ThingSpeak Platform Research Articles

Development and Modeling of the Internet of Things Network for Patients IT Diagnostics

The work of a network that implements algorithms for IT diagnostics of neurological diseases based on the Internet of Things technology has been developed and modeled. The network includes a smartphone, a platform, a neural network, and applications. First, the voices of sick patients are entered from the smartphone to train the neural network, and then the examined patients for IT diagnostics. Data is transferred between the smartphone and the platform (ThingSpeak) via the MQTT protocol. The smartphone’s mobile application extracts the voice functions of the examined patients and records them on the Internet of Things network platform. Recognition is performed using the trained neural network. The structure and algorithm of the ThingSpeak platform are presented. IT diagnostics data are displayed in the application on the smartphone. The patient data used in the study are taken from the ADReSS 2020 Challenge program, which contains speech data of patients with Alzheimer’s disease and healthy people.

PENGEMBANGAN SISTEM IOT UNTUK PEMANTAUAN KESEHATAN DOMBA DENGAN ALGORITMA C4.5 BERBASIS THINGSPEAK

Effective livestock health monitoring is one of the primary challenges in modern farming, especially in detecting early health disorders in sheep. This research aims to develop a sheep health monitoring system based on the Internet of Things (IoT) using the C4.5 algorithm and the ThingSpeak platform. The system collects vital sheep data such as body temperature, heart rate, sound, and physical activity in real time through sensors and microphones connected to IoT devices. The data is then transmitted to the ThingSpeak platform for analysis and storage. The C4.5 algorithm is used to build a decision model capable of classifying the health conditions of sheep based on collected parameters such as temperature, heart rate, and respiration. The processed data results are displayed in the form of graphs and warning notifications on the ThingSpeak platform, allowing farmers to monitor livestock health easily and responsively. The accuracy test yielded a 90% accuracy rate using a confusion matrix with a data sampling split of 80% for training data and 20% for testing data. This indicates that the system has a high level of accuracy in detecting sheep health conditions. Consequently, the system has the potential to assist farmers in improving the efficiency of livestock health monitoring automatically and in real time. Moreover, the application of IoT technology and the C4.5 algorithm in the livestock sector is expected to provide innovative solutions to support productivity and animal welfare.

A garment-integrated textile stitch-based strain sensor device, IoT-Enabled for enhanced wearable sportswear applications

The field of wearable technology is undergoing a transformative shift with the integration of IoT-enabled, AI-assisted sensors into sportswear, bringing sophisticated performance analysis within reach for a broader audience. Despite advancements in stitch strain sensors, there is a significant research gap in developing high-performance, stable, and IoT-connected complete stitch strain wearable sensor systems. These advanced sensors are essential for enhancing wearable technology and meeting the growing demands for improved user experience and functionality. In this study by utilizing conductive sewing threads and innovative stitching techniques, we fabricated textile strain sensors that offer high sensitivity, flexibility, and durability. Extensive calibration tests revealed strong linear relationships between resistance and tensile strain, with highest sensor sensitivity (2.08 Ω/mm during loading and 2.72 Ω/mm during unloading). These sensors demonstrate reliable performance with minimal hysteresis, particularly in shorter lengths. The wearable sportswear device, tested in gym environments, demonstrated the sensor's capability to provide real-time feedback on knee flexing/bending and body movement during exercise. This functionality supports injury prevention and enhances workout efficiency. The sensor system is IoT-enabled, powered by an ESP8266 microcontroller, facilitating data transmission to the Thing Speak platform for advanced analysis and remote monitoring. This comprehensive approach not only advances wearable technology but also opens new avenues for remote health monitoring and smart e-textiles, contributing significantly to the fields of sports science and fitness technology.

