Remote Monitoring Capabilities Research Articles

Development of a remote therapeutic monitoring platform: applications for movement disorders

Digital health tools are emerging as a promising solution for optimizing Deep Brain Stimulation (DBS) therapy for neurodegenerative diseases such as Parkinson’s Disease (PD), addressing challenges of therapy maintenance, care access, and discrete assessments. Wearable technology and mobile health platforms can offer remote monitoring capabilities, allowing for real-time virtual programming with optimization of patients’ therapy, use of digital biomarkers that can help identify the onset, symptoms, medication-related fluctuations and side-effects, and response to DBS treatment. Through an investigational remote monitoring application (RM app) integrating patient-reported outcomes (PROs) and objective data using wearables, we aimed to develop an accessible, data-driven digital tool to monitor patient symptoms and deliver low-burden and easy-to-access DBS therapy. The platform is designed to be compatible with several consumer-grade wearables and ensures patient data privacy and security. We conducted a feasibility study to test the tool’s effectiveness in a large-scale DBS study, where subjects received a home-monitoring kit consisting of an iPhone and Apple Watch, pre-configured with the RM app to enable remote data collection. Compliance in this paper is defined as adherence to the RM app and Apple watch usage. Analysis of 67 subjects from the study demonstrated an average compliance rate of 63.3% for daily check-ins, 85.5% for monthly surveys and 61.9% for passive data with an average of 55 days of post-implant data. The study highlights the feasibility of frequent monitoring in the early post-implant period that could lead to optimal outcomes for patients. The platform developed can further optimize study execution and be extended to other chronic conditions treated with neuromodulation implants such as depression and pain.

Advancements in wearable heart sounds devices for the monitoring of cardiovascular diseases

AbstractCardiovascular diseases remain a leading global cause of mortality, underscoring the urgent need for intelligent diagnostic tools to enhance early detection, prediction, diagnosis, prevention, treatment, and recovery. This demand has spurred the advancement of wearable and flexible technologies, revolutionizing continuous, noninvasive, and remote heart sound (HS) monitoring—a vital avenue for assessing heart activity. The conventional stethoscope, used to listen to HSs, has limitations in terms of its physical structure, as it is inflexible and bulky, which restricts its prospective applications. Recently, mechanoacoustic sensors have made remarkable advancements, evolving from primitive forms to soft, flexible, and wearable designs. This article provides an in‐depth review of the latest scientific and technological advancements by addressing various topics, including different types of sensors, sensing materials, design principles, denoising techniques, and clinical applications of flexible and wearable HS sensors. This transformative potential lies in the capacity for ongoing, remote, and personalized monitoring, promising enhanced patient outcomes, amplified remote monitoring capabilities, and timely diagnoses. Last, the article highlights current challenges and prospects for the future, suggesting techniques to advance HS sensing technologies for exciting real‐time applications.

Lightweight Digit Recognition in Smart Metering System Using Narrowband Internet of Things and Federated Learning

Replacing mechanical utility meters with digital ones is crucial due to the numerous benefits they offer, including increased time resolution in measuring consumption, remote monitoring capabilities for operational efficiency, real-time data for informed decision-making, support for time-of-use billing, and integration with smart grids, leading to enhanced customer service, reduced energy waste, and progress towards environmental sustainability goals. However, the cost associated with replacing mechanical meters with their digital counterparts is a key factor contributing to the relatively slow roll-out of such devices. In this paper, we present a low-cost and power-efficient solution for retrofitting the existing metering infrastructure, based on state-of-the-art communication and artificial intelligence technologies. The edge device we developed contains a camera for capturing images of a dial meter, a 32-bit microcontroller capable of running the digit recognition algorithm, and an NB-IoT module with (E)GPRS fallback, which enables nearly ubiquitous connectivity even in difficult radio conditions. Our digit recognition methodology, based on the on-device training and inference, augmented with federated learning, achieves a high level of accuracy (97.01%) while minimizing the energy consumption and associated communication overhead (87 μWh per day on average).

Automation and innovation in home hemodialysis machines.

