Arduino-based Sensor Research Articles

Smart-Agro: Enhancing Crop Management with Agribot

The Agri-Bot robotic system indeed characterizes a substantial advancement in modern agriculture, offering a multifaceted solution for monitoring and managing agricultural environments. By integrating various Arduino-based sensors and motor drivers, it provides a comprehensive toolkit for farmers to effectively oversee their crops' health and optimize resource usage. The inclusion of pH and moisture sensors enables real-time monitoring of soil conditions, allowing farmers to adjust irrigation and fertilizer application precisely according to the plants' needs. Additionally, the DHT11 sensor offers insights into ambient conditions crucial for plant growth, such as temperature and humidity, facilitating informed decision-making. The incorporation of the L298 motor driver further enhances the system's capabilities by enabling automation of tasks like irrigation and seed sowing with precision and efficiency. This integration of robotics and sensor technology not only streamlines agricultural processes but also empowers farmers with data-driven insights to optimize crop growth and sustainability.

Eigenfrequency analysis using fiber optic sensors and low-cost accelerometers for structural damage detection

Structural Health Monitoring (SHM) is crucial for infrastructure safety and integrity. Arduino-based sensors are gaining popularity in low-cost SHM structures. Distributed fiber optic systems (DFOS), such as Distributed Acoustic Sensing (DAS), are employed for accurate SHM despite their high costs, computational demands, and energy consumption. The primary objectives of this work are to compare the accuracy of an accelerometer named LARA (Low-cost Adaptable Reliable Accelerometer (LARA)) that utilizes both Arduino and Raspberry Pi technologies with a DAS system in detecting structural damage and to explore the potential advantages of combining LARA and DAS to create an effective SHM tool. This study is the first to enhance the design of LARA. Subsequently, LARA and DAS were used in a laboratory setting to analyze eigenfrequency changes in a beam model with induced localized damage. Finally, this study evaluated the precision and reliability of LARA and its potential role as a trigger for DAS in detecting localized damage. The findings show that both LARA and DAS can identify changes in the eigenfrequencies of damaged structures with deviations as small as 3.68 %. Consequently, LARA demonstrated its potential as a trigger for DAS, significantly reducing the computational demands while enriching the analysis. This approach offers highly accurate eigenfrequency measurements and enhances the analytical capabilities of DAS by identifying the primary axes of the detected eigenfrequencies.

Hydro Electrometric Tracking Application

We offer a brand-new technique for real-time control over the project's water flow rate and electricity usage. Unlike current systems, which are frequently costly and solely monitor power or water, our method integrates both parameters into a single, reasonably priced platform. Our solution allows users to track their resource consumption in real-time by utilizing Arduino-based sensors and a user-friendly mobile application created with MIT App Inventor. By providing consumers with the knowledge, they need to make educated decisions about how much water and power to use, this promotes more effective and sustainable resource management. It is unique in that it provides an affordable solution that integrates water and power metering capabilities, solving major issues with current systems.

Smart Water Pollution Management: IoT for Automatic Detection and Prevention

Water quality monitoring is vital for public health and environmental sustainability, but traditional methods face challenges such as high costs, lack of portability, and limited real-time data access. This paper presents a solution using Arduino-based sensors integrated with IoT technology to enhance water quality monitoring. The system employs various sensors to measure parameters like Total Dissolved Solids (TDS), turbidity, water transparency, methane gas, and alcohol gas. Data from these sensors is transmitted to a NodeMCU microcontroller and uploaded to the Blynk cloud platform, allowing real-time monitoring and remote access via customizable dashboards. The proposed system is cost-effective, portable, and provides immediate data access, addressing the limitations of traditional methods. It enables timely detection of water quality issues, facilitating prompt responses to health and environmental threats. This innovative approach significantly advances water quality monitoring by leveraging IoT technologies to improve environmental management practices

Solar Powered Car Optimization Through Machine Learning

The main goal of the project is to predict the solar power output of the solar panel depending on prevailing weather conditions using some Machine Learning algorithms for the purpose of optimizing and adapting the power consumption so as to strike a good balance between solar power supply and demand. For predicting the power output, the following parameters are measured with the help of an Arduino-based sensor unit -Temperature, Pressure, Light intensity and Relative humidity. Apart from them as well, cloud coverage, visibility, and dew point, and we add wind speed is also taken into consideration which we collect from the weather forecast that help a lot. In order in an attempt to train the Machine Learning algorithms, a dataset using the above parameters will be formed hopefully. As renewable energy is gaining a lot of ground rapidly, it becomes necessary for us interestingly to make solar systems reliable kind of. By predicting the power output of the panel we can plan a Necessary alternative accordingly as well, making the system more reliable indubitably. Solar energy prediction is significant in enhancing the competitiveness of solar power plants in the energy market indeed, and decreasing reliance on fossil fuels in socio-economic development largely. Our work is aiming to accurately predict the solar energy. IndexTerms–MachineLearning, SolarPower, Arduino Uno, PythonJupyterNotebook, Sensors.

