Mobile Web Research Articles

Smart Irrigation System using IOT

The majority of nations rely heavily on agriculture to power their economies. A nation's growth is mostly dependent on agriculture, notably for people's livelihood and the production of food and other raw resources. The rise of agriculture is the main driver of a nation's economy. One type of irrigation system that is frequently utilized in the agricultural industry is the smart irrigation system. In areas of farming where water is scarce, irrigation is used. This intelligent irrigation system is designed with farmers in mind. In this paper, the proposed smart irrigation system uses NodeMCU ESP8266. This system reads the values using sensors and waters the plants based on those values. Also, the system will send notification as well as email to the owner for a certain interval of time depending on the owner specification. The owner can also remotely monitor values by using the mobile application or web application. It is very easy to implement, and will save manpower, leading to a cost-effective solution for efficient water management in agriculture. This system harnesses the power of a microcontroller to monitor and control irrigation process, ensuring optimal water usage.

An Equitable Communication and Dissemination Model for Linking Culturally Diverse Communities to COVID-19 Services.

In 2020, the Office of Minority Health (OMH) funded the establishment of the National COVID-19 Resiliency Network (NCRN) to engage priority communities that included African American, American Indian, Alaska Native, Asian, Hispanic, Native Hawaiian, and Pacific Islander persons to aggregate culturally relevant messages, education tools, and national data sources. A mobile application and Web site supported a culturally sensitive, equity-focused communication and dissemination initiative to link communities to COVID-19 clinical, social, and behavioral health services to address health determinants. (Am J Public Health. Published online ahead of print October 16, 2024:e1-e5. https://doi.org/10.2105/AJPH.2024.307813).

Using Digital Technology to Promote Patient Participation in the Rehabilitation Process in Hip Replacement: A Scoping Review.

The purpose of this scoping review was to identify and summarize how technology can promote patient participation in the rehabilitation process in hip replacement. We conducted a scoping review following the steps outlined by the Joanna Briggs Institute. The PRISMA Checklist (Preferred Reporting Items for Systematic reviews and Meta-Analyses) was utilized to systematically organize the gathered information. A thorough search of articles was performed on PubMed, Scopus, and CINAHL databases for all publications up to December 2022. Twenty articles were included in this study. Various technologies, such as mobile applications, Web sites, and platforms, offer interactive approaches to facilitate total hip replacement rehabilitation. The analyzed studies were based on the rehabilitation of total hip arthroplasty, which in most of them was developed in mobile applications and Web sites. The studies identified reflect trends in the application of digital health technologies to promote patient engagement in the rehabilitation process and provide risk monitoring and patient education.

IoT Smart Gardening on Herbal Plant and Analytic Virtualization Platform System

The Internet of Things (IoT) is an emerging technology developing recently. The technology of IoT allows users to communicate with controllers and sensors, which are widely used in the agricultural sector, such as farming and weather forecasts. Recently, it has been identified that traditional herbal home gardens are difficult to monitor where herbal plants are sensitive and need a proper monitor. This research has developed an IoT smart gardening for herbal plant systems that helps to analyze four herbal plant types: Aloe Barbadensis miller, Salvia Rosmarimus, Clinacanthus nutans, and Curcuma. The system was designed using a NodeMCU ESP32 controller, soil moisture sensor, and DHT11 temperature and humidity sensor. The collected data was monitored in real-time on the Blynk platform via mobile phones and web browsers on sunny and rainy days. The result shows that the highest temperature on a sunny day was captured at 30°C, and the lowest was 26°C. However, during a rainy day, the highest temperature captured is 28°C and the lowest was 24°C. The highest humidity percentage on a sunny day was 80%, and the lowest was 40%, while the highest humidity on a rainy day was 98%, and the lowest was 68%. The soil moisture data that captured the water content of the soil of each herbal plant on a sunny day has been lost due to high temperatures. However, each herbal plant barely lost its water content due to low temperature and high humidity percentage conditions. The comparison showed that temperature, humidity, and weather could affect the water content in the soil. This research has significantly improved the agriculture sector in monitoring herbal plants and moving from traditional planting with an IoT system concept.

