Wi-Fi Module Research Articles

IoT Based Health Monitoring System

Abstract: This paper presents a comprehensive system designed for continuous monitoring of patients' physiological parameters using Internet of Things (IoT) technology. In recent times, patient monitoring systems have gained significant traction among researchers and caregivers alike. The proposed system is adept at monitoring key physiological indicators such as pulse rate, body temperature, and heart rate from the patient's body. Data collected by the system is transmitted to an IoT cloud platform via a Wi-Fi module, where the patient's health status is securely stored. This setup allows medical professionals or authorized individuals to remotely monitor the patient's condition in real-time through the cloud server. The primary aim of this research is to provide efficient and effective healthcare facilities to patients, thereby enhancing overall patient care.

Aquaculture Monitoring and Control System Based On IOT

- This project offers a comprehensive solution for monitoring and regulating aquaculture conditions using cutting-edge Internet of Things (IoT) technology. By leveraging ESP8266 NodeMCU and Arduino UNO, alongside a range of sensors including the DS18B20 waterproof temperature sensor, water level sensor, and turbidity sensor, it ensures real-time monitoring of vital parameters in aquatic environments. Precise control of water circulation and management is made possible through the integration of motor drivers with submersible pumps. The Blynk App serves as the user-friendly control interface, granting remote access and management of water pumps. Meanwhile, sensor data is continuously collected and displayed on ThingSpeak, offering consumers valuable insights into the current state of their aquaculture setup. This project harnesses IoT technology to enhance aquaculture management efficiency and productivity, while also ensuring optimal environmental conditions for aquatic life. Its modular design enables scalability and customization, catering to various aquaculture configurations and settings. Key Words: Arduino UNO, Sensors, Motor Driver, Wifi Module, ThingSpeak, IOT, Aquaculture, Blynk App.

Wireless Home Security Using IOT

Abstract: We are living in the fourth industrial revolution. Our life is becoming more comfortable and smarter with the help of rapid upgrade of technology. Internet of things (IOT) is playing a massive role in this. One of the major sides of IOT is a smart Electrical Appliances. As we are in the era of never-ending growth of the internet and its application, smart home system or home automation system is highly increasing to provide comfort in life and improving the quality of life. In this project, we present an IOT based low cost smart electrical equipment automation system. This system is based on a web portal which controlled by an ESP32 WI-FI module. Also, a custom-made private web server is developed for maintaining the current states of electrical devices appliances.

Wireless Patient Monitoring System: IoT in Healthcare

Abstract: In the conventional medical field, diagnosis often requires numerous sensors and significant human effort, posing challenges for large-scale implementation due to the scarcity of medical professionals and infrastructure. Addressing this issue, this research proposes an IoT-based healthcare application aimed at continuous wireless monitoring of patients, utilizing both web and mobile platforms. The system's primary objective is to create a cost-effective solution for transmitting vital patient signs during emergencies. Sensors are employed to measure these signs, with data transmitted via a wireless network to the cloud for storage, facilitated by a Wi-Fi module connected to a controller. Cloud-based processing enables analysis of the data, with subsequent feedback provided remotely to medical professionals for further assessment. This remote viewing alleviates the burden on doctors while offering accurate patient health status updates. Additionally, in urgent situations, the system automatically alerts doctors, ensuring prompt attention to critical cases. These innovations promise to streamline healthcare delivery, enhancing patient care and reducing the strain on medical resources

Connected Robotics for Intelligent Surveillance: An IOT Approach

Abstract: The robot employs the HTTP Communication protocol, enabling users to access live video streaming from the OV2640 camera through a standard web browser. The web interface includes intuitive directional buttons for seamless control, allowing users to command the robot to move left, right, forward, and backward. The locomotion of the robot is achieved through two DC motors integrated with a Robot chassis and an L293D motor driver module. This paper presents the development and implementation of an innovative IoT-based Surveillance Robot designed for remote control, utilizing a Wi-Fi module and a smartphone interface. The portable espionage robot integrates cutting-edge technology, featuring an AI Thinker ESP32-CAM module with an ESP32-S processor, an OV2640 camera, motor drivers, and a robust chassis with two moving wheels. The ESP32-CAM allows the capture and storage of images on a microSD card, facilitating efficient data management. The real-time video feed is transmitted wirelessly to the user's smartphone over a Wi-Fi infrastructure, providing a livestreaming experience. This wireless surveillance robot finds applications in various scenarios such as war fields, disaster management, and beyond, showcasing its versatility in remote monitoring and reconnaissance tasks.

