Remote Monitoring Capabilities Research Articles

Home Automation System Using ESP8266 and Blynk Mobile App

The emergence of Internet of Things (IoT) technology has revolutionized various aspects of our lives, including home automation. This abstract introduces a home automation system based on the ESP8266 microcontroller, a versatile and cost-effective solution for IoT projects. The proposed system aims to provide homeowners with remote control and monitoring capabilities over various home appliances and devices through a user-friendly interface. The core components of the system include the ESP8266 microcontroller, which serves as the central processing unit, and sensors/actuators for interaction with the physical environment. The system utilizes Wi-Fi connectivity to establish communication between the ESP8266 module and a designated mobile application or web interface, enabling users to remotely monitor and control connected devices from anywhere with internet access. Keywords: Home-Automation, Energy Efficient, Blynk IOT Platform.

Implementation of Health Monitoring System of Induction Machine Using Computational Intelligence

The induction machine is a critical component in various industrial applications, and its reliable operation is paramount for uninterrupted processes. To ensure the continuous performance and prevent unexpected failures, the implementation of an effective health monitoring system is imperative. In this study, we propose a novel approach to monitor the health of induction machines using computational intelligence techniques. The proposed system integrates advanced computational intelligence algorithms, including machine learning and signal processing methods, to analyze the operational data and identify potential faults or anomalies in the induction machine. Specifically, we employ techniques such as artificial neural networks, support vector machines, and genetic algorithms to process the sensor data acquired from the induction machine. The health monitoring system operates in real-time, continuously analyzing various parameters such as current, voltage, temperature, and vibration patterns to detect deviations from normal operating conditions. By establishing baseline performance profiles and employing pattern recognition algorithms, the system can identify early signs of degradation or impending failures, allowing for proactive maintenance interventions. Moreover, the proposed system offers flexibility and scalability, enabling adaptation to different types of induction machines and operating environments. It facilitates remote monitoring capabilities, enabling maintenance personnel to access real-time health status and diagnostic information from anywhere, facilitating timely decision-making and proactive maintenance strategies.  

Exploring Intervention Techniques for Alzheimer's Disease: Conventional Methods and the Role of AI in Advancing Care

Alzheimer's disease (AD) is a neurodegenerative condition characterized by cognitive decline and functional impairment. This study compares conventional intervention techniques with emerging artificial intelligence (AI) approaches to AD. Intervention technique refers to a specific method or approach employed to bring about positive change in a particular situation. In the context of AD, such techniques are crucial as they aim to slow down the progression of symptoms, alleviate behavioral challenges, and support patients and their caretakers in managing the complexities of the condition. Conventional intervention techniques, such as cognitive stimulation and reality orientation, have demonstrated benefits in improving cognitive function and emotional well-being. Conventional intervention approaches are widely preferred as they have a proven track record of effectiveness, personalized response, cost-effectiveness, and patient-centered care. Despite these benefits, they are limited by individual variability in response and long-term effectiveness. On the other hand, AI-based approaches such as Computer Vision and Deep Learning (DL) hold the potential to revolutionize Alzheimer's interventions. These technologies offer early detection, personalized care, and remote monitoring capabilities. They can provide tailored interventions, assist decision-making, and enhance caregiver support. Although AI-based interventions face challenges such as data privacy and implementation complexity, their potential to transform Alzheimer's care is significant. This research paper compares conventional and AI-based approaches. It reveals that while traditional techniques are well-established and have proven benefits, AI-based interventions offer novel opportunities for personalized and advanced care. Combining the strengths of both approaches may lead to more comprehensive and effective interventions for individuals with AD. Continued research and collaboration are crucial to harness the full potential of AI in improving Alzheimer's care and enhancing the quality of life for affected individuals and their caregivers.

Review on VEND IT!- A Smart Vending Machine

Abstract: Traditional vending machines, though reliable, have often lagged behind in terms of convenience, personalization, and sustainability. As society rely on digital connectivity and automation continues to grow, the need for a smarter, more responsive vending solution has become increasingly evident. In a world marked by constant technological innovation and evolving consumer preferences, the time has come to reimagine the traditional vending machine. "Vend It" is a visionary project that aims to reinvent the conventional vending machine, leveraging cutting-edge smart technology to create a seamless, efficient, and user-friendly vending experience. VEND IT! encompasses a comprehensive suite of innovations, ranging from intelligent inventory management to user-centric interface design, and remote monitoring and management capabilities.