In this review, we discuss the timeline of innovation and technologic development in home hemodialysis (HHD) in the United States and the legislative approvals that accompanied them. The most recently FDA-approved home hemodialysis devices provide features that include on-demand and batch dialysate generation, access disconnect for venous needle dislodgement, touchscreen interface with visual and auditory prompts and animations, drop-in sterilized cartridges with prestrung tubing, hot water disinfection of tubing allowing extended-use, dialysate flow rates as high as 500 ml/min, as well as remote treatment monitoring capabilities. Furthermore, wearable/portable dialysis devices are currently under development to simplify dialysis delivery to patients with end-stage kidney disease. Home hemodialysis devices providing longitudinal hemodialysis across different clinical settings, virtual reality headsets for more personalized training, automated patient support, as well as wearable device development and innovations give hope for a future where home hemodialysis is more accessible and seamless.

Leveraging digital adherence technologies to enhance public health: insights from TB care in Ukraine

Abstract Background Effective tuberculosis (TB) care is vital for public health, especially in war-affected Ukraine. In response to the complex healthcare delivery landscape, where traditional directly observation treatment methods became impractical, digital adherence technologies (DATs) emerged as a promising solution, offering more autonomy for people, and remote monitoring capabilities for healthcare providers. Intervention The PATH-led USAID-funded Support TB Control Efforts in Ukraine (STBCEU) project implemented smart pill boxes in eleven Ukrainian regions. These devices equipped with built-in reminders helped people adhere to their medication regimen, and signaled healthcare providers when the box was opened. Results Since April 2022, STBCEU has supported 2,022 people with TB who used smart pill boxes during their treatment, including 489 (24%) with drug-resistant TB (DR-TB). A total of 482 people continue utilizing boxes. Of 1,540 people with TB who completed the use of boxes, 1,207 people (78%) successfully completed TB treatment with an average of 84% treatment adherence rate digitally reported by the technology. Conclusions The integration of smart pill boxes in TB management has enhanced people-centered care, ensuring treatment continuity even in challenging circumstances and providing healthcare providers with real-time adherence data. The innovation contributed to TB control efforts and health equity through expanding opportunities for people to choose their treatment model based on their needs, available resources and community support programs. Leveraging digital solutions proved to be essential in strengthening healthcare systems. In the realm of TB management, DATs enhance differentiated care, embodying a harmonious integration of technology automation and provider interventions. Key messages • Integration of smart pill boxes enhanced patient-centered care, providing real-time adherence data and empowering individuals to choose personalized treatment models. • Digital technologies like smart pill boxes transformed TB care, offering tailored support for patients and remote monitoring capabilities for healthcare providers even in challenging circumstances.

Data-Driven Water Monitoring System with Descriptive Analytics for Aquaculture Prawn Farms

This study aimed to develop a data-driven water monitoring system with descriptive analytics for aquaculture prawn farms. This system was designed to automate the monitoring of the water quality of the prawn farm so as to minimize labor and time spent for manually testing the water. Utilizing a rapid prototyping methodology, which is a rapid or quick creation of a prototype or a physical model of a device used in the development of a project, the said monitoring system was evaluated through a comparative analysis between the data gathered manually and the data collected by the monitoring system. To ensure reliability and validity of this monitoring system, it was used various times over several consecutive days. Also, a t-test was used to determine the significant differences between manual and system readings. Findings of the study showed that the designed water monitoring system provided accurate results, and the objectives of the study were met, particularly in terms of integrating a Raspberry Pi 3 microprocessor to automate the system in monitoring the water quality. It was also found out that the use of different sensors to test the water condition in terms of its temperature, pH level, salinity, dissolved oxygen, and turbidity was accurate. Furthermore, the system also met its objective of developing an Android mobile application to monitor the status remotely. With this, it is recommended for prawn farmers to utilize the system (with internet connectivity) for their efficient water quality management and to improve their remote monitoring capabilities.