IoT Architecture for Vehicle Pollutant Gas Emission Monitoring and Validation through Machine Learning

This study proposes an IoT architecture for monitoring emissions of polluting gases in vehicles, in response to the growing concern about air pollution and global warming. The architecture is based on a node equipped with DHT22, MQ9, and MQ135 sensors to capture temperature, humidity, and gas emissions, respectively. This node effectively communicates through the LTE network to send the data to the ThingSpeak platform. An analysis of CO2, CO, and CH4 pollution levels is conducted using the collected data. These data are validated through the technical review of a test vehicle. Subsequently, an Artificial Neural Network (ANN) is trained using a specific database of CO2 emissions from vehicles in Canada. As a result, a high R2 of 99.2% is achieved, along with low values of RMSE and MSE, indicating that the model is making accurate predictions and fits well to the training data. The ANN aims to predict CO2 emissions and verify CO2 data from the IoT network. The architecture demonstrates its capability for real-time monitoring and its potential to contribute to pollution reduction.

IoT Enhanced Smart Aeroponics System

Abstract: Recently, urban farming has gained popularity as people become more aware of the quality of their food. Aeroponics is an urban farming technique that uses air as its growing medium. Compared to traditional farming and hydroponics, aeroponics offers a significant reduction in water usage while increasing yield. This study introduces an aeroponics system integrated with Internet of Things (IoT) technology for real-time and automated monitoring. The system maintains the pH level of the nutrient solution using a pH sensor and monitors temperature and humidity with an A DHT sensor. A peristaltic pump, coupled with a ultrasonic sensor, regulates the tank's liquid level. All these processes are accurately timed and shared on the ThingSpeak Platform and Blynk App for user interaction, facilitated by the ESP 32 Micro controller

EMPOWERING FUTURE BUILDINGS WITH IOT AND 5G TECHNOLOGY

In the realm of smart buildings, the convergence of Internet of Things (IoT) and 5G technology has unlocked unprecedented opportunities for enhanced building management, efficiency, and sustainability. This project endeavors to harness the potential of IoT sensors and actuators, including LDR, DHT, gas and smoke sensors, PIR, HVAC, lights, servo motors, buzzers, and displays, integrated with Arduino Nano microcontrollers. These microcontrollers serve as end nodes, orchestrating sensor data collection and actuator control. The data collected is transmitted to a gateway device, an ESP32 microcontroller, which interfaces with a wireless Wi-Fi router for connectivity. Leveraging the ThingSpeak platform, the gateway transmits data to the cloud, facilitating real-time monitoring and analysis. This system promises to revolutionize building management by enabling remote monitoring, predictive maintenance, energy optimization, and enhanced occupant comfort and safety. Through rigorous testing and optimization, this project aims to demonstrate the feasibility and effectiveness of IoT and 5G technology integration in empowering future building infrastructures. Keywords— Heating, Ventilation, and Air Conditioning (HVAC),Light Dependent Resistor (LDR), (Digital Humidity and Temperature(DHT)

Women Safety System Using ARM Micro controller and Arduino

This abstract presents a comprehensive women's safety device leveraging modern technology for effective emergency response. The device integrates various components including the Neo6M GPS module, module, Arduino Nano microcontroller, push button for emergency alert, , pulse oximeter MAX30100, and temperature sensor LM35. The Arduino Nano serves as the central processing unit, orchestrating data collection, processing, and transmission.Incorporating a Python Flask WebApp and ThingSpeak platform, the device ensures real-time alerts and cloud storage capabilities. The Flask WebApp facilitates user interaction and customization, enabling users to set preferences and receive notifications. ThingSpeak enables seamless data logging and retrieval from the cloud, ensuring data integrity and accessibility.Upon activation, the device initiates location tracking via the GPS module and triggers an emergency alert through the module. Simultaneously, vital signs such as pulse rate and temperature are monitored using the MAX30100 pulse oximeter and LM35 temperature sensor respectively. These data are transmitted to the Flask WebApp and ThingSpeak platform for real-time monitoring and alert generation.This innovative solution aims to provide women with a reliable, multifunctional safety device capable of prompt emergency response and continuous monitoring, ultimately enhancing their safety and well-being in various settings. Key Words: Safety system, ARM micro-controller, Arduino, GPS, Alerting, Web-Application.