Ragi Pest Control

The rise of global population has put increasing pressure on the agriculture industry to meet the demand for food. However, the growing use of pesticides and insecticides in conventional farming practices has caused significant harm to the environment and human health. Thus, there is a growing interest in using sustainable agriculture practices that reduce the use of these harmful chemicals. One such practice is pest detection, which enables farmers to detect pests in their crops before they cause significant damage. In this context, this project aims to develop a pest detection system using IoT and Arduino. The system will be designed to detect pests in crops through a combination of sensors and machine learning algorithms. The system will consist of an Arduino microcontroller, soil moisture sensors, temperature and humidity sensors, infrared sensors or camera modules, and a Wi-Fi or Bluetooth module. The sensors will collect data on soil moisture levels, temperature, humidity, and pest activity. The data will be sent to a cloud-based server or database for analysis and visualization. The infrared sensor or camera module will detect the presence of pests in the crops. The system will use machine learning algorithms to distinguish between pests and other objects, such as leaves or debris. When pests are detected, the system will alert the farmer through a buzzer or LED connected to the Arduino board. The farmer can then take appropriate action, such as applying pesticide or removing infested plants. The pest detection system has the potential to reduce the use of harmful pesticides and insecticides in agriculture, as farmers will be able to identify pests before they cause significant damage. The system will also provide farmers with real-time information on pest activity, enabling them to take proactive measures to control pests and reduce crop damage. Additionally, the system can track and store the pest detection data over time, allowing farmers to monitor trends and patterns in pest activity. In conclusion, this project proposes the development of a pest detection system using IoT and Arduino that will enable farmers to monitor pest activity in their crops in real-time. The system has the potential to reduce the use of harmful chemicals in agriculture and improve crop yield while ensuring sustainable and environmentally friendly practices.

Smart and Intelligent Prayer System for Elderly Muslim with IoT

An assistive and monitoring prayer system has been designed to help elderly Muslims in performing their daily prayers. However, the system has several limitations in the aspect of efficiency, cost, size, complexity, security and usability. In this paper, several enhancements have been proposed to improve the existing smart prayer system including a better approach to retrieve exact prayer times, efficient way to retrieve current date/time, two different sensors used to detect the physical movements, smaller microcontroller with a built-in Wi-Fi module and the fabrication of PCB board. On the monitoring system, this project offers a more user’s friendly mobile application with several enhanced features. Reminder and authentication features are added to the mobile application for increased security and usability. The mobile application only allowed authorized or registered users to use the mobile application to view prayer performance of the elderly and send a prayer reminder to the elderly. The proposed system implements Internet of Things (IoT) technology, with a microcontroller communicating with the database through Wi-Fi, and data is modified before being displayed via the mobile application. Experimental testing shows a high level of support for the system's commercialization due to its ability to assist and monitor the elderly with their daily prayers. This new smart prayer system offers a more reliable assistive and monitoring system for elderly Muslim.

An IOT Navigating Device for Blind People

Blindness involves the loss of vision. Mobility and independence have long been challenges for blind and visually impaired people. This project proposes BlinDar, a smart Electronic Travel Aid (ETA) to improve their lives. Equipped with Internet of Things technology, BlinDar aims to help the visually impaired navigate indoor and outdoor spaces freely. This highly efficient, reliable, fast-responding, lightweight, low-power, and cost-effective device uses ultrasonic sensors to detect obstacles and potholes within 2 meters. GPS and an ESP32 Wi-Fi module share the user's location with the cloud. An MQ2 gas sensor detects fires along the path, while an RF transmitter/receiver helps locate a misplaced walking stick. The Arduino Mega2560 microcontroller, with its 54 digital I/O pins, provides easy interfacing between all components. . KeyWords: Blindness, Smart Stick, BlinDar, GPS, ESP8266, Internet of Things, RF Tx/Rx, MQ2.