Sensorsync auto: Integrating IoT sensors for smart vehicle air quality monitoring

This project introduces an innovative Air Quality Monitoring System based on IoT technology, leveraging components such as NodeMCU, DHT11, MQ135, a buzzer, an LCD, and the Blynk 2.0 app. The primary objective is to provide real-time monitoring of environmental conditions, encompassing temperature, humidity, and air quality, while also offering audible alerts for immediate user notification. The NodeMCU functions as the central microcontroller, interfacing with the DHT11 sensor for temperature and humidity readings and the MQ135 sensor for air quality measurements. The collected data is visually displayed on a 16x2 LCD screen. Additionally, the system enables remote monitoring and control through the Blynk 2.0 app. The app displays comprehensive information using gauge widgets for temperature, humidity, and air quality, along with detailed statistics through a display widget. To enhance user awareness, an integrated buzzer delivers audible alerts when air quality exceeds predefined thresholds. The Arduino sketch incorporates essential libraries for sensor interfacing, data processing, and seamless integration with the Blynk app. The app is configured with widgets corresponding to virtual pins in the sketch, ensuring smooth communication between the hardware and the app. This IoT-based Air Quality Monitoring System serves as a valuable tool for individuals and communities concerned about indoor or outdoor air quality. The system's features, including real-time data visualization, remote monitoring capabilities, and an alert system, contribute to creating a more informed and healthier living environment.

Web Integrated Continuous Positive Airway Pressure System

Keywords: Continuous Positive Airway Pressure(CPAP) 1, Sleep Apnea 2, Neonatal 3, Respiratory Distress Syndrome (RDS) 4, Premature baby 5, Transpiration medical device 6, Internet of Things 7 CPAP, a non-invasive ventilation method crucial in neonatal care, has significant implications for the survival of very low birth weight preterm infants. However, its widespread adoption is hindered by cost constraints and the need for additional features like Apnea Hypopnea Index (AHI) monitoring. This study seeks to overcome these challenges by developing an affordable CPAP device for Neonatal Intensive Care Units (NICUs). By incorporating sensors for data collection and a microcontroller-based control system, this device aims to optimize CPAP function while minimizing energy consumption. Furthermore, the collected data will be securely stored on a cloud platform, potentially benefiting both neonatal care and sleep apnea management. Recognizing the vital role of sleep in human health, this innovation underscores the importance of restorative rest for physical and mental well-being. Highlights: · Web-integrated CPAP devices often come with remote monitoring capabilities. This allows healthcare providers to remotely monitor patients' CPAP usage data, including compliance and efficacy metrics, without requiring in-person visits. This feature facilitates better patient management and allows for timely intervention if issues arise. · These devices typically track and record various parameters such as usage hours, mask leaks, apnea-hypopnea index (AHI), and pressure settings. The data can be accessed by both patients and healthcare providers, providing insights into treatment progress and effectiveness over time. · Many web-integrated CPAP devices offer cloud connectivity, allowing users to sync their data to secure online platforms. This feature enables seamless access to CPAP usage data from multiple devices, such as smartphones, tablets, or computers, enhancing convenience and flexibility for users. · Some modern CPAP machines offer wireless connectivity options, such as Bluetooth or Wi-Fi, enabling users to connect their devices to compatible accessories or smartphone apps. This connectivity allows for features like remote control of device settings, automatic software updates, and integration with health tracking apps for a more comprehensive health management experience. · The integration of web-based CPAP platforms with telemedicine services allows for virtual consultations between patients and healthcare providers. This feature enhances access to care, particularly for patients in remote or underserved areas, and enables real-time adjustments to CPAP settings based on patient feedback and data analysis.