SMART BUILDING CONTROL THROUGH INTERNET OF THINGS/MACHINE-TO-MACHINE DEVICE MANAGEMENT VIA HETEROGENEOUS WIRELESS NETWORKS

The increasing use of wireless communication technologies has made IoT and machine-to-machine (M2M) connections crucial to a wide range of industries. Smart buildings are an excellent example of an IoT/M2M application. Issues such as a lack of a convergent solution, high costs, restricted wireless transmission range, user-unfriendly interfaces, and the inability to integrate IoT and M2M technologies. Consequently, this article suggests a more effective method for for Machine-to-Machine (M2M) and The Internet of things smart building systems: heterogeneous wireless networks that include both WSNs and MCNs. The suggested system is a cost-effective embedded system that includes Several functional actuators, sensors, and modules for Arduino and NodeMCU, ESP32CAM boards allow for data collection and control across a variety of heterogeneous communication methods like Bluetooth, Wi-Fi, and global system for Mobile (GSM). All collected data is issued to the ThingSpeak platform, allowing the building system to be monitored via the Thing Speak webpage or the UR Smarthome app. One of the researchs most important results is that it can give the server precise information with extremely little delay, allowing users to quickly control and monitor remotely the proposed system that consists of several innovative services names indoor applications(fire detection and gas leakage detection, intrusion alarm by ultrasonic, medicine reminder by RTC module, light and fan control, smart door) and outdoor applications(garden irrigation by soil moisture and DHT11, air quality, smart parking ). Our free mobile application UR Smarthome, which is a local server that controls and monitors the building from a distance, is used to plan and execute all of these services that provides remote control and monitor of the building via Bluetooth/ Cellular networks and Wi-Fi access. With its customizable features, this IoT/M2M smart building system can be tailored to the demands of its users, enhancing their quality of life and safety while using less energy. In addition, identifying and lowering risks, it assists in preventing the loss of resources and human lives. KEYWORDS— Converged networks, heterogeneous networks (HetNets), Internet of Things (IoT)/machine-to-machine (M2M), mobile application, mobile cellular networks (MCNs), smart building, wireless sensor networks (WSNs)

Design of Incu Analyzer for IoT-Based Baby Incubator Calibration

An incubator analyzer, serving as a calibration tool, is utilized to measure diverse parameters such as temperature, mattress temperature, humidity, airflow, and noise in infant incubators. The present study focuses on the development of the Design Incubator Analyzer for IoT (Mattress Temperature and Humidity) with LCD and ThingSpeak display, specifically designed for calibrating baby incubators. The primary objective is to design and develop an Incubator Analyzer as a calibration device for assessing various parameters in infant incubators, encompassing temperature, mattress temperature, humidity, airflow, and noise. The design of this calibration device incorporates a Thermocouple Type-K sensor for baby incubator mattress temperature parameters, a DHT22 sensor for humidity parameters, and an ESP32 microcontroller. The ESP32 processes data from the Thermocouple Type-K and DHT22 sensors to generate values for mattress temperature (TM) and humidity (RH), which are then displayed on LCD and ThingSpeak displays. The device underwent rigorous testing against an established measuring device, the INCU II. In the study, the TM parameter or mattress temperature exhibited the smallest error of -0.0140% at 35°C and the largest error of 0.0584% at 36°C. Concerning the humidity parameter, the largest error was 0.0570% at 32°C, while the smallest error was 0.0207% at 35°C. Overall, the Incubator Analyzer Design for IoT-Based Baby Incubator Calibration device, or IoT-based Incubator Analyzer, demonstrates potential usability following the planning and execution phases, including a thorough review of existing literature. To enhance user experience during the calibration process, an IoT system was developed for data transmission over Wi-Fi, presenting results on the ThingSpeak platform in real-time

IoT-Based Rainfall Monitoring System for Chili Farming Land

This research focuses on the design and implementation of a rainfall monitoring system for chili pepper farms using Internet of Things (IoT) technology. The rainfall monitoring system consists of a transmitter system, a receiver system, the Thingspeak platform as a database, and a weather station application that can be accessed via a mobile device. The weather station application is built using the MIT App Inventor platform. In the testing phase, the system successfully collected data from two sensors used, namely the rainfall intensity sensor and the raindrop sensor. The test results showed that the data obtained from the rainfall intensity sensor was 0.25 inches and the raindrop sensor was 1. This result shows that there was no rain during the test. This rain intensity and raindrop data can provide farmers with an overview of the weather conditions in the chili pepper farm. So, with this rainfall monitoring system, farmers can monitor the condition of their agricultural land in real-time. The collected data can help farmers to care for chili pepper plants more effectively and adapt to environmental changes. In addition, this system is expected to increase the productivity of chili pepper farming because it uses a more precise and responsive approach to changes in environmental conditions on the chili pepper farm.