Earthquake Detection and Alerting system using ANN and IOT

Earthquakes are one of the deadliest natural disasters. The damage caused by earthquakes is very destructive and the number of deaths are at a very large scale. The National Earthquake Information Centre now detects about 20,000 earthquakes around the globe each year, or approximately 55 per day. Many of the Earth’s earthquake zones coincide with areas of high population density. When a large earthquake occurs in such areas, the results can be catastrophic. The basic parameter to determine an earthquake before it was going to occur is the p wave or primary shock wave which is not destructive to infrastructure because it travels parallel to the propagation of seismic wave. A series of destructive waves that follows p waves are s waves or secondary waves which has a speed of about 60% that of p waves. The movement of the s wave is perpendicular to the direction of propagation of the seismic wave and hence it is extremely risky to infrastructures and cause a lot of damage. This research aims to build an earthquake detection and alerting system using a low-cost accelerometer, ESP32 Wi-Fi module, and ANN algorithm. The input earthquake data is taken by the accelerometer and with the help of the ESP32 Wi-Fi module the data is sent to a system that processes the ANN algorithm with Multi layered perceptron trained with stochastic gradient descent learning algorithm and detects whether an earthquake is going to occur or not and alerts users with ESP32 Wi-Fi module. Key Words: Artificial Neural Networks, ESP32, Accelerometer, Earthquake, Multi-layer perceptron, Sigmoid function, Stochastic Gradient Descent.

Automatic Medical Dispatcher with Dynamic Telemonitoring System Using IoT in Rural Areas

Accessing quality healthcare in rural areas can be challenging, often requiring individuals to travel long distances to nearby towns or cities. To address this issue, the Anytime Medical Machine (AMM) project was developed, leveraging Internet of Things (IoT) technology to provide on-the-spot medical consultations. AMM, a part of the Internet of Medical Things (IoMT), is designed for primary deployment in rural areas. Through AMM, individuals can receive consultations from qualified doctors without the need to travel. Doctors can prescribe medications and provide treatment recommendations, reducing both time and expenses associated with travel. The system collects patient data using sensors for temperature, heart rate, and blood oxygen saturation (SPO2), transmitting this information to doctors via a Wi-Fi module. The doctor-patient interaction is facilitated through secure video conferencing, requiring a unique username and password for each user to prevent misuse. After analyzing the patient's health parameters, the doctor prescribes appropriate medication, which is then dispensed through AMM. Patient information is securely uploaded to a cloud server for future reference, and the system is maintained by an administrator who refills medications as needed. Keywords- Rural Healthcare, Remote Consultation, IoT Healthcare, Medical IoT, Telemedicine, Remote Monitoring, Cloud-Based Healthcare, Healthcare Accessibility

Design and Development of MPPT Solar Charge Controller for Efficiency Enhancement in Solar PV System

Maximum power point tracking charge controller simulation is done in in proteus 8 professional software and validate using development of hardware model of MPPT solar charge controller for battery. Solar panel does not generate enough voltage all time.Panel can generate 12V to 21V according to solar radiation and environmental condition. For charging of 12V battery minimum required voltage is 14.6V. So, the Maximum power point tracking controller will give extra voltage into usable current, so battery can charge in short period.Development of MPPT Solar Charge Controller is done using ardunionano controller board, ACS 712 current sensor, mosfet driver IR2102, dc-dc buck converter using mosfet, inductor, capacitor and wifi module for data collection, LCD display,LED indication for battery status of charging. Battery charge using three state charging say buck, absorption and float mode. Performance analysis done using simulation model using LCD display status, where first column of display shows PV voltage, PV current and PV power. Second column of display reads battery voltage, status of battery. Third column reads pwm duty cycle in percentage and load status. Voltage level is shown by LED. Yellow LED indicates a fully charged battery, green indicates a regular voltage, and red indicates a low voltage. Performance study was done and the model was validated using a hardware model. All parameters and functionalities for measured solar panel voltage, current, power, battery voltage, charger state, SOC, PWM duty cycle, and load status are shown in real-time on the hardware display. Three-step battery charging algorithms are utilized, specifically the buck stage, absorption stage, and float stage. The battery peak charges to 90% SOC and the battery current is almost steady during the buck stage. In the buck stage, the battery voltage rises to a peak of 14.4V. Absorption occurs in the second phase when the battery's voltage remains constant and its current decreases. Battery current will be very low in the final phase, almost like trickle current.