Blended and Collaboration-Based Learning Management Model to Enhance Mobile Website User Experience (UX) Design Skills

This study presents a collaborative learning model designed to enhance mobile website design skills with a focus on user experience (UX). The model, assessed by nine experts across utility, feasibility, propriety, and accuracy, demonstrates high-quality scores, reflecting substantial agreement and effectiveness. Specific strengths include the model’s adaptability to the curriculum (4.56) and its applicability to university students (4.44). Areas for improvement, with mean values of 3.89, highlight opportunities for refinement, particularly in the appropriateness of learning processes and the precision of details within activities. Synthesizing steps from cooperative, blended, and Project-Based Learning, the model aligns with Stufflebeam’s framework, emphasizing high feasibility, utility, propriety, and accuracy. Comparative insights with previous research validate its strength across diverse educational contexts. The model’s success, proven through evaluation and refinement, carries significant implications for education by enhancing both technical skills and a holistic understanding of user experiences in mobile web design. Despite acknowledged limitations, the collaborative learning model invites continued exploration and adaptation, ensuring its relevance in the evolving landscape of educational practices.

49205 Development of and insights derived from SkinTracker, an integrated mobile health app and web portal enabling remote-friendly clinical research study of atopic dermatitis

49205 Development of and insights derived from SkinTracker, an integrated mobile health app and web portal enabling remote-friendly clinical research study of atopic dermatitis

Mobile Web Application for Durian Orchard Management and Geospatial Data Visualization Using Deep Learning

Durian, a globally popular fruit, is primarily exported by Thailand, making it the foremost contributor to the world market. Nevertheless, there remains a notable absence of a comprehensive platform or application catering to both consumer tourists and businesses seeking domestic purchases. Prior research has highlighted several shortcomings, notably the inability of existing applications to provide location-based search functionality or automatically identify durian plantation plots from digital photographs. Consequently, this study proposes the development of a mobile web application aimed at managing, processing, and visualizing geospatial data pertaining to orchards and durian plantations. Through the integration of mobile technology, geospatial technology, and machine learning, the research endeavours to address these deficiencies. The findings indicate promising results, particularly in the accurate classification of durian plantations using four machine learning algorithms: convolutional neural network (CNN), support vector machine (SVM), random forest, and k-nearest neighbor (KNN). Among these algorithms, CNN exhibited the highest accuracy, achieving a value of 95%, with precision, recall, and f-measure values of 95.55%, 94.44%, and 94.97%, respectively.

Electronic data capture in resource-limited settings using the lightweight clinical data acquisition and recording system

Our prototype system designed for clinical data acquisition and recording of studies is a novel electronic data capture (EDC) software for simple and lightweight data capture in clinical research. Existing software tools are either costly or suffer from very limited features. To overcome these shortcomings, we designed an EDC software together with a mobile client. We aimed at making it easy to set-up, modifiable, scalable and thereby facilitating research. We wrote the software in R using a modular approach and implemented existing data standards along with a meta data driven interface and database structure. The prototype is an adaptable open-source software, which can be installed locally or in the cloud without advanced IT-knowledge. A mobile web interface and progressive web app for mobile use and desktop computers is added. We show the software’s capability, by demonstrating four clinical studies with over 1600 participants and 679 variables per participant. We delineate a simple deployment approach for a server-installation and indicate further use-cases. The software is available under the MIT open-source license. Conclusively the software is versatile, easily deployable, highly modifiable, and extremely scalable for clinical studies. As an open-source R-software it is accessible, open to community-driven development and improvement in the future.

Effects of a Preoperative Patient-Controlled Analgesia Education Program Using Smart Learning in Laparoscopic Cholecystectomy Patients

BackgroundPatients undergoing laparoscopic cholecystectomy develop severe postoperative pain, and this acute pain often becomes chronic. ObjectivesThis study determines the effects of preoperative education on patient-controlled analgesia (PCA) through smart learning in patients using PCA after undergoing laparoscopic cholecystectomies. DesignWe conducted a quasi-experimental study with a nonequivalent control group pretest–posttest design. ParticipantsA total of 60 adult patients aged 20-65 years, admitted for laparoscopic cholecystectomy at a hospital in Korea, participated in a smart learning training program. MethodsThe concept of smart learning, which integrates learning content and solutions with the fourth industrial revolution using mobile devices such as smartphones and media tablets was applied in this study. This smart learning training program comprised three phases: (1) prebriefing, where patients accessed PCA knowledge via a program created by researchers and accessible using a mobile web device (e.g., smartphone or an iPad), with training covering pain characteristics, PCA effects, precautions, and usage methods; (2) simulation, where patients learned using PCA with a machine; (3) debriefing, where patients reviewed their knowledge and skills. ResultsComparing the pain between the experimental and control groups, the pain decreased significantly in both the experimental (Z = –4.40, p < .001) and control groups (Z = –4.41, p < .001), with no significant difference between groups (Z = –1.00, p = .319). Preoperative knowledge significantly increased in both the experimental (Z = –4.74, p < .001) and control groups (Z = –3.55, p < .001), with a significant difference between groups (Z = –6.05, p < .001). Total satisfaction with pain control was higher in the experimental group than in the control group. ConclusionsA structured educational program on PCA use is an effective nursing intervention. PCA educational programs using smart learning could help patients undergoing laparoscopic cholecystectomy understand postoperative pain, promote efficient PCA use, and enhance their satisfaction with pain control.