Fish Tank: An IoT-Based Mini Aquarium Control System

The manual management of aquarium systems is a meticulous process involving hands-on monitoring of various critical parameters such as water quality, feeding, lighting, and maintenance. However, this approach comes with challenges, including significant time consumption, the potential for human errors, and a lack of remote monitoring capabilities. To overcome these challenges, the development of automated smart aquarium systems has emerged as a transformative solution. These systems aim to simplify maintenance, enhance the well-being of aquatic life, provide remote accessibility, and contribute valuable data for research and education in the field. In the context of this evolutionary shift, this paper proposes a comprehensive Fish Tank system built upon the innovative IoT solution, IoT talk. This system utilizes sensors to drive actuators in real-time, enabling intelligent control of various water conditions within the aquarium. A notable feature of this proposed system is the implementation of a precise fish-feeding mechanism, enhancing the overall care provided to aquatic life. The paper goes further to present an analytic model, simulation, and measurement experiments that delve into the effects of IoT message delays and loss on water condition control, contributing valuable insights to the efficiency of the proposed system. Moreover, the proposed Fish Tank system incorporates advanced monitoring tools seamlessly connected to a robust database. This integration allows for efficient data storage and retrieval, enhancing the overall performance and reliability of the system. By extending this connectivity to a mobile application, the solution not only reduces human effort and errors in aquarium management but also provides users with convenient remote access. This transformative approach ensures continuous monitoring and control of the aquarium environment, ultimately contributing to the sustained health and well-being of aquatic life

Dehydration of Food Materials using Solar Dryer with Mobile App Integration

This study explores the development and integration of a solar dryer system using microcontrollers-NodeMCU and Arduino UNO with a mobile application to enhance the efficiency and user experience of food drying processes. The solar dryer utilizes renewable energy from the sun to dehydrate fruits, vegetables, and other perishable items, contributing to reduced energy consumption and environmental impact. The integration with a mobile app aims to provide users with remote control and monitoring capabilities, making the drying process more convenient and user-friendly. The mobile application allows users to select the specific product to dry and monitor drying parameters such as temperature and humidity levels, providing a customizable and automated experience. The integration of a solar dryer with a mobile app not only enhances the sustainability of food preservation but also promotes efficient resource management. By leveraging the power of renewable energy and smart technology, this system aims to contribute to the global shift towards eco-friendly and technologically advanced solutions for agriculture and food processing. The proposed solar dryer represents a step forward in creating a more sustainable and user-centric approach to food preservation, aligning with the growing demand for innovative solutions in the realm of renewable energy and smart agriculture.

IoT-based health monitoring and fault detection of industrial AC induction motor for efficient predictive maintenance

This research paper presents the development and implementation of an integrated condition monitoring and fault detection system for AC induction motors using a combination of sensors, GSM communication, and a cloud-based Internet of Things (IoT) platform. The proposed system aims to enhance industrial motors’ operational reliability and efficiency by providing real-time data monitoring and early fault detection. The key components of the system include temperature, vibration, current, voltage, and speed sensors, which are strategically placed to gather critical motor performance data. These sensors feed data to an Arduino-based control unit responsible for sensor data acquisition and processing. To ensure timely response to anomalies, the system is equipped with an alarm system and GSM alerts, which notify designated personnel in case of abnormal motor behavior. Moreover, the paper incorporates remote monitoring capabilities, enabling users to access motor health data and real-time status from a distance. Historical data is also stored for analysis and comparison through the integration of a cloud-based Blynk-IoT platform. Additionally, the system facilitates RPM control and utilizes relay modules for seamless motor control and protection. The proposed system was tested and validated using Proteus for circuit diagram simulation and Arduino for sensor coding. The results demonstrate its effectiveness in detecting abnormal motor behavior and its potential to prevent catastrophic failures by enabling predictive maintenance. The proposed system successfully detects and displays abnormalities in important parameters like vibration, temperature, speed, three-phase currents, and voltages with 99% accuracy.