Diagnostic Machine safety with IoT Monitoring

After analyzing the current situation of safety of diagnostic machine, it is realized that there is a need to develop a system that could inform the users about various faults in the machine, thereby protecting from severe damages. The proposed project presents a novel system that monitors machines and detects faults. This project is a model for a real time monitoring system using contemporary technologies like Internet of Things (IoT)-based sensors that are promising for efficient machine monitoring. The proposed project is a model for a real-time monitoring system using IoT-based sensors accessible via webpages. The system involves connecting the machine to two sensors: a current sensor to continuously monitor current flow and a temperature sensor to gauge heat production. These sensors provide input signals to an Arduino controller, which processes them into real values based on sensor output electrical signals. Subsequently, these values are translated from machine-level language to low-level language and displayed on an LCD. A Wi-Fi module facilitates transmission of these values over the internet to cloud storage. In this system "ThingSpeak" is opted as cloud platform, offering data collection. This platform stores the data, enabling access to values worldwide via webpages. It also generates graphs depicting sensor values over time. Key Words: Fault detection, Machinery monitoring, Arduino controller, Cloud storage, Real-time monitoring

IoT Based Smart Kitchen Using Fuzzy Intelligence and Signal Processing

Kitchen management remains a challenging task, despite many technological advancements. As a result, it's clearly time for us to get the kitchen work done with ease. The ingenious Kitchen uses the Internet of Things for design and features a mobile application as a user interface. Smart Kitchen includes a grocery management system, a fuzzy system for adaptive cooking and signal processing for identifying the cooker whistle. In the grocery management system, an ultra-sonic sensor measures the inventory level and using a Wi-Fi module, data updates the value of a particular key in the cloud. Users can see the amount of item left, be notified of shortage, and get the shopping list using the mobile application.

IoT Based Smart Kitchen Using Fuzzy Intelligence and Signal Processing

Kitchen management remains a challenging task, despite many technological advancements. As a result, it's clearly time for us to get the kitchen work done with ease. The ingenious Kitchen uses the Internet of Things for design and features a mobile application as a user interface. Smart Kitchen includes a grocery management system, a fuzzy system for adaptive cooking and signal processing for identifying the cooker whistle. In the grocery management system, an ultra-sonic sensor measures the inventory level and using a Wi-Fi module, data updates the value of a particular key in the cloud. Users can see the amount of item left, be notified of shortage, and get the shopping list using the mobile application.

Solar Based Smart Irrigation System

This article presents a novel mobile application utilizing modern information and communication technology for agriculture, centered on the Internet of Things (IoT), embedded systems, and unmanned aerial vehicles (UAVs). The proposed agricultural monitoring system, employing Arduino microcontroller boards, Wi-Fi modules, and environmental sensors, facilitates data collection on temperature, humidity, and soil moisture. UAVs gather environmental data across the farm, enabling cloud-based computation of irrigation requirements for each region. Additionally, the system remotely monitors farm conditions, including water needs, through an Android mobile application, aiding farmers. Results indicate the effectiveness of our IoT-based system in reducing unnecessary water irrigation, aligning with smart agriculture goals. As the global population burgeons, the demand for increased food production escalates. Traditional farming methods necessitate extensive labor and water usage. This paper introduces a compact automated agricultural system prototype. Employing directional guides, the hardware enables precise processes such as seeding, humidity sensing, and watering, ensuring optimal crop growth and economic viability. From seed sowing to watering, the entire system operates autonomously. Testing involved integrating the hardware with a scaled-down plantation model to validate functionality. Keywords: Solar Panels, Arduino Uno, Soil Moisture Sensor, Relay, Lithium Battery, PIR Sensor, TP4058 Module, Gear Motor, LDR Sensor, Submersible Pump, Buzzer.

A low-cost Wi-Fi smart home socket using internet of things

With the emergence of smart home appliances, traditional power sockets are becoming less compatible with modern living styles. Furthermore, modern commercialized sockets are expensive and unaffordable. This project presents the development of a low-cost Wi-Fi smart home socket using internet of things (IoT) technology that is user-friendly for smartphone users to control home appliances. Smart home socket devices can turn on and off power outlets automatically from any location if they are linked to the internet and providing the user with more convenience and energy savings. This project uses a node microcontroller unit (NodeMCU) Wi-Fi module (ESP8266) as the main microcontroller unit to connect to a cloud platform. It also uses a mobile phone application to send instructions to the microcontroller for turning on and off household appliances remotely through a smart socket. The switching mechanism is monitored and controlled through the Blynk platform. A 4-channel relay module is used to transition DC current loads to AC current loads in order to activate switching processes. According to the study’s findings, the Wi-Fi smart home socket system is able to save on excessive usage of electrical appliances while also increasing electrical appliance safety.

