Wemos D1 Research Articles

Design of a Smart Donation Box Based on IoT

This research, entitled "IoT-based Intelligent Charity Box Design", aims to develop a charity box system that utilizes the Internet of Things (Iot) technology to enhance public participation in charity. In this context, the charity box not only serves as a container for storing donations, but is also equipped with features that can automatically monitor and calculate the amount of money inserted. The system uses the Wemos D1 Mini as a microcontroller connected to the Internet, allowing users to access data in real time through a website. In addition, this smart charity box is equipped with a PIR sensor that detects motion and triggers sound on the DFPlayer, as well as a TCS3200 color sensor that counts the paper money entered. Thus, the research not only offers technological solutions, but also seeks to remind the public of the importance of charity in a more interactive and efficient way. The results of this research are expected to benefit the management of charity funds and raise social awareness in the community.

Implementation Of Dot Matrix Max7219 For Product Price Display

The advancement of technology has strengthened the role of retail businesses as providers of products and services that are not only efficient but also responsive to market changes. In this context, the implementation of technological solutions such as the Dot Matrix MAX7219 for product price display has become increasingly important in improving operational efficiency and customer experience. This research investigates the development and implementation of such a system using the Wemos D1 Mini microcontroller as the central control unit. Experimental methods were used to design, test, and update the system, focusing on the system's response to changes in product prices and WiFi connection stability. The test results show that the system is able to display prices quickly, accurately, and responsively, while also having an adequate WiFi connection range. The practical implications of this research include the potential adoption of this solution by retail businesses to enhance operational efficiency and customer experience. However, continuity in system development and maintenance remains necessary to ensure the sustainability and success of the implementation in the long term.

Solar-powered pesticide sprayer using WeMos D1 Arduino-based board with Internet of Things (IoT) integration

Solar-powered pesticide sprayer using WeMos D1 Arduino-based board with Internet of Things (IoT) integration

Rancang Bangun Alat Pendeteksi Kerusakan pada Bearing Kendaraan Angkut NPS 75 4x4 milik TNI Angkatan Darat Berbasis K- (Nearest Neightbor)

The Indonesian National Army (TNI) is a key element in maintaining national defense. However, challenges in the TNI's performance arise due to the need to modernize the main defense system equipment (alutsista) especially related to the condition of the NPS75 4x4 transport vehicle. One of the problems that often occurs is damage to wheel bearings, which can threaten personnel safety. Traditional methods such as listening to the sound of the engine and carrying out a physical inspection have been used before, but with the development of electronic technology, automatic devices can be created that monitor the condition of vehicle bearings using vibration detection. In this research, we propose the use of the Wemos D1 Mini which is connected to the driver's mobile phone via a WiFi network. This device uses the K-Nearest Neighbor (KNN) method to classify damage to bearings based on learning data. The aim is to assist in automatically detecting and classifying bearing damage categories on NPS75 4x4 transport vehicles, so that timely and targeted maintenance actions can be taken.

DESIGN OF CARBON MONOXIDE (CO) MEASUREMENT TOOL USING SIM900A AND SOLAR PANEL BASED ON WEMOS D1 MINI

With abundant natural resources, East Kalimantan has a major environmental issue: forest fires. Several cases in East Kalimantan currently garner pros and cons regarding relocating Indonesia's new capital to the region. Fires in peatlands are usually dominated by smoldering processes, resulting in the highest emissions of carbon monoxide (CO) particles. Carbon monoxide gas is odorless, colorless, and tasteless, making it difficult to detect, yet highly toxic, and can cause illness in humans. Considering the dangers caused by CO gas, this research aims to create a CO gas detector to determine air pollution conditions in the environment. This research uses an MQ-7 sensor to detect CO gas, where the data is processed into parts per million (ppm), Wemos D1 Mini as the microcontroller, and SIM900A module as the data transmitter to the website. The system uses solar panels to fulfill its power needs independently. The device's characteristics can measure CO levels ranging from 51.55 to 907.61 ppm with an average accuracy rate of 95.10% and precision of 97.18%. The model performed well overall.