Driver Safety: Accident Prevention and Detection System

The integrated Accident Prevention and Detection System is a groundbreaking venture poised to elevate vehicular safety with advanced technology. By amalgamating a NodeMCU microcontroller, alcohol detection sensor, eye blink sensor, and GSM module, this system offers a holistic solution. Monitoring alcohol levels and signs of fatigue, the sensors feed data to the NodeMCU, the brain of the system. Real time analysis allows the system to foresee potential collisions and promptly intervene. Beyond collision prevention, the GSM module enables emergency communication, instantly alerting authorities via SMS in case of a looming collision. With remote monitoring capabilities and GPS tracking for accurate location data, this system is tailored for adaptability and user- friendliness. By combining these elements, it not only detects impairment but also proactively mitigates risks, standing as an innovative safeguard for road safety. Key Words: NodeMCU microcontroller, Eye Blink Sensor, GSM Module, Alcohol Detection Sensor, SMS Alerts, Vehicle Safety, Remote Monitoring, GPS Tracking.

Digital Solutions Available to Be Used by Informal Caregivers, Contributing to Medication Adherence: A Scoping Review.

Medication adherence is essential for managing chronic diseases and achieving optimal health outcomes. However, this process is often challenging, particularly for patients with complex care needs. Informal caregivers play a pivotal role in supporting medication management, but they may face resource limitations and a lack of necessary support. Digital health tools offer a promising avenue to enhance medication adherence by providing reminders, education, and remote monitoring capabilities. This scoping review aimed to identify and evaluate digital solutions available to informal caregivers for improving medication adherence. A systematic search of PubMed and Web of Science was conducted using relevant keywords. Four studies were included in the review, examining a variety of digital tools including mobile apps, SMS messaging, and wearable devices. These tools demonstrated efficacy in improving medication adherence, managing disease symptoms, and enhancing quality of life for patients and caregivers. Digital health interventions hold the potential to revolutionize medication adherence among chronic disease patients. By empowering informal caregivers, these tools can bridge the gaps in medication management and contribute to better health outcomes. Further research is warranted to optimize the design, implementation, and evaluation of digital interventions for medication adherence.

Near-Infrared Off-Axis integrated cavity output spectroscopic sensor system for ammonia leakage measurement in cold store using embedded Multi-Core processor

A near-infrared off-axis integrated cavity output spectroscopic (OA-ICOS) sensor system for ammonia (NH3) leakage detection was developed. A distributed feedback (DFB) laser was used to target the absorption line of NH3 at 6612.167 cm−1. Experiments were conducted to evaluate the performance of the sensor system. When the averaging time is 0.8 s, the detection limit (1σ) of NH3 is ∼ 1.21 parts-per-million by volume (ppmv) using wavelength modulation spectroscopy (WMS). The 10–90 % response time of the NH3 sensor system is ∼ 7.2 s. Based on the developed local monitoring and control platform (LMCP), cloud server, remote monitoring and control end (RMCE), the unattended NH3 leakage detection experiments were carried out in a cold store located in Jilin City. The experimental results confirmed the rapid detection capability of the reported sensor system for NH3 leakage as well as the capability of remote monitoring and control.

IoT Based Smart Agriculture / Gardening

IoT Based Smart Agriculture / Gardening

Effective Emergency Communication through Public Displays: A Real-Time Evaluation with the Emergency Communication Display Test

In the study of "Effective Emergency Communication through Public Displays," our research indicates major advancements and problems in using public displays to disseminate essential information during crises. According to data research, there has been a 33% rise in the deployment of high-resolution 4K TVs, showing a technical change toward improved message visibility and clarity. Geographic distribution has improved, with a 10% increase in ideal placements in high-risk regions due to population density and vulnerability evaluations. Message design and content adherence to plain language standards, as well as the usage of visual aids and standardized symbols, have all increased by 5%. The psychological components of message reception highlight the necessity of simple language and high-resolution panels, as LED screens result in a 15% higher message understanding rate than LCD screens, while jargon-laden messages raise audience anxiety levels by 25%. Although resilience evaluations have shown a 15% increase in the existence of backup power supplies and a 10% rise in remote monitoring capabilities, 20% of installations still have physical vulnerabilities. These results suggest a complete strategy to improving the efficacy of public displays for emergency communication, highlighting the need of continual technological improvements and best practices to protect the safety and well-being of different groups during disasters.