Comparison of Face Masks by Arduino Sensors


 
 
 The COVID-19 pandemic in early 2020 has created a health crisis around the world. There have been many precautions taken against the virus, such as social distancing and the use of personal protective equipment. Face masks are an essential part of personal protective equipment, especially for essential workers. Although the pandemic has caused a high demand for facemasks, there is limited scientific knowledge available on the various types of masks. Knowledge about the air quality inside the different facemasks could be beneficial for both the COVID-19 pandemic and future disease outbreaks, which will seemingly increase in the future. There are different types of masks, ranging from tight-fitting N95 respirator masks to looser surgical masks to homemade cloth masks. Herein, N95 respirator masks, surgical masks, cloth face masks, and surgical masks on top of N95 respirator masks were compared in terms of CO2 levels, Volatile Organic Compound levels (VOCs), temperature, humidity, and O2 concentrations. To record these measurements, Arduino-based sensors were constructed and utilized for the measurements. It was found that the N95 mask and the surgical mask on top of the N95 mask trapped the highest concentration of carbon dioxide, VOCs, temperature, humidity, and the lowest oxygen levels compared to the surgical mask and cloth mask.
 
 

CERP: Cooperative and Efficient Routing Protocol for Wireless Sensor Networks.

Wireless sensor networks (WSNs), constrained by limited resources, demand routing strategies that prioritize energy efficiency. The tactic of cooperative routing, which leverages the broadcast nature of wireless channels, has garnered attention for its capability to amplify routing efficacy. This manuscript introduces a power-conscious routing approach, tailored for resource-restricted WSNs. By exploiting cooperative communications, we introduce an innovative relay node selection technique within clustered networks, aiming to curtail energy usage while safeguarding data dependability. This inventive methodology has been amalgamated into the Routing Protocol for Low-Power and Lossy Networks (RPL), giving rise to the cooperative and efficient routing protocol (CERP). The devised CERP protocol pinpoints and selects the most efficacious relay node, ensuring that packet transmission is both energy-minimal and reliable. Performance evaluations were executed to substantiate the proposed strategy, and its practicality was examined using an Arduino-based sensor node and the Contiki operating system in real-world scenarios. The outcomes affirm the efficacy of the proposed strategy, outshining the standard RPL concerning reliability and energy conservation, enhancing RPL reliability by 10% and energy savings by 18%. This paper is posited to contribute to the evolution of power-conscious routing strategies for WSNs, crucial for prolonging sensor node battery longevity while sustaining dependable communication.

Teaching uniform linear motion using an Arduino sensor and a smartphone device

In this work, we designed and implemented an Arduino-based experiment to study one-dimensional uniform linear motion. The position of a small vehicle running at a constant velocity in one dimension, is measured using an ultrasonic sensor. A Bluetooth module connected to an Arduino board is paired with a smartphone, where data are acquired and position vs time curves of the vehicle are plotted on the smartphone’s screen. Students can share the experimental data via their smartphones and study uniform linear motion through guided experiments. The educational experience of the classroom implementation of the project is also presented. The results show that pairing an Arduino-based sensor with an android device, improves students’ comprehension of motion, enhances their motivation and cultivates positive attitude and anticipation for the forthcoming science lessons.