Cloud Storage for Object Detection using ESP32-CAM

Cloud storage services can create an object storage bucket to store our pictures, among them the Cloud Storage FUSE, Scaleway, S3 bucket, Firebase, etc. intelligent IoT systems generate vast amounts of multi-source industrial data, which necessitate a large amount of storage and processing power to enable real-time data processing and analysis. Cloud computing can be intricately linked into intelligent IIoT systems due to its strong computational and storage capabilities. Cloud Storage for Object Detection using ESP32-CAM. Create a workable solution that supports distributed storage bucket and implement it in a real-world setting. Implement the entire system as an addition to the well-known IoT cloud storage and run multiple experiments to evaluate its functionality in scenarios with varying setups and system. The target objects that are used as data sets are the ESP8266, Wemos D1, and Arduino Uno. Figuring out the ideal parameters for training the FOMO (First Object, More Object) model and then putting it into practice. It was necessary to find a balance between learning rate and accuracy, on the other hand, to maintain the highest possible accuracy in the identification of the microcontroller object to minimise the number of false positive reports. Find the value learning rate effective to this object is 0.01 with F1 score 98.7% and accuracy score 89.58%.

Design an Automatic Condenser in Swallow Bird House

This design is a system development carried out by swallow breeders in stabilizing the temperature and humidity of swallow houses. This design uses the principles of the Internet of Things (IoT), which will monitor temperature and humidity through internet media. This design aims to make a temperature and humidity control system in the swallow house automatically and find out the level of accuracy of temperature and humidity readings in the design made. This design uses a Wemos D1 Mini Board microcontroller that controls components such as DHT22 sensors, DS18B20 sensors and relays. The result of this design can control the condenser automatically, when the temperature and humidity are above the standard temperature, it will turn on the condenser automatically.

Portable Low-Cost Home Sleep Monitor using Wemos D1 Mini

Sleeps disorders are a common disease overlooked by many people. Sleep disorder have many types and kinds and often associated with other severe illness such as diabetes, stroke, obesity and many others. Sleep monitors are one of many ways to read all parameters related to sleep and detect sleep disorders the subject has, however access to sleep monitor still expensive and tough to come by. Because of that accessibility, countless development of home sleep monitor occured around the world. However, most of that device still hard to operate and some of them gives error readings of parameters. Based on this, a portable low-cost home sleep monitor was developed using Wemos D1 mini as a microcontroller, MAX30102 as an oxygen level sensor, MPU6050 as an accelerometer, DS18B20 as a breathing flow sensor, and MAX9814 as a microphone. Each of the sensors read and give value to microcontroller and store the data to cloud and display the result in user’s gadget. The aim of this development is to detect sleep disorders associated with each reading of sensors used and determine sleep quality as an early detection of symptoms before referring to professional relaled to sleep disorders or doctors.

IOT Soft Starter Revolutionizes Single-Phase Induction Motor Control

Induction motors often face issues with large initial currents that can disrupt networks and damage the motors. This study designs a soft starter for single-phase induction motors using an IoT-based Arduino Uno system to regulate initial current surges and enable remote monitoring via the Blynk App. The system uses the AC Light Dimmer module controlled by Arduino Uno and Wemos D1 Mini microcontrollers, with the PZEM-004T sensor detecting current and voltage. Experimental results showed a significant reduction in initial current, averaging 0.48 A and 201.90 V within 10 seconds. Additionally, the system allows for monitoring from up to 16 km away using an Android smartphone, providing an effective solution for managing induction motor performance and ensuring reliable operation. Highlight: Renewable technology for controlling single-phase induction motor initial current. Software and system flowcharts illustrate IoT-based motor control. Significant reduction in initial current demonstrated in induction motor testing. Keywoard: Induction Motors, Soft Starter, IOT, Arduino Uno, Remote Monitoring.