Mobile Health App and Web Platform (eDOL) for Medical Follow-Up of Patients With Chronic Pain: Cohort Study Involving the French eDOL National Cohort After 1 Year.

Chronic pain affects approximately 30% of the general population, severely degrades quality of life and professional life, and leads to additional health care costs. Moreover, the medical follow-up of patients with chronic pain remains complex and provides only fragmentary data on painful daily experiences. This situation makes the management of patients with chronic pain less than optimal and may partly explain the lack of effectiveness of current therapies. Real-life monitoring of subjective and objective markers of chronic pain using mobile health (mHealth) programs could better characterize patients, chronic pain, pain medications, and daily impact to help medical management. This cohort study aimed to assess the ability of our mHealth tool (eDOL) to collect extensive real-life medical data from chronic pain patients after 1 year of use. The data collected in this way would provide new epidemiological and pathophysiological data on chronic pain. A French national cohort of patients with chronic pain treated at 18 pain clinics has been established and followed up using mHealth tools. This cohort makes it possible to collect the determinants and repercussions of chronic pain and their evolutions in a real-life context, taking into account all environmental events likely to influence chronic pain. The patients were asked to complete several questionnaires, body schemes, and weekly meters, and were able to interact with a chatbot and use educational modules on chronic pain. Physicians could monitor their patients' progress in real time via an online platform. The cohort study included 1427 patients and analyzed 1178 patients. The eDOL tool was able to collect various sociodemographic data; specific data for characterizing pain disorders, including body scheme; data on comorbidities related to chronic pain and its psychological and overall impact on patients' quality of life; data on drug and nondrug therapeutics and their benefit-to-risk ratio; and medical or treatment history. Among the patients completing weekly meters, 49.4% (497/1007) continued to complete them after 3 months of follow-up, and the proportion stabilized at 39.3% (108/275) after 12 months of follow-up. Overall, despite a fairly high attrition rate over the follow-up period, the eDOL tool collected extensive data. This amount of data will increase over time and provide a significant volume of health data of interest for future research involving the epidemiology, care pathways, trajectories, medical management, sociodemographic characteristics, and other aspects of patients with chronic pain. This work demonstrates that the mHealth tool eDOL is able to generate a considerable volume of data concerning the determinants and repercussions of chronic pain and their evolutions in a real-life context. The eDOL tool can incorporate numerous parameters to ensure the detailed characterization of patients with chronic pain for future research and pain management. ClinicalTrials.gov NCT04880096; https://clinicaltrials.gov/ct2/show/NCT04880096.

Internet of Things (IoT) Based Home Automation Using ESP-32

Abstract: In the era of rapidly advancing technology, automated home systems have emerged as an innovative solution to enhance convenience, efficiency, and security in residential environments. This project focuses on the execution of a automated home system utilizing the ESP32 microcontroller, a versatile and powerful platform renowned for its connectivity capabilities. The system integrates various sensors, actuators, and communication protocols to enable seamless control and monitoring of household devices and appliances. The ESP32 serves as the central hub of the home automation network, facilitating communication between different components and providing connectivity to the internet for access and control. Sensors such as motion detectors, temperature and humidity sensors, and light sensors gather environmental data to inform automated actions and trigger responses based on predefined conditions. Actuators such as relays, servo motors, and LED lights enable the system to communicate with and control appliances, lights, locks, and other devices within the home. Through a user- friendly interface, residents can remotely monitor and manage their home environment, adjust settings, schedule tasks, and receive notifications/alerts via a mobile application or web interface

