system, it is expected to increase efficiency and reliability in monitoring pond water quality. From the monitoring conducted for approximately 24 hours, the temperature in the fishpond is 28°-30°. After analyzing with direct monitoring, the good temperature is at night at 18.00-00.00 WIB. In conjunction with a standard thermometer, resulted in an average temperature sensor error rate of less than 5%, highlighting the reliability of the IoT technology in real-time environmental monitoring.

An IoT Intelligent Approach for Safety and Efficiency of Robotic Medicine Delivery in Hospitals

In this paper, we introduce an IoT-based robotic medicine delivery system designed to boost safety and efficiency in hospital settings. This IoT-embedded robotic system integrates advanced sensors such as proximity, temperature, pressure, pulse rate, etc., sensors for ensuring real-time health and environmental monitoring behind this system. The robot is connected to a centralised controller, enabling healthcare workers to monitor patients using a cloud-based system. . Our robotic system moves about with the support of both line follower and LiDAR scanning technology for environmental safety and proximity detection. Experimental results show that the robotic system was able to navigate static obstacles and dynamically move away from the mobile entities within its reach without touching them. Besides, the proposed robotic system was able to reach the bed at the right time under normal operating conditions, which shows the efficiency of this approach in real-time settings. Patient feedback suggests that patients are very happy about the system, in terms of its usefulness, trustworthiness, and to their suprise to be taken care of by a robot. Overall, our study shows the potential of the proposed robotic system in streamlining the existing medicine delivery process in hospitals. It can potentially improve various aspects including safety, efficiency, patient compliance, and quality of care delivery. The promising results suggest further studies for incorporating advanced features and for ensuring compliance with regulatory requirements. Additional studies are to be conducted to further improve the proposed sensor-based robotic medicine delivery system, to facilitate its adoption in hospitals.

Eco-efficient well planning: Engineering solutions for reduced environmental impact in hydrocarbon extraction

This paper explores the concept of eco-efficient well planning, aiming to minimize the environmental impact of hydrocarbon extraction through engineering solutions. Faced with increasing environmental concerns, the industry necessitates a shift towards sustainable practices. The paper analyzes technologies such as advanced drilling techniques, intelligent reservoir management, and real-time environmental monitoring. Key findings showcase the potential of eco-efficient well planning to significantly reduce the carbon footprint, water usage, and land disturbance associated with hydrocarbon extraction. Real-world examples demonstrate successful implementation in diverse contexts, highlighting economic viability, regulatory compliance, and improved stakeholder engagement. This research advocates for integrating eco-efficient well planning as a fundamental practice in the future of hydrocarbon extraction. The engineering solutions proposed offer a pathway to a more sustainable and environmentally conscious industry, contributing to a responsible energy landscape amidst global transitions to renewable sources.

Integrating IoT Sensors, Cloud AI, and Satellite Broadband for Enhanced ESG Governance in Smart Cities: A Tripartite Approach to Real-Time Environmental Monitoring

Urbanization is accelerating at an unprecedented rate, pushing the limits of environmental sustainability and demanding innovative solutions for effective management. Traditional methods for monitoring the environment fall short, lacking the scalability, immediacy, and comprehensive coverage required for modern urban settings. Our paper introduces an innovative solution to bridge these gaps, harnessing the power of Internet of Things (IoT) sensors, advanced cloud-based AI through Claude 3 Sonnet models on Amazon Bedrock, and the extensive reach of Project Kuiper's satellite broadband. This threefold integration forms a cutting-edge approach to urban environmental governance. It enables the analysis of environmental data in real time, offers predictive insights, and achieves wide-ranging coverage. The system equips city leaders, policymakers, and citizens with precise, actionable information, fostering decisions that align with Environmental Social Governance (ESG) goals. More than just improving urban life and sustainability, our approach makes environmental data accessible to all, promoting a collaborative and inclusive model of urban environmental management.