Sistem Monitoring Kualitas Air dan Pakan Otomatis Pada Akuarium Ikan Mas Koki Terintegrasi IoT

Water is a living medium for goldfish, which greatly influences their lives. Water quality as a living medium for fish greatly influences the growth and development of goldfish; therefore, water quality must meet the needs of goldfish. Several water quality parameters that greatly influence fish life are temperature, degree of acidity (pH), and solid substances dissolved in water (TDS). The aim of this research is to design a monitoring system capable of measuring temperature, degree of acidity (pH), and solid substances dissolved in water based on the Internet of Things. This tool is used for an Internet of Things-based water quality monitoring system in goldfish aquariums, where the results obtained will be sent via smartphone to the Blynk application. And it can also provide food automatically and provide notifications if the water becomes cloudy. This tool is designed using an Arduino Uno, a Wemos D1 R2 controller, and sensors used to measure each parameter, namely the DS18B20 sensor, pH sensor, and TDS sensor. Then the feeder uses a servo to open the feed and an RTC as a timer. The results of this research conclude that the calibration results for the DS18B20 sensor have an average accuracy rate of 99% with a sensor error percentage of 1%, the pH sensor has an accuracy rate of 98.7% with an error percentage of 1.3%, and the TDS sensor has an accuracy rate of 94.59% with an error percentage of 5.41%.

Analysis of Using Machine Learning Techniques for Estimating Solar Panel Performance in Edge Sensor Devices

The importance of the usage of renewable energy sources in powering wireless sensor nodes in IoT and sensor networks grows together with the increasing number of utilized sensor nodes. Considering the other types of renewable energy sources, solar power differs as the most suitable one and emerges as the major source for powering sensor nodes. Thus, the consideration of using sensor nodes and collected sensor data for estimating solar panel performances and therefore solar power potential can improve the efforts in this direction. This paper presents the methodology for implementing edge intelligence on wireless sensor nodes for solar panel output voltage estimation and forecasting. The methodology covers the usage of the Python Scikit-learn package and micromlgen library for the implementation of edge intelligence on Arduino clone-based sensor nodes, particularly the development boards based on the ESP8266 chips. Scikit-learn is used for analyzing the efficiency of various regressors on collected solar data. The micromlgen library is then used for implementing those regressors on Arduino and clone nodes. The prediction of solar panel voltage generation is based on a single-sensor reading—UV or BH1750 light sensor. The Random Forest and Decision Tree regressors are implemented on the ESP8266-based development board—Wemos D1 R2. The estimation accuracy of the RF model is an MSE of approximately 0.10, MAE of 0.07 for UV and 0.04 for BH1750, and an R2 of approximately 0.93 for both UV and BH1750 light sensors. The Decision Tree model has a lower accuracy with an MSE between 0.13 and 0.14, MAE of 0.07 for UV and 0.04 for BH1750, and R2 of 0.90 and 0.89 for the UV and BH1750 sensors, respectively. The methodology and its efficiency are presented and discussed in this paper.

Development of cloud visualization a machining manufacturing system shop floor

<p><span>The industry is currently experiencing the fourth industrial revolution, characterized by the automation of cyber physical systems and advanced connectivity through the internet of things (IoT). This revolution enables real-time monitoring of machines status on the shop floor by leveraging cyber-physical and IoT technologies. This paper describes the results of research that develops IoT and cloud-based visualization for a machining manufacturing system shop floor. Our proposed solution involves an internet of things device equipped with two current sensors to detect machine and spindle current. The sensor connected to an Arduino Nano, which is then connected to Wemos D1 for wireless transmission of data to the cloud. The cloud has been developed to store data and provide visualization applications, in the form of machines layout map to monitor machines conditions in the form of machines ON, machines OFF, spindles ON and spindles OFF in real time.</span></p>

Development of an internet of things-based weather station device embedded with O<sub>2</sub>, CO<sub>2</sub>, and CO sensor readings

Weather station devices are used to monitor weather parameter conditions, such as wind direction, speed, rainfall, solar radiation level, temperature, and humidity. This article discusses the design of a customized weather station embedded with gas concentration readings, whereby the gas concentration measurement includes oxygen (O<sub>2</sub>), carbon dioxide (CO<sub>2</sub>), and carbon monoxide (CO). The measurements and data processing of input sensors were transmitted to an Arduino Uno microcontroller, and the input data were then remitted to Wemos D1 Mini to be uploaded to a cloud server. Furthermore, the gas sensors' characterization methods were also considered to reveal the obtained results of accuracy, precision, linearity, and hysteresis. An android-based mobile application was also designed for monitoring purposes. The system in our experiment utilized an internet connection with a field station, base station, and database server.