Machine Learning based Precision Agriculture Model for Farm Irrigation to Optimize Water Usage

The food demand is ever increasing each year and to meet this demand precision agricultural approach using machine learning tools play an important role. Precision irrigation systems integrate cutting-edge technologies, such as sensors, controllers, data analytics and internet, to achieve sustainability in agriculture and maximize water use so as to improve crop production while minimizing water wastage and climate impact. The main purpose of this article is to find out the precise water requirements for a particular area of the land by using soil moisture sensors. These sensors provide real-time data that is transmitted to a central control unit, which utilizes data driven algorithms to analyze moisture levels in the soil and controls the water supply. Furthermore, the model developed offers remote monitoring and control capabilities, enabling farmers to access and manage the system from anywhere using mobile or web application. This feature allows farmers to remotely adjust irrigation schedules, receive real-time alerts and notifications, and track water consumption, promoting convenient and efficient management of water resources. Thus by using effective water management techniques such as precision irrigation, controlling the water quality, will accomplish optimizing water usage and intern optimizes the yield.

Precision Agriculture Model for Farm Irrigation using Machine Learning to Optimize Water Usage

The food demand is ever increasing each year and to meet this demand precision agricultural approach using machine learning tools play an important role. Precision irrigation systems integrate cutting-edge technologies, such as sensors, controllers, data analytics and internet, to achieve sustainability in agriculture and maximize water use so as to improve crop production while minimizing water wastage and climate impact. The main purpose of this article is to find out the precise water requirements for a particular area of the land by using soil moisture sensors. These sensors provide real-time data that is transmitted to a central control unit, which utilizes data driven algorithms to analyze moisture levels in the soil and controls the water supply. Furthermore, the model developed offers remote monitoring and control capabilities, enabling farmers to access and manage the system from anywhere using mobile or web application. This feature allows farmers to remotely adjust irrigation schedules, receive real-time alerts and notifications, and track water consumption, promoting convenient and efficient management of water resources. Thus by using effective water management techniques such as precision irrigation, controlling the water quality, will accomplish optimizing water usage and intern optimizes the yield.

The Impact/Role of Artificial Intelligence in Anesthesia: Remote Pre-Operative Assessment and Perioperative

Artificial intelligence is a thriving field in the modern world today. Almost every other operation in today’s world is being integrated with the application of Artificial Intelligence (AI). Artificial Intelligence does not only include the automation of conventional processes, but also includes the introduction of several new programs and interactions that help make work easier for everyone.
 The introduction of artificial intelligence (AI) into the realm of anesthesia, particularly in remote pre-operative assessment and perioperative care, brings forth a nuanced landscape of advantages and challenges. On the positive side, AI demonstrates remarkable efficiency and precision in the preoperative phase, rapidly analyzing extensive datasets to offer accurate insights into patient health and potential risks. Its expertise in predicting and struggling through anesthesia-related risks stands out, aiding healthcare professionals in anticipating challenges and allowing for personalized interventions. The capability to tailor anesthesia plans based on individual patient characteristics further adds a layer of sophistication, potentially optimizing administration and improving overall outcomes. In perioperative care, AI’s remote monitoring capabilities provide real-time insights into vital signs and potential complications, enabling patient safety through prompt responses. Additionally, AI serves as a valuable decision support system, offering recommendations and additional information for more informed decision-making.
 This article shall review the scope of artificial intelligence within the field of anesthesia and would reflect upon how it has helped people living in remote areas access better healthcare facilities through its proposition.

Advancing nursing practice with artificial intelligence: Enhancing preparedness for the future.

This article aimed to explore the role of AI in advancing nursing practice, focusing on its impact on readiness for the future. A position paper, the methodology comprises three key steps. First, a comprehensive literature search using specific keywords in reputable databases was conducted to gather current information on AI in nursing. Second, data extraction and synthesis from selected articles were performed. Finally, a thematic analysis identifies recurring themes to provide insights into AI's impact on future nursing practice. The findings highlight the transformative role of AI in advancing nursing practice and preparing nurses for the future, including enhancing nursing practice with AI, preparing nurses for the future (AI education and training) and associated, ethical considerations and challenges. AI-enabled robotics and telehealth solutions expand the reach of nursing care, improving accessibility of healthcare services and remote monitoring capabilities of patients' health conditions.