IoT-based real-time monitoring of supercapacitors used in electric vehicles

The research focuses on the condition monitoring of supercapacitor systems installed in the electric vehicle that acts as the power source for the vehicle when it needs high power for a lesser time. This research work aims to introduce the Internet of Things in battery systems along with supercapacitors aiming to increase the efficiency and reliability of the system. In the corresponding research work, IoT-based condition monitoring for a 48V supercapacitor for an electric vehicle is implemented, the supercapacitor is charged using the phase shift full bridge DC-DC converter after that the data is transferred from the Matlab Simulink to the Thingspeak platform to enable IoT based application Hence establishing the real-time monitoring of super- capacitor. Introducing the Internet of Things will help researchers in a deeper understanding of supercapacitor behavior in a real-time environment as well as the real-time data will be helpful in the protection of the system.

Comprehensive Design Analysis of Economical E‐Bike Charger with IoT‐Empowered System for Real‐Time Parameter Monitoring

The demand for electrically powered transportation is increasing exponentially due to high fuel prices and global environmental issues. The use of electric bikes is increasing rapidly within urban mobility. The current E‐bike chargers are expensive and fail to provide proper authentication, real‐time monitoring, parameter analysis, health maintenance alerts, etc. To meet future demand, the paper presents detailed design procedures and experimental analysis of smart, user‐friendly, economical, and green charging solutions for electric bikes. The research provides an IoT‐based cheap charging facility for different workplaces, organizations, and highway rest areas. Real‐time pricing and parameters are sensed using an IoT module and observed online with a monitoring interface via the ThingSpeak platform. The parameters are visualized using both simulation and hardware analysis. The detailed power converter and high‐frequency transformer design procedure with mathematical equations are presented in the article. The proposed design improves reliability, security, timely maintenance, and system health conditions. The research provides economical off‐board charging stations with smart interfaces that can be accessed by users or service providers through the data cloud. The low‐cost smart charging stations promote the use of electric vehicles and decrease the charging anxiety of users. The proposed scheme reduces emissions problems, improves the air quality index, and facilitates people with affordable and reliable transportation.

Green Energy Production Aid Spider Robot: An Innovative Approach for Waste Separation Using Robotic Technology Powered with IoT

Growing worldwide waste management challenges have prompted research efforts for sustainable solutions, especially in green energy technologies. This study highlights robotics’ critical role in modern waste management by putting forth a novel strategy incorporating cutting‐edge technologies. The research aims to create an effective waste monitoring system to measure the amounts of methane gas and organic waste at disposal sites. The main component of this method is a 3D‐printed spider robot driven by a servo motor with solid and flexible claws. This robotic system communicates gas level readings to the ThingSpeak platform on the Internet of Things, while it navigates waste disposal sites autonomously. The study also investigates how to develop robotic systems more economically and efficiently by using a portable, low‐power microcontroller, such as the Arduino Nano. The study uses a rigorous testing methodology to assess the performance and viability of the microcontroller and robotic system. The results are expected to emphasise a practical and economic development approach while offering insightful information on developing sustainable waste management technologies.

Monitoring and Control System for Electrical Energy Consumption Based on The Internet of Things

Electrical energy consumption in the household sector is frequently uncontrolled, particularly when electronic devices are used. That is the tendency of users to forget to turn off or unplug the electronic device from the socket. In that case, modifications are made to the socket so that it can be controlled remotely via a smartphone. The hardware design uses the NodeMCU ESP8266 as a microcontroller combined with the PZEM-004T sensor module for electrical magnitude. Relay modules are used to secure the circuit in cases of higher loads. So that in the system, all three sockets can be monitored simultaneously. While the software design uses MIT App Inventor and Thingspeak Platform as servers. The data is saved in CSV format, which can be converted to Google Sheets or Microsoft Excel. Data is stored in the form of the name of the electronic equipment, the time of use, as well as voltage, current, power, cos phi, and electrical energy used. So that users can regulate the use of electrical equipment at home and reduce the waste of electrical energy. The test results showed the average faults for voltage, current, power, cos phi, and energy were 0.06%, 5.32%, 5.67%, 0.7%, and 7%, respectively.