Self-powered strain sensing devices with wireless transmission: DIW-printed conductive hydrogel electrodes featuring stretchable and self-healing properties

With rapid advancements in health and human–computer interaction, wearable electronic skins (e-skins) designed for application on the human body provide a platform for real-time detection of physiological signals. Wearable strain sensors, integral functional units within e-skins, can be integrated with Internet of Things (IoT) technology to broaden the applications for human body monitoring. A significant challenge lies in the reliance of most existing wearable strain sensors on rigid external power supplies, limiting their practical flexibility. In this study, we present an innovative strategy to fabricate glutaraldehyde (GA)-poly(vinyl alcohol) (PVA)/cellulose nanocrystals (CNC)/Poly(3,4-ethylenedioxythiophene):poly(styrene sulfonate) (PEDOT:PSS) conductive hydrogels through multiple hydrogen bonding systems. Combining the advantageous rheological properties of the precursor solution and the high specific surface area after freeze–thaw cycling, we have created a self-powered sensing system prepared by large-area printing using direct ink writing (DIW) printing. The resulting conductive hydrogel exhibits commendable mechanical properties (411 KPa), impressive stretchability (580 %), and robust self-healing capabilities (>98.3 %). The strain sensor, derived from the conductive hydrogel, demonstrates a gauge factor (GF) of 2.5 within a stretching range of 0–580 %. Additionally, the resultant supercapacitor displays a peak energy density of 0.131 mWh/cm3 at a power density of 3.6 mW/cm3. Benefiting from its elevated strain response and remarkable power density features, this self-powered strain sensing system enables the real-time monitoring of human joint motion. The incorporation of a 5G transmission module enhances its capabilities for remote data monitoring, thereby contributing to the progress of wireless tracking technologies for self-powered electronic skin.

ATC-CNN-Based IoT Framework for Real-Time Cardiovascular Monitoring: A Comparative Analysis with Deep Learning Models

Background: Cardiovascular diseases (CVDs), including hypertension, coronary heart disease, and ventricular fibrillation, remain the leading cause of death worldwide. The COVID-19 pandemic accelerated the adoption of telecardiology to minimize in-person interactions, enhancing access to cardiovascular care, especially in remote areas. However, traditional real-time monitoring (RTM) systems often require patient visits, creating a need for cost-effective, user-friendly alternatives that leverage IoT and AI for improved patient management. Methods: This study developed an IoT-enabled RTM system integrated with an Adaptive Thresholding Classifier-Convolutional Neural Network (ATC-CNN) framework for managing cardiovascular patients (CP). Data from a heart failure dataset in the UCI Machine Learning Archive was used to train and evaluate the model. Multiple deep learning (DL) algorithms, including MLP, CNN, LSTM, RNN, and the proposed ATC-CNN, were assessed based on accuracy, precision, recall, and F1 scores. Results: The ATC-CNN model achieved an accuracy of 0.9831, outperforming other DL models, including MLP, CNN, LSTM, and RNN. It demonstrated superior capabilities in handling complex cardiovascular data, offering reliable and timely diagnosis with high precision and recall. The integration of IoT and AI facilitated continuous monitoring and real-time data analysis, addressing key limitations of traditional RTM systems. Conclusion: The ATC-CNN framework shows great potential for revolutionizing cardiovascular care by enhancing remote monitoring capabilities and clinical decision-making. It provides an effective, real-time solution for early diagnosis and intervention, particularly in remote and underserved areas. Future research should focus on expanding datasets and refining the model for broader adaptability in diverse healthcare settings.

Automated Interpretation of Lung Sounds by Deep Learning in Children With Asthma: Scoping Review and Strengths, Weaknesses, Opportunities, and Threats Analysis.