Ultraviolet-C lamp control system designed to estimate deactivation of the coronavirus disease

To prevent the transmission of the coronavirus disease (COVID-19), one approach involves the application of disinfectants containing specific chemical compounds. Nonetheless, an overabundance of chemicals may yield adverse effects on both humans and the environment. Therefore, alternative methods are needed to prevent the spread of the virus without endangering humans and the environment. One method that minimizes the use of chemicals is ultraviolet-C (UV-C) light. The method used in this study is to make a UV-C lamp control system based on the internet of things (IoT). Then conduct experiments on the spread of UV-C radiation using a system that has already been built. Based on the research that has been done, a disinfectant system has been successfully designed using two Philips 30 W UV-C lamps with Wemos D1 mini microcontroller and Blynk application. The results of data collection show that the highest ultraviolet-C radiation irradiation on the intended object is 0.017 mW/cm<sup>2</sup> with a distance between the two is 1.5 m.

Component Modification and Data Communication Lines of Automatic Crop Sprinklers on Soil Moisture-Based Agricultural Land

Plants are living things that need water for their growth. This research was carried out designing, making, and developing tools that previously existed. This development focuses on how to communicate between microcontrollers and aims at the needs of large and open land. Where in previous studies the coverage area was only the area around the radius of the wifi router and the coverage will be increased again using NRF24L01. In previous studies using relays on the solenoid valve will be replaced using Mosfet LR7843 which has lower electrical power requirements. This system uses the Wemos D1 Mini and Arduino Uno as its microcontroller, where data from sensors in the system in the Arduino Uno will be sent using NRF24L01 and received by NRF24L01 on the Wemos D1 Mini. Then the data that has been received will be processed and sent to the internet and can be monitored through the Blynk application which can see temperature conditions, air humidity and soil moisture. From the test results, the average difference obtained at a temperature reading of 0.44 ° C and air humidity of 3.2%, and when the humidity reaches >50% the solenoid valve can open automatically. The distance that can be reached when testing as far as ±20 meters can be due to the test site being blocked by walls.

IoAT: Internet of Aquaculture Things for Monitoring Water Temperature in Tiger Shrimp Ponds with DS18B20 Sensors and WeMos D1 R2

Cultivation of tiger prawns stands as a crucial sector in Indonesia's fisheries industry, significantly contributing to the country's foreign exchange. However, challenges persist in the cultivation process, particularly concerning suboptimal harvest outcomes. A critical factor in tiger prawn cultivation is the water temperature within shrimp ponds, a parameter directly influencing shrimp growth. The recommended normal temperature range is 28-31°C. Deviations from this range can adversely impact the shrimp's metabolic system and appetite, resulting in stress and potential mortality. Temperature fluctuations can lead to severe issues such as hindered growth, reduced productivity, and increased shrimp mortality. Real-time monitoring of air temperature emerges as a pivotal element in ensuring the success of shrimp farming. This research aims to provide a practical solution for shrimp cultivation by presenting a system that enables farmers to adjust air temperature in ponds in real-time through a user-friendly website application. The ability to promptly respond to abnormal temperature fluctuations empowers farmers to optimize cultivation conditions, thereby reducing shrimp mortality rates. The research focuses on creating a water temperature monitoring system for tiger prawn ponds using cloud storage through the Firebase platform. By implementing real-time temperature monitoring, financial risks for shrimp farmers can be mitigated, preventing losses attributed to temperature-induced shrimp mortality. The research utilizes the DS18B20 temperature sensor and WeMos D1 R2 as the control center. The website displays air temperature measurements, showcasing a high accuracy of 99% with a minimal error of 1.2%. This underscores the system's effectiveness in measuring air temperature both above and below the pond. The incorporation of IoT technology for monitoring water quality in ponds offers a practical and innovative approach to tiger prawn cultivation, with the potential to enhance production outcomes in each harvest.