Model Prototype of a Solar Tracking System Supplying Electrical Power for Sensors Used in a Natural Disaster Monitoring System

Sun is the universe main source of energy. This energy can be converted to useful electrical energy by the utilization of solar panels or photovoltaic (PV) during the daytime with the presence of suitable sun irradiation level. It is important that during the energy conversion process, suitable methods are employed to extract as much energy as possible from the PV panels. Some issues affecting PV performance are the accuracy of the panel’s orientation and tilt angle with respect to the sun position. This causes PV to operate with lower efficiency, thus generating low output power compared to its rated capacity. One of the approaches to this issue is to constantly maintain a perpendicular profile of PV panels towards the sun direction as to capture the sun energy as much as possible, thus increases the PV efficiency. To do so, the conversion system needs a tracking mechanism to help the solar panel to constantly follow the sun’s direction, enhancing the achievement of higher PV efficiency. For this reason, a model prototype has been designed and developed which consists of a dual-axis solar tracking system, with two axis movement direction from east to west and north to south by utilizing the Light Dependent Resistors (LDRs) sensors. This tracking system is developed using Arduino UNO as the microcontroller that allows the PV panel to move optimally towards the high intensity of sunlight via four LDRs and two servo motors. To analyze the PV tracker’s performance, a monitoring system is implemented using ThingSpeak platform as the Internet of Things (IoT) platform interfacing with the controller Node MCU ESP8266. This monitoring system records the data of the PV energy parameters from the sensors’ output employed in this prototype. To assess the system efficacy, the dual-axis tracking system is compared with the single-axis tracking system. The result showed that the dual-axis tracker has efficiencies of 68.46% which is higher compared to that’s of the single-axis tracker. Implementation of the real-time monitoring system has shown a practical and handy way to analyze and monitor the solar tracking system’s performance.

MACHINE LEARNING SMART PACKAGING PENGIRIMAN TELUR AYAM BERBASIS INTERNET oF THINGS (IoT) MENGGUNAKAN ALGORITMA C.45 DENGAN PLATFORM THINGSPEAK

In the era of Industry 4.0, the integration of Machine Learning and the Internet of Things (IoT) plays a crucial role in enhancing the efficiency and safety of logistics processes. This research aims to develop a Smart Packaging system for the shipment of chicken eggs utilizing Machine Learning with the C.45 algorithm and IoT-based on the ThingSpeak platform. The system integrates Node-MCU (ESP8266) as the central processing unit, the DHT11 sensor to monitor temperature and humidity within the packaging, the Vibration Sensor SW-420 to detect potential damage to eggs during shipment, and the Unblock Neo6m-V2 GPS Module for real-time location tracking. The C.45 algorithm is employed to process data and make intelligent decisions regarding the condition of the eggs and the shipping environment. Sensor data is collected and transmitted to the ThingSpeak platform through the Wi-Fi connection provided by Node-MCU. The C.45 algorithm is applied to analyze the data, provide predictions regarding egg conditions, and make decisions for further actions during the shipping process. Experiments were conducted to evaluate the system's accuracy using RapidMiner software. The results indicate that the system is capable of predicting egg conditions with a high level of accuracy, enabling responsive actions to situations that may affect egg quality during shipment. The implementation of Machine Learning and IoT technologies in this chicken-egg shipping system is expected to enhance the quality of delivered products, optimize logistical processes, and provide an intelligent solution to ensure the sustainability of the food product supply chain.