The interpretation of lung sounds plays a crucial role in the appropriate diagnosis and management of pediatric asthma. Applying artificial intelligence (AI) to this task has the potential to better standardize assessment and may even improve its predictive potential. This study aims to objectively review the literature on AI-assisted lung auscultation for pediatric asthma and provide a balanced assessment of its strengths, weaknesses, opportunities, and threats. A scoping review on AI-assisted lung sound analysis in children with asthma was conducted across 4 major scientific databases (PubMed, MEDLINE Ovid, Embase, and Web of Science), supplemented by a gray literature search on Google Scholar, to identify relevant studies published from January 1, 2000, until May 23, 2023. The search strategy incorporated a combination of keywords related to AI, pulmonary auscultation, children, and asthma. The quality of eligible studies was assessed using the ChAMAI (Checklist for the Assessment of Medical Artificial Intelligence). The search identified 7 relevant studies out of 82 (9%) to be included through an academic literature search, while 11 of 250 (4.4%) studies from the gray literature search were considered but not included in the subsequent review and quality assessment. All had poor to medium ChAMAI scores, mostly due to the absence of external validation. Identified strengths were improved predictive accuracy of AI to allow for prompt and early diagnosis, personalized management strategies, and remote monitoring capabilities. Weaknesses were the heterogeneity between studies and the lack of standardization in data collection and interpretation. Opportunities were the potential of coordinated surveillance, growing data sets, and new ways of collaboratively learning from distributed data. Threats were both generic for the field of medical AI (loss of interpretability) but also specific to the use case, as clinicians might lose the skill of auscultation. To achieve the opportunities of automated lung auscultation, there is a need to address weaknesses and threats with large-scale coordinated data collection in globally representative populations and leveraging new approaches to collaborative learning.

Triboelectric Nanosensor Fabricated on Robotic Platform for Self-Powered Detection of Heavy Metal Ions in Aquatic Environments

The escalation of heavy metal ions pollution, caused by the natural processes as well as human activities has emerged as a significant global challenge. The accumulation of heavy metal ions in the human body primarily occurs through water and food consumption which poses a severe threat leading to irreversible health damage. To address this issue, the World Health Organization (WHO) has established permissible limits for various heavy metal ions in drinking water, ranging from 0.003 to 3 ppm. When these limits are exceeded, water is deemed contaminated which necessitates effective monitoring and detection strategies. Robotic chemical sensing platforms have become indispensable for tackling chemical threats by detecting harmful pollutants in toxic environments. However, chemical sensors integrated with robotic platforms encounter drawback such as reliance on external power sources. In response to this challenge, our approach integrates a highly sensitive, self-powered triboelectric nanosensor (TENS) into a robotic platform charged by a thermoelectric generator (TEG) and equipped with wireless transmission capabilities. This integration enables the rapid on-site detection of heavy metal ions. The system employs copper electrodes modified with ion-selective membranes (ISM) as sensing probes and solid triboelectric contact materials. Through periodic contact-separation cycles with ionic solutions, the system generates electrical output, facilitating the detection of various concentrations of Pb2+, As3+, and Cr6+ ions. The ionophores in the respective ISMs contribute to the selective binding capacity of the sensing probe to the particular ion. This self-powered robotic platform provides an innovative and cost-effective solution for automated chemical sensing in water environments. Its advantages include the avoidance of external power sources, low-cost operation, and the elimination of the need for frequent battery replacements. With its capability for remote monitoring and detection, this system emerges as an ideal tool for preventing and controlling heavy metal ions pollution, ultimately contributing to the intelligent monitoring of heavy metal ions in aquatic environments. Keywords: Heavy metal ions, Solid-liquid triboelectric nanosensor, Robotic platform, Self-powered detection, Copper electrodes.

A Review of Remote Monitoring in Neuromodulation for Chronic Pain Management.

Neuromodulation techniques have emerged as promising strategies for managing chronic pain. These techniques encompass various modalities of nerve stimulation, including Spinal Cord Stimulation (SCS), Dorsal Root Ganglion Stimulation (DRG-S), and Peripheral Nerve Stimulation (PNS). Studies consistently demonstrate significant improvements in pain intensity, quality of life, and reduced opioid usage among patients treated with these modalities. However, neuromodulation presents challenges, such as the need for frequent in-person follow-up visits to ensure proper functionality of the implanted device. Our review explored factors impacting compliance in current neuromodulation users and examined how remote monitoring can mitigate some of these challenges. We also discuss outcomes of recent studies related to remote monitoring of neuromodulation. While remote monitoring capabilities for neuromodulation devices is an emerging development, there are promising results supporting its role in improving outcomes for chronic pain patients. Higher patient satisfaction, improved pain control, and reduced caretaker burdens have been observed with the use of remote monitoring. This review discusses the current challenges with neuromodulation therapy and highlights the role of remote monitoring. As the field continues to evolve, understanding the importance of remote monitoring for neuromodulation is crucial for optimizing pain management outcomes.