DESIGN AND DEVELOPMENT OF AI CLOUD-BASED VIDEO RECORDING SYSTEM FOR ATHLETE MOVEMENTS

This paper presents the design and development of an AI cloud-based video recording system for athlete move- ment analysis. The proposed system utilizes the Wemos D1 Mini microcontroller as the core hardware platform and a GoPro cam- era for high-quality video capture. By leveraging the capabilities of these components, the system enables real-time video re- cording of athlete movements, facilitating detailed performance analysis and feedback. Furthermore, the system seamlessly inte- grates with Amazon Web Services (AWS) IoT Core, enabling efficient data transmission and storage in the cloud. Through re- search and development, both the hardware and software components of the system were designed and implemented, ensuring robust performance and scalability. Experiments demonstrate the efficacy of the proposed solution in capturing high-fidelity video footage of athlete movements and securely transmitting it to the cloud for further analysis. This research lays the foundation for advanced athlete monitoring systems, offering valuable insights for coaches, trainers, and sports scientists to enhance training regimens and optimize performance.

Prototipe dengan Sistem IoT Pada Pengaturan Suhu dan Kelembaban Pada Permentasi Tempe

This research aims to design an IoT-based prototype for temperature and humidity control to enhance the productivity of tempe fermentation. The prototype utilizes the WeMos D1 R2 microcontroller, processing data from the temperature sensor (DHT11) and RTC DS3231 to control a DC fan and heater. The Blynk application enables remote monitoring and management of temperature and humidity. Research results indicate stable temperature (30°C–37°C) and humidity (60–70 RH). The prototype demonstrates effectiveness in controlling tempe fermentation conditions and is suitable for implementation. .

Alat Pencegah Dini Kecelakaan Pada Kendaraan Bermotor Berbasis Internet of Things (Studi Kasus Jalur Sitinjau Lauik)

The creation of an Internet of Things-based motor vehicle early prevetion tool (Sitinjau Lauik Case Study) has been completed.. The aim of this research is to develop a tool to minimize accidents caused by human physiological factors such as fatigue and drowsiness. This device utilizes the MPU 6050 sensor, pulse heart sensor, buzzer, vibration motor, GPS, Arduino Uno and Wemos D1 Mini. The research method began with the construction of the device and the permance testing of the system. The results indicate that the system of this device can detect drowsiness or fatigue by measuring the heart rate at less than 70 bpm using the pulse heart sensor and detecting head movements using the MPU 6050 sensor, where the X- axis angle is > 29,23 ° < - 30,63 ° and the Y-axis angle is > 29,33° < -20,12°. When drowsiness is detected, the device sends notifications via the Telegram application containing the driver’s condition, heart rate, andlocation through a Google Maps link. Subsequently, when the driver is drowsy, the buzzer sounds to provide a warning and the vibration motor provides vibration to give a sensory wake-up signal to the driver, helping to awaken them from a drowsy state. Overall, the device functions effectively.

SISTEM MONITORING KUMBUNG JAMUR TIRAM OTOMATIS BERBASIS IoT (Internet of Things) MENGGUNAKAN METODE K-NEAREST NEIGHBOUR

In response to the growing market demand for oyster mushrooms, effective monitoring of the growth environment becomes crucial. This research aims to develop an automated Internet of Things (IoT)-based monitoring system for oyster mushroom cultivation, utilizing Wemos D1 R1 hardware, DHT22 temperature and humidity sensor, LM35 temperature sensor, and the K-Nearest Neighbour (KNN) method for data analysis. The IoT platform employed in this study is Antares. The system is designed to monitor real-time temperature and humidity within the mushroom cultivation chamber. The DHT22 sensor is employed for simultaneous measurement of air temperature and humidity, while the LM35 sensor gauges the soil temperature in the mushroom growth substrate. Data collected by these sensors are automatically transmitted to the Antares platform through Wemos D1 R1 via WiFi connectivity. The K-Nearest Neighbour (KNN) method is applied to analyze the accumulated temperature and humidity data. KNN provides the capability to identify patterns and trends in oyster mushroom growth based on environmental conditions. The results of this analysis offer valuable insights for oyster mushroom farmers to optimize growth conditions and enhance harvest yields. Through the implementation of this system, it is anticipated that efficiency and productivity in oyster mushroom cultivation will be improved. This IoT-based automated monitoring system provides an effective and practical solution for real-time monitoring of oyster mushroom growth conditions, offering farmers the opportunity to take prompt and informed actions to enhance their harvest yields.