Handpose Estimation Based Learning Academy for Improving Typing Efficiency by Using Mobile Technologies

This paper focuses on enhancing touch-typing skills through state-of-the-art deep learning technologies. It has been demonstrated that writing posture can be assessed using integrated keyboard detection and hand pose estimation in real-time browser environments. A unique dataset was created for training the object detection model to recognize individual keyboards. The object detection models were trained using various architectures and then assessed for both inference time and accuracy. In addition, a hand pose estimation was implemented to precisely recognize 21 knuckle points of the hand and compute their exact positions. In order to implement object detection and hand pose estimation, a stream transmission of the keyboard and hand scene is required. For this purpose, server-client communication via a QR code connection is implemented to transfer the stream between two mobile devices. Based on these deep learning technologies, a mobile web app was developed that offers 170 learning courses for touch-typing training. With the help of the Typing Learning Academy prototype, a study was conducted as part of this paper to evaluate the usability and utility of the learning app. This research demonstrates the potential for enhancing the development of writing skills through touch typing through the utilization of advanced deep learning technologies.

An Analysis of the Domain Names Linked to the Important World Events using a Longitudinal Measurement Approach

Since the lack of sufficient security mechanisms, domain system (DNS) has become the main operational infrastructure for cyber intruders to launch cyber-attacks. Therefore, how to discover and block the potential malicious domains and its corresponding IP addresses fast and accurately has become a hot research area as it is one of the most important method in preventing unknown cyber-attacks. In this paper, we proposed an approach to detect malicious domains by analyzing massive mobile web traffic data. We used multiple features to classify, including the textual features and the traffic statistics features of domains and presented three typical classifiers to compare the classifying effect of each. Spark framework is leveraged to speed up the calculation of a large-scale DNS traffic. The efficiency of our system makes us believe the approach can help a lot in the field of network security. The new features are harder to be tampered with and can help determine whether a domain is malicious from a more comprehensive perspective. We evaluate MalPortrait on the passive DNS traffic collected from real-world large ISP networks.

A Regeneration Model for Mitigation Against Attacks on HTTP Servers for Mobile Wireless Networks

The widespread expansion of the internet has fueled a global surge in the utilization of various online transactions, with a significant portion of such services operating on mobile web platforms. Simultaneously, the deployment of innovative Mobile Ad Hoc Network (MANET) technologies and mobile applications has grown as solutions for diverse tasks. Unfortunately, this progress has attracted the attention of hackers, who continually devise new strategies to exploit the vulnerabilities inherent in mobile networks. This study aims to address the escalating challenges posed by cyber threats in the era of widespread internet expansion, particularly focusing on securing mobile web platforms against sophisticated attacks such as Structured Query Language Injection (SQLi) for web-based database solutions and Denial of Service (DoS/DDoS) for various applications. In response to the identified vulnerabilities, this paper proposes an HTTP regeneration (HReg) model that not only detects various cyber-attacks but also ensures the uninterrupted provision of critical services during such incidents. The model introduces an innovative regeneration algorithm capable of scanning both the connection channel and web application to detect attacks, creating survivable connections within the underlying TCP engine to replace compromised ones during an ongoing attack. In simulated environments using OMNeT++, where the server is subjected to attacks, the experimental results demonstrate the efficacy of the model. The response and performance metrics, including throughput (73%), delivery ratio (68.8%), delay (3s), and network load, showcase the model's ability to detect and neutralize attacks. A comparison with state-of-the-art approaches highlights the superior performance of the regeneration model, attributed to its additional survivability layer. While the regeneration model proves robust in simulated environments, its real-world application may encounter limitations. Future research should explore these limitations to enhance the practical applicability of the proposed model. The proposed HReg model's resilient performance under attack conditions ensures the survivability of web-based applications. This innovative approach offers practical implications for securing mobile web platforms, providing continuous delivery of critical services even in the face of persistent and evolving cyber threats. This research addresses a significant gap in existing efforts by not only focusing on attack detection but also emphasizing the development of a survivable TCP connection for HTTP servers during attacks. The introduced regeneration model stands out for its unique ability to maintain service continuity, showcasing originality in the approach to cybersecurity in the context of web-based applications.