The Role of Tele-Orthodontics in Enhancing Patient Compliance and Treatment Monitoring

ABSTRACT Background: Tele-orthodontics, the application of telecommunications technology in orthodontic care, has emerged as a promising tool to enhance patient compliance and facilitate treatment monitoring. In traditional orthodontic practice, patient compliance and monitoring are pivotal but often challenging aspects. Tele-orthodontics offers a solution by providing remote communication and monitoring capabilities, potentially improving patient engagement and treatment outcomes. Materials and Methods: This study conducted a retrospective analysis of orthodontic patients who utilized tele-orthodontics as part of their treatment plan. A total of 100 patients were included in the study, with ages ranging from 12 to 40 years old. Patients were provided with mobile applications or web-based platforms enabling them to communicate with their orthodontists, submit progress images, and receive feedback remotely. Treatment compliance was assessed based on adherence to scheduled appointments, wearing of orthodontic appliances, and completion of prescribed exercises. Results: The analysis revealed a significant improvement in patient compliance with the implementation of tele-orthodontics. Adherence to scheduled appointments increased by 30%, with patients attending 95% of their appointments compared to 65% before the introduction of tele-orthodontics. Moreover, compliance with wearing orthodontic appliances showed a remarkable rise, with patients consistently wearing their appliances for an average of 22 h per day, up from 14 h per day previously. Additionally, the completion rate of prescribed exercises saw a notable enhancement, with 80% of patients completing their exercises as instructed, compared to 50% previously. Conclusion: Tele-orthodontics plays a significant role in enhancing patient compliance and treatment monitoring in orthodontic care. The remote communication and monitoring capabilities provided by tele-orthodontic platforms empower patients to actively participate in their treatment process, leading to improved adherence to appointments, better compliance with wearing orthodontic appliances, and increased completion of prescribed exercises. These findings underscore the importance of integrating tele-orthodontics into orthodontic practice to optimize patient outcomes and satisfaction.

Structural evaluation of urban bridges in Amsterdam through InSAR-based displacement data

Thousands of bridges worldwide face growing risks due to aging materials, increased traffic loads, and climate change-induced weather extremes. Managing these assets is financially demanding, necessitating prioritisation strategies for interventions. Consequently, innovative approaches are urgently required to evaluate the structural conditions of these bridges continuously and regularly. Recent advancements in space-borne Interferometric Synthetic Aperture Radar (InSAR) technology offer cost-effective remote monitoring capabilities, ensuring extensive coverage and high spatial resolution. Multi Temporal (MT) InSAR techniques enable the reconstruction of millimetre-scale deformation measurements for a large number of assets, opening opportunities for long-term regional-scale monitoring of bridge deformations. However, a major challenge in utilising MT-InSAR-based displacement data operationally is that MT-InSAR analysis reconstructs only the projection of displacements along the satellite Line of Sight (LOS) direction. Due to the typical availability of only two satellite viewing geometries, in most cases the three-dimensional displacement field cannot be fully reconstructed. Consequently, without accounting for the anticipated motion of a given structure and its alignment with respect to the satellite flight path, the actual asset movement is likely to be underestimated, leading to erroneous interpretation. In this paper, we propose a method using the bridge typologies and their associated likely failure mechanisms to derive assumptions regarding expected displacement directions. Then, the information on bridge alignments with respect to the satellite flight direction is used to assess the MT-InSAR sensitivity to the expected displacement directions and define ad-hoc damage indicators. We tested the proposed method on urban bridges in Amsterdam, the Netherlands, using deformation measurements derived from TerraSAR-X data spanning 2011 to 2020. Findings have potential to enhance current procedures for the structural evaluation of bridges.