Monitoring Bpm And Body Temperature Based Internet Of Things (Iot) Thingspeak Platform

The development of technology in the health sector as well as modern science and technology, both in the treatment of patients and in technology that facilitates the development of health science itself, has experienced very rapid development. Various operational processes in hospitals have now undergone many developments, namely by replacing human performance with automated systems. One of them is an automatic system for monitoring heart rate and human body temperature. Heart rate and body temperature are two important parameters used by medical personnel to determine a person's physical health and mental condition. Because if the heart rate and body temperature are not normal, more effort is needed to prevent unwanted things from happening. Heart disease is known as one of the highest causes of death in the world, while body temperature can detect something that is in the body. In order for monitoring tools to be more practical and efficient to use, tools have been made with real time monitoring and can be monitored remotely. The design of this tool uses the Max30102 as a BPM sensor which has a digital output, also the Mlx90614 sensor as a temperature sensor which has a digital output, then processes it on and displays the value on the I2C LCD and sends data to the Thingspeak platform assisted by ESP32 as a wifi module. BPM has the smallest error of 0.23% and the largest of 2.11% while Temperature has the smallest error of 0.59% and the largest of 3.37%. The results of this study can be implemented on patient monitors to increase the efficiency of the remote monitoring system

Building an IoT temperature and humidity forecasting model based on long short-term memory (LSTM) with improved whale optimization algorithm

In particular, predicting the temperature and humidity information plays a crucial role in plantation, estimating rainfalls and climate change, and predicting air quality via specified geographical regions. The temperature and humidity forecasting information is occasionally presented with low accuracy due to uncertain techniques and vast methods that employ different sensors and models. For this reason, this work proposes an Internet of Things (IoT) temperature and humidity forecasting model based on an improved whale optimization algorithm with long short-term memory (IWOA-LSTM) technique. To increase the convergence speed processing time and overcome the local optimization problem, the IWOA is introduced. The number of hidden layers, learning rate momentum, and weight decay of the LSTM optimized using the IWOA. The actual temperature and humidity data are collected using DHT11 and ESP8266 NodeMCU practical model and processed using the ThingSpeak platform. The processing data stage depends on filling the missing data gaps using the rolling average technique (RAT). The performance evaluation of the proposed IWOA-LSTM forecasting model is assessed using some statistical functions, namely known as mean square error, mean absolute error, root mean square error, and mean absolute percentage error. The IWOA-LSTM techniques were also assessed using throughput, latency, and power consumption. The developed IWOA-LSTM model shows high accuracy, leading to better forecasting information than other forecasting models.

Sistem Pemantau Detak Jantung dan Saturasi Oksigen dalam Darah (SpO2) Berbasis IoT

Heart rate and blood oxygen saturation (SpO2) are the two main parameters needed to determine the condition of a patient being treated in the intensive care unit (ICU) at a hospital. These two parameters must not be left unattended from monitoring because they can be fatal for the patient.This study aims to create an IoT-based heart rate monitoring and blood oxygen saturation (SpO2) application so that it can be accessed online in real time anytime and anywhere. The observation process is in the form of analog signal changes which are processed with fast fourier transform arithmetic as digital signal processing. The final process of processing displays the measured heart rate and blood oxygen saturation (SpO2).Based on the trial results, the use of the thingspeak platform can work well. Parameters of heart rate and blood oxygen saturation (SpO2) can be accessed online in real time anytime and anywhere so as to assist medical personnel in monitoring patients in hospitals.

IoT-Based Real-Time Crop Drying and Storage Monitoring System

Maize flour obtained from the dried corn is one of the most consumed foods in Rwanda. It is imperative that this should be healthy and risk-free for a safe consumption. Therefore, it is vital to keep track of the environmental conditions during the drying process and the characteristics that exist inside maize storage containers. In Rwanda, traditional methods are most commonly used by maize farmers for drying and storage purposes, where no smart system is being used to monitor the environmental conditions under which the maize grains are dried and stored. This mostly affects the quality of maize and flour being produced which will finally affect food security. In this research, temperature, humidity, and light sensors are deployed in the grain storage containers for environmental parameter detection purposes to achieve the primary goal of providing practical, secure, and easily accessible storage in inclement weather. Temperature and humidity are two factors that have an impact on grain quality while in storage. The ThingSpeak platform has been used to help farmers monitor the drying and storing conditions of the maize on a real-time basis. A global system for mobile (GSM) communication module is used to notify farmers by sending a short message in case of critical drying or storing environmental parameters under which the maize grains are stored. The result is shown in the form of humidity, temperature, and light graphs which are displayed on the ThingSpeak platform in real-time mode.