IoT ENBALED SMART AQUARIUM FOR MONITORING THROUGH SENSORS

The development of an IoT-enabled smart aquarium system offering real-time monitoring and automated control of crucial environmental parameters. The core of the system lies in the ESP8266 microcontroller, facilitating seamless internet connectivity and data processing. Water quality assessment is achieved through a turbidity sensor, detecting suspended particles indicative of potential pollution. Environmental conditions are monitored using a DHT11 sensor, providing data on both temperature and humidity – essential factors for maintaining a healthy habitat for specific fish species. For automated lighting control mimicking natural day/night cycles and preventing excessive algae growth, a Light-Dependent Resistor (LDR) is implemented. The LDR adjusts aquarium lighting based on ambient light levels. Finally, a relay module controls the operation of a water pump, ensuring optimal water circulation and distribution of nutrients throughout the aquarium. While an I2C LCD connected to an Arduino Nano provides local data visualization within the aquarium setup, the ESP8266 transmits sensor readings to a user-friendly platform accessible remotely through a mobile application or web interface. This allows for convenient real-time monitoring of water quality, temperature, humidity, and lighting conditions. The system can be further programmed to generate alerts when critical parameters deviate from the ideal range, enabling proactive user intervention to maintain a healthy environment. This project demonstrates the potential of IoT technology to create a more automated, efficient, and user-friendly system for maintaining a thriving aquatic ecosystem. KEYWORDS: IoT (Internet of Things), Smart Aquarium, ESP8266, Turbidity Sensor, DHT11 Sensor, LDR(Light-Dependent Resistor), Relay Module, Water Pump, I2C LCD, Arduino Nano, Real-Time Monitoring, Automated Control, Remote Access, Mobile Application, Web Interface, Water Quality, Temperature, Humidity, Light Control, Aquatic Environment.

Toward Distributed Collaborative Rendering Service for Immersive Mobile Web

Toward Distributed Collaborative Rendering Service for Immersive Mobile Web

Visual Water: An Integration of App and Web to Understand Chemical Elements

This paper presents Visual Water: a novel inter- active application designed to augment chemical education and discovery. This mobile app and web version collectively provides an interactive environment for users to explore and understand chemical elements. Using vast databases and visual interfaces, Visual Water provides real-time information about various chem- ical elements, leading to a range of new, engaging and accessible learning tools for students, educators and enthusiasts. This paper provides a description of the development of this system, key features and potential impact of the Visual Water system in helping users better understand chemical reactions and their application in formal and informal educational spaces.

IoT Based Smart Parking System

Abstract: The IoT-based smart parking system represents an innovative approach to alleviating the prevalent issues of parking shortages and traffic congestion in modern cities. By integrating advanced hardware components and software applications, this system aims to enhance transportation efficiency and sustainability while improving commuter experiences. At its core lies the ESP32 microcontroller, orchestrating data processing from various sensors including parking and traffic IR sensors. These sensors enable real-time monitoring of parking availability and traffic flow, with data seamlessly transmitted to an LCD display for immediate visualization. Additionally, commuters can access this information remotely through a mobile application or web portal, empowering them to make informed travel decisions. The system's standout feature, the smart parking allocation system (SPAS), allows users to locate and reserve parking spaces in advance, effectively reducing the time spent searching for spots and consequently mitigating traffic congestion. Overall, the integration of IoT technology into transportation infrastructure offers a promising solution to the challenges of urban mobility, fostering efficiency, sustainability, and improved commuter satisfaction.

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.

Recent advances of ECG monitoring and webserver health monitoring applications: A review

In response to recent improvements in mobile technology and cloud computing-based real-time monitoring systems, numerous cloud-based healthcare services and devices have been developed. In the cloud system, it is feasible to collect and distribute medical data immediately from anywhere to medical specialists, and patients can receive network-based feedback. This paper reviews the most recent developments and trends in wearable technology and biomedical equipment (mostly ECG), as well as the development of a cloud-based solution for mobile device and web browser users. The system contains ECG enhancement, ECG quality grading, and ECG parameter extraction methods. A use case was utilized to showcase the system, in which a web server processed ECG data from a mobile phone in real-time. The system has been proved to be functional, accurate, and efficient. We review the primary principles underlying the operation of the ECG and its fundamental operation, as well as the recording and transmission paradigm for ECG signals. Detailed processes were also provided for the system components, their configuration, and the employed technologies. In conclusion, we examine the complete study and the project's future scope.