Wireless Sensor Network Based Gas Monitoring System Utilizing ZigBee Technology

This paper presents a ZigBee-based wireless network gas monitoring system, utilizing ZigBee technology to enable reliable and low-cost communication. The system leverages small sensor nodes and ZigBee protocols to collect data on physical phenomena, particularly gas levels, in various environments. The network's low-power consumption and extended range capabilities allow for effective gas sensing within a radius of up to 300 meters. While interoperability with devices from different manufacturers remains a limitation, the system showcases the versatility of ZigBee technology in diverse fields. The study evaluates the system's performance through simulation and experimentation, addresses potential challenges, and proposes future developments to enhance ZigBee-based wireless network gas monitoring systems, offering improved remote monitoring and manipulation capabilities. Furthermore, our research contributes to the advancement of wireless sensor network technology by demonstrating the practicality of ZigBee in gas monitoring. The findings from our experiments and simulations provide valuable insights into the optimization of such systems, which can have far-reaching impacts on environmental monitoring, safety, and industrial processes. In addition, our proposed enhancements pave the way for more reliable and efficient remote monitoring and control, offering substantial benefits for various industries, including environmental management, industrial automation, and beyond.

Identifying Putative Subsurface Microbial Drivers of Methane Flux on Earth and Mars

On Earth microorganisms are critical drivers of the methane cycle, both producing and consuming methane (Boetius et al. 2000, Knittel and Boetius 2009, Orphan et al. 2001). Molecular and isotopic-based investigations of archaeal-bacterial consortia catalyzing the anaerobic oxidation of methane (AOM) in marine methane seeps identified the pivotal role of these microorganisms in mitigating the release of methane into the atmosphere (Knittel and Boetius 2009, Orphan et al. 2001). In the marine environment, AOM is predominantly carried out by closely associated consortia of methanotrophic archaea (ANME) and sulfate reducing bacteria (SRB) coupling methane oxidation to sulfate reduction in the absence of oxygen. Wolf Spring (WS), Axel Heiberg Island, Nunavut is a hypersaline cold spring methane seep and the only known terrestrial permafrost hosted methane seep known to host ANME-1 archaea associated with AOM (Niederberger et al. 2010, Magnuson et al. 2022). Wolf Spring is an unparalleled analogue for putative subsurface brines and sites of methane release on Mars. Enigmatic observations of methane in the near-surface Martian atmosphere remain a tantalizing potential biosignature. The combination of field site characterization, microbial microcosm experiments, and in situ methane monitoring represents a coordinated interdisciplinary effort to identify methane driven microbial metabolisms not only critical to understanding methane flux in the Arctic, but also as possible drivers to the methane cycle on Mars. Detailed microbial characterization of these springs has identified a chemotrophic community dominated by sulfur cycling (Altshuler et al. 2022, Niederberger et al. 2010). To date, microbial and geochemical characterization has been carried out on sediment samples to a few centimeters depth. This study expands on these initial studies, with the successful collection and analysis of deeper sediment cores at WS focusing on AOM activity to better understand the microorganisms involved and the methane cycling capacity at depth. Two decades of observing methane on Mars (Mumma et al. 2009) have generated data indicative of a dynamic, geochemical system characterized by a profile similar to the release of methane from seeps on Earth (Etiope and Oehler 2019) producing both distinct pulses known as plumes and slow background seepage. These observations suggest as of yet unknown geochemical and potentially geobiological methane sources and sinks. While methane can be produced abiotically (Etiope and Lollar 2013), on Earth most methane is biogenic. Determining the biogenicity of CH4 is non-trivial and requires a correlated approach including determination of carbon isotopes. In terrestrial systems, biogenic CH4 is 13C depleted. To characterize methane sources and sinks on Mars, near surface measurements at a frequency not possible with existing instrumentation are required. We are currently developing off-axis integrated cavity-enhanced output (OA-ICOS) spectrometry as a portable trace gas analyzer capable of obtaining high frequency measurements of methane at the sub-ppb level (Sapers et al. 2021). Optimizing OA-ICOS trace methane measurements at WS will help refine sensitivity and measurement cadence in a Mars-like environment as well as providing new remote methane monitoring capabilities for Arctic methane emissions. We are currently developing in situ 12CH4:13CH4 capabilities using OA-ICOS technology. The importance of δ 13C as a biosignature is summarized in Fig. 1.