SANITIZATION SYSTEM FOR HOSPITALS

The paper introduces a robot equipped with UV light and spray sanitizer for cleaning surfaces and objects. It's designed to combat the spread of pathogens by sanitizing various environments, with a focus on hospitals, especially ICU and operation theatres. Its dual sanitization mechanisms, UV light and spray sanitizer dispenser provides optimal efficiency. This combination ensures thorough cleaning and disinfection of diverse surfaces, enhancing public health and safety. The robot's versatility makes it suitable for use in hospitals, transportation systems, educational institutions, and other high-traffic areas where sanitation is crucial. By integrating UV sanitization technology with a spray sanitizer mechanism, the robot offers comprehensive coverage against infectious diseases, providing an effective solution for maintaining cleanliness and hygiene in various settings. Overall, the robot's autonomous operation and remote monitoring capabilities make it a valuable tool for promoting public health and mitigating the spread of harmful pathogens. Key Words: sanitization, robot, UV light, spray sanitizer, pathogen control.

Development of Communication System between TPMS and Server using Combination of OFDM and Convolutional Code Technique Based on SDR

The Tire Pressure Monitoring System (TPMS) has evolved into an essential element of contemporary vehicles, playing a pivotal role in enhancing road safety and the overall driving experience. Traditionally, TPMS systems rely on dedicated hardware components for the collection and transmission of tire pressure data to the vehicle's onboard computer and the data is visible only to the driver. In this research, we have developed a wireless communication system between TPMS and a server, enabling tire pressure data to be accessible not only to the driver but also remotely traceable by others. To build a reliable communication system, we utilized a combination of Orthogonal Frequency Division Multiplexing (OFDM) and Convolutional Code technologies. This system is implemented using Software-Defined Radio (SDR) technology. This communication method employs OFDM to enhance data throughput and integrates Convolutional Code to mitigate errors in received data. Consequently, this approach achieves a maximum throughput of 119.19MBps when utilizing the OFDM system alongside 16QAM modulation. The bit error rate for received data without coding stands at 5.77%, but the application of Convolutional Code with a 1/2 code rate effectively reduces this error rate to 3.85%. This system improves the reliability of TPMS communication with the server while also ensuring a consistently high throughput. It enhances road safety and remote monitoring capabilities.

Validation of Beam Angle Switching Method Using a LED Lumen Maintenance Testing Device

In today’s world, LED predominantly ousted all forms of light sources because of its long lifetime, reliability, very low power consumption, and excellent controllability. That is why, it became a necessity to find proper methods to examine all its characteristics. As directed in several standards, an accelerated life test (ALT) is a good method to predict LED’s lumen depreciation. In correspondence with these standards, to find that persistency of light output from a medium power LED, a prototype is developed, which has predefined data storage capacity with remote monitoring capability, at the Electrical Engineering Department’s Illumination Engineering Laboratory at Jadavpur University to measure the lumen maintenance factor, junction temperature, and ambient humidity in the defined test chamber. Besides, to test the device a particular case study is done to validate beam angle switching, a method for LED dimming. The case study reveals that the dimming method has no effect on the LED’s junction temperature or light output.

Physiologist-led management of heart failure patients using cardiac implantable electronic device diagnostics during COVID-19: a 2-year UK perspective

Abstract Background/ Introduction The COVID-19 pandemic prompted greater use of remote monitoring capabilities of cardiac implantable electronic devices (CIED). The HeartLogic Index incorporated within certain Boston Scientific CIEDs uses device-derived physiological parameters to generate a score which may be predictive of worsening heart failure (HF). We aimed to define the utility of a novel service, led by cardiac physiologists acting autonomously upon HeartLogic Index alerts, to guide HF patient care during the pandemic. Purpose To assess the utility of a physiologist-led HF service, using CIED diagnostics combined with clinical assessment, to improve HF management. Methods In a single-centre prospective study, 153 Boston Scientific CRT/ICD patients were reviewed over a 2-year period using the integrated HeartLogic algorithm. In response to a HeartLogic alert indicating worsening HF, the patient was contacted by a cardiac physiologist using a structured telephone triage to assess HF symptoms. In those deemed as being at risk of decompensated HF based on clinical assessment, an intervention was made e.g. face-to-face physiologist-led clinical assessment, consultant review, medication optimisation. Results During the 2-year audit, 748 alerts were received from 60 patients, the majority of which had a HF implant indication (n=42). Twenty-one patients within the study population required an intervention due to worsening HF. Following intervention, there were no further hospitalisations within this group. Five hospitalisations were documented during the audit, only 3 of which were HF related. Of these HF hospitalisations, one patient denied HF symptoms at the time of telephone contact, one had a HF decompensation due to deteriorating renal failure and the final patient had a disconnected remote monitor. During the 2-year audit, there were a total of 13 deaths, 3 of which were classified as caused by HF. Conclusion A physiologist-led service may be of clinical utility within this complex patient population, with the audit results demonstrating favourable outcomes with a low rate of mortality and HF hospitalisations. The addition of this service can act to streamline the time from patient presentation to HF review, and therefore improve patient care.