Implementation of a Wireless Sensor Network for Environmental Measurements

Nowadays, the need to monitor different physical variables is constantly increasing and can be used in different applications, from humidity monitoring to disease detection in living beings, using a local or wireless sensor network (WSN). The Internet of Things has become a valuable approach to climate monitoring, daily parcel monitoring, early disease detection, crop plant counting, and risk assessment. Herein, an autonomous energy wireless sensor network for monitoring environmental variables is proposed. The network’s tree topology configuration, which involves master and slave modules, is managed by microcontrollers embedded with sensors, constituting a key part of the WSN architecture. The system’s slave modules are equipped with sensors for temperature, humidity, gas, and light detection, along with a photovoltaic cell to energize the system, and a WiFi module for data transmission. The receiver incorporates a user interface and the necessary computing components for efficient data handling. In an open-field configuration, the transceiver range of the proposed system reaches up to 750 m per module. The advantages of this approach are its scalability, energy efficiency, and the system’s ability to provide real-time environmental monitoring over a large area, which is particularly beneficial for applications in precision agriculture and environmental management.

IoT based Smart Poultry Management System

This research focuses on the development of an intelligent system utilizing embedded frameworks and smartphone technology for monitoring and managing chicken farms. The proposed system aims to control environmental parameters and automate feeding and water supply, offering a cost-effective, asset-saving, and quality-oriented approach to poultry farming. By leveraging smart devices and IoT technologies, this system can replace manual labour, addressing industry labour challenges and introducing semi-automation. The system encompasses tasks such as feeding using a container, water supply through nipple waterers, temperature and humidity regulation with fans and heaters, automatic lighting, and real-time environmental monitoring accessible through IoT cloud data. This technology-driven solution holds promise for enhancing productivity and efficiency in poultry farming and broader agricultural applications.

Portable smartphone platform utilizing AIE-featured carbon dots for multivariate visual detection for Cu2+, Hg2+ and BSA in real samples

Heavy metals cause serious toxic threats to both environment and human health. The multivariate, instrument-free, portable, and rapid detection strategy is crucial for determination of heavy metals. Herein, aggregation-induced emission (AIE) featured carbon dots (SN-CDs) were fabricated hydrothermally by optimizing co-doping precursors. With bright yellow emission at 560 nm, the SN-CDs were utilized for multivariate sensing Cu2+, Hg2+ and bovine serum albumin (BSA) based on AIE behavior and static quenching effect, with detection limits of 0.46 μmol·L−1, 25.8 nmol·L−1 and 1.52 μmol·L−1. A portable smartphone platform was constructed to enable portable, prompt, and sensitive analysis for Cu2+, Hg2+, and BSA via different strategies in real water and food samples with satisfied recovery. Moreover, a logic gate circuit was designed to provide the possibilities for utilization of intelligent facility. The proposed AIE SN-CDs possessing great contribution in preferable sensing performance, present promising prospects in real-time monitoring of environment and food safety.

A BIM-enabled digital twin framework for real-time indoor environment monitoring and visualization by integrating autonomous robotics, LiDAR-based 3D mobile mapping, IoT sensing, and indoor positioning technologies

Existing indoor environment monitoring methods often fall short due to the lack of using standardized evaluation methods, missing spatial information, or the absence of user-friendly visualization interfaces. To address these limitations, this paper proposes a novel digital twin framework for real-time indoor environment monitoring and visualization by integrating Building Information Modeling (BIM), Internet of Things, autonomous robot-based mobile mapping and sensing, and indoor positioning technologies. The goal is to autonomously monitor indoor environmental conditions and provide real-time visualization using a standardized assessment index with estimated spatial information in BIM software. The proposed framework was demonstrated and validated in two indoor scenarios for air quality monitoring and visualization-related applications. Implementation results revealed that: the framework effectively captured spatial characteristics of air quality in small and medium-sized indoor spaces and intuitively visualized it using a BIM digital twin model; and, the ultra-wideband-based indoor positioning system achieved a nearly centimeter-level positioning precision. Practical interpretations of the results demonstrated that the proposed framework can not only detect and localize the potential sources of air pollutants but can also identify the possible causes of low indoor air quality. This study ultimately contributes to the body of knowledge by offering a novel and generic digital twin framework that seamlessly bridges various popular yet previously isolated technologies for better informing building facility management-related decisions. This paper is the first research work that integrates several emerging technologies (i.e., BIM, digital twins, IoT sensing, indoor localization/positioning/tracking, and robotics) for advancing real-time monitoring and visualization of indoor environmental conditions. While the proposed framework was applied for indoor air quality monitoring, it could be extended and used for various indoor environmental assessment and visualization-related applications.