IoT-based health information system using MitApp for abnormal electrocardiogram signal monitoring

Information systems are currently developing very rapidly, and this is inseparable from the role of internet of things (IoT) technology, especially in the world of telemedicine. MitApp is an open-source application that can be used to monitor electrocardiogram (ECG) signals in real-time. The aim of this study is to develop an IoT-based ECG signal monitoring system that utilizes the MitApp application to detect abnormal ECG signals that are characterized by symptoms of cardiac arrhythmias. To process ECG signal data obtained from lead electrode results, the research method utilizes Arduino Uno as a microcontroller. The result is then displayed on the thin film transistor (TFT) layer using the Nextion module. The ESP32 module is used as a Wi-Fi module to send data to the MitApp app on a smartphone. The results showed that the results of the comparison test of ECG signal module data with ECG simulator tools with beats per minute values of 60, 80, 100, 120, and 140 obtained an error rate of 0.05. Based on these results, there is potential to develop this feature and integrate the system with the patient management system to improve the effectiveness of remote monitoring.

Performance analysis of photovoltaic panel using machine learning method

Demand for energy is increasing as the world’s population grows, fossil fuels deplete on a daily basis, and climate conditions change. Renewable energy is more important than ever. Solar energy is the most accessible and cost-effective renewable energy source available today. Photovoltaic (PV) cells are the most promising way to convert solar energy into electricity. Wind speed, ambient temperature, incident radiation rate, and dust deposition are some of the internal and external variables that affect photovoltaic panel performance. Unwanted heat from the sun’s rays raises panel temperatures, reduces the amount of energy that solar cells can produce, and lowers conversion efficiency. Solar panels must be adequately cooled. The current research is focused on improving photovoltaic panel performance. The experimental system includes a fully automated photovoltaic panel, a microcontroller (NodeMCU8266), a DC pump, voltage and temperature sensors. The experiment was carried out with and without cooling of the PV panel. The findings suggest that keeping PV panel temperatures close to ambient temperatures improves performance. The Wi-Fi module collects real-time data on PV panel temperature, irradiation, ambient temperature, water temperature, and PV panel power output. The collected data was analyzed using machine learning. The PV panel’s performance was analyzed using the linear regression method.

Alcohol and Drowsiness Detection in Smart Transportation System

Abstract: Drunk driving is a major contributing factor in the rising number of unintentional deaths that occur in the modern day. Drunk driving is an extremely dangerous practice that can result in car accidents and injuries on the road. Numerous preventative actions had been implemented, including license suspension, fines, and the seizure of motor vehicle permits. Notwithstanding the numerous measures implemented, the proportion of collisions caused by intoxicated drivers continues to rise. The purpose of this module is to shield people from needless deaths brought on by intoxicated and sleep-deprived drivers in traffic accidents. This module includes a Wi-Fi module and an alcohol detection sensor (MQ-3) based on the Raspberry Pi model 3. We are incorporating an automated drowsy driver monitoring and accident prevention system as part of the design of our major project.

Iot-based flood monitoring and early warning system for proactive security measures

The project introduces an IoT-based flood monitoring system using Arduino Uno, soil moisture sensor, LCD, buzzer, Wi-Fi module (e.g., ESP8266), and ThingSpeak cloud platform. It monitors soil moisture levels, issuing alerts when a predefined threshold indicative of potential flooding is reached. The soil moisture sensor measures moisture content and is displayed on an LCD screen for real-time monitoring. A buzzer provides audible alerts for high moisture levels. A Wi-Fi module enables wireless communication, transmitting data to the ThingSpeak cloud platform for centralized storage and analysis. Users access real-time and historical data through ThingSpeak's interface. The system configures the Wi-Fi module to connect to a network, securely sending data using a unique API key. ThingSpeak enables visualization, analysis, and alert setup based on soil moisture levels, offering a comprehensive flood monitoring solution. This cost-effective system, integrating open-source hardware and cloud platforms, enhances accessibility and deployment ease and is suitable for various environmental monitoring applications.