Greenhouse Monitoring using ESP 8266

This research presents a groundbreaking approach to greenhouse agriculture through the implementation of a monitoring and control system using the ESP8266 microcontroller with Wi-Fi connectivity. By integrating sensors to monitor critical environmental factors and automating irrigation, ventilation, and lighting systems, the proposed system optimizes crop growth conditions in real-time, leading to increased yields and resource conservation. Moreover, its remote monitoring and management capabilities empower farmers to make data-driven decisions, ensuring sustainability and resilience in the face of changing environmental conditions. Overall, this research contributes to the advancement of smart agriculture practices, offering a scalable and efficient solution to address food security challenges and adapt to evolving agricultural landscapes

Design & Development of Smart Fire Safety System For e-Vehicles Using IOT

The project concept of An autonomous fire safety mechanism for EVs is an inventive way of considering a less cost-effective design and a more convenient application for e-vehicle safety. Day by day, the number of fatalities in electric vehicles increases. In this project, our main aim is to reduce the number of accidents with electric vehicles by making a smart fire safety system for electric vehicles. With the rapid adoption of electric vehicles (EVs), ensuring their safety, particularly concerning fire hazards, has become paramount. This abstract presents a conceptual framework for a sophisticated fire safety system tailored specifically for EVs. The proposed system integrates advanced technologies such as battery monitoring systems (BMS), temperature and smoke sensors, machine learning algorithms, active fire suppression mechanisms, emergency shutdown features, remote monitoring capabilities, user alerts, and compliance with safety standards. By combining these elements into a cohesive system, EV manufacturers can significantly mitigate the risks associated with fires in electric vehicles, enhancing overall safety for drivers, passengers, and emergency responders. With this technique, the fire extinguisher automatically puts out the fire when it detects flames or smoke coming from the vehicle's fire zones. Numerous factors can cause an electric vehicle to catch fire. The lithium-ion battery is the primary cause of flames in electric vehicles used within them. Installing an autonomous fire extinguishing system with Internet of Things (IoT) technology can reduce potential financial loss from a fire while also raising vehicle and human safety standards. Additionally, as soon as an accident is discovered, the nearby authorities or emergency services are notified, allowing them to get to you sooner. KEYWORDS:- Accident prevention, emergency alert, controller, fire incident, IOT technology, etc.

AI ENABLED WATER CONSERVATION FOR IRRIGATION USING IOT

This project introduces a smart irrigation system that utilizes Internet of Things (IoT) technology and machine learning algorithms to enhance water management in agriculture. The system employs a series of IoT sensors distributed across the field to consistently monitor key environmental factors such as soil moisture levels, temperature, humidity, and others. The collected data is analysed using machine learning Cat Boosting algorithm. This algorithm analyze the data to determine the optimal irrigation schedule based on crop water requirements, soil conditions, weather forecasts, and historical data. The system controls irrigation equipment such as pumps, valves, and sprinklers to deliver precise amounts of water to crops at the right time. Continuous feedback from the system allows for refinement of irrigation schedules, leading to improved water conservation, increased crop yield, and cost savings for farmers. This project discusses the benefits of such a system, including water conservation, increased crop yield, cost savings, environmental sustainability, and remote monitoring and control capabilities. Overall, the integration of IoT and machine learning technologies offers a powerful solution for sustainable agriculture, enabling data-driven decision-making processes to optimize water usage and maximize crop productivity. Keywords— Smart irrigation system, Internet of Things(IOT)technology, Water conservation, Environmental parameters, Soil moisture monitoring, Increased crop yield, Machine learning algorithms