Determining the coefficient of kinetic friction using smartphone sensors

Nowadays, smartphones play an important role in the physics laboratory as well as in Arduino-based sensors. Some recent examples include the analysis of acoustic experiments with mobile devices, mechanics experiments such as those on linear and circular motion as well as simple/damped harmonic motion. Smart phones can accurately record experimental data using internal sensors (e.g., accelerometer, gyroscope, magnetometer) via appropriate applications (apps), such as phyphox, SensorLog, and SensorKineticsPro. Moreover, students are familiar with using smartphones and these apps, making these experiments interesting and easy for them to perform. The coefficient of static/kinetic friction can be measured using smartphone sensors, as seen in previous reports. Typically, the coefficient of kinetic friction can be determined by measuring the acceleration of a sliding object (smartphone) on an inclined or horizontal plane. In this paper, we propose alternative methods to measure the coefficient of kinetic friction between a smartphone and paper in linear and circular motions using the smartphone’s sensors. In the experiment, the acceleration of the sliding object (smartphone) is measured using the linear motion method, while its angular speed is measured using the circular motion method. The experimental data are then sent to the user for analysis to determine the coefficient of kinetic friction. The results obtained from the linear motion method are in good agreement with those obtained from the circular motion method.

RANCANG BANGUN ALAT UKUR SISTEM MONITORING pH, TEMPERATUR, DAN KELEMBAPAN AKUARIUM IKAN HIAS BERBASIS ARDUINO UNO

Water quality in the process of ornamental fish cultivation plays an important role in creating an environment of life that is in accordance with the needs of ornamental fish. Acidity (pH) and water temperature are one of the important factors of ornamental fish growth. The purpose of this study is to describe how the design system builds arduino-based humidity and pH temperature measuring instruments. Review the validity of moisture temperature and water pH. Monitor the humidity temperature and pH of ornamental fish aquarium water to know the results of the same data with liturgy. This research is a research and development research, conducted in the Laboratory of Physics Education Study FKIP Jember University. The research time was carried out for four months. The samples used in this study were ornamental carp. Based on the analysis of data obtained, it can be concluded that, (1) The work of pH, temperature, and spouting systems based on Arduino Uno is made with sen0161-V2, DS18B20 and DHT-11 sensors and data appearance media namely LCD. (2) Rakaian system is then calibrated to state that the system can be used properly. Calibration includes Arduino-based pH, temperature and Humidity sensor calibration as well as LCD testing. (3) The data that has been found by the researcher and the data in the literature has a match.
 
 Keywords: water quality, Acidity (pH), water temperature, arduino-based.

Water Quality Monitoring with Arduino Based Sensors

Water is a quintessential element for the survival of mankind. Its variety of uses means that it is always in a constant state of demand. The supply of water most primarily comes from large reservoirs of water such as lakes, streams, and the ocean itself. As such, it is good practice to monitor its quality to ensure it is fit for human consumption. Current water quality monitoring is often carried out in traditional labs but is time consuming and prone to inaccuracies. Therefore, this paper aims to investigate the feasibility of implementing an Arduino-based sensor system for water quality monitoring. A simple prototype consisting of a microcontroller and multiple attached sensors was employed to conduct weekly onsite tests at multiple daily intervals. It was found that the system works reliably but is reliant on human assistance and prone to data inaccuracies. The system however, provides a solid foundation for future expansion works of the same category to elevate the system to being Internet of Things (IoT) friendly.

Використання дисперсії Аллана для ідентифікації нормальної роботи сенсорних вузлів

In cyberphysical systems and the Internet of Things (IoT) the threat of failure of sensor nodes as well the threat of unauthorized interference during the transmission of measurement data exists. Thus, the need to develop and improve methods of monitoring sensor nodes is relevant given the large number of such nodes, energy constraints that limit traffic and computing resources of systems, as well as the need for full automation of work due to the economic inexpediency of their maintenance. It is proposed to use one of the statistical methods, namely the Allan variance or a more general concept of structural functions, to study stochastic processes in time sequences representing the implementation of random measurement results in such systems. The ability to calculate the statistical characteristics of nonstationary processes and to determine the spectral dependences of various kinds of noise at moderate computational costs is the advantage of that method. The use of Allan dispersion to process model temperature and humidity data from Arduino-based sensors in the Internet of Things system is considered. The dependences of Allan deviation and its modifications as functions of various averaging interval are calculated. For the typical modes of operation of the considered sensors in the Internet of Things applications the dependences of the Allan deviation correspond to the type of noise "Random Walk FM". It is confirmed by calculations at different measurement intervals. The influence of different types of noise on these dependences is investigated. The minimum set of parameters that can with a limited data set characterize the normal measurement process, possible malfunctioning or natural and artificial interference to normal operation was chosen. The possibility of splitting the process of statistical processing between the sensor node and the controller of the Internet of Things is indicated, which will reduce the traffic and the appropriate energy consumption of the sensor node.

An arduino-based sensor to measure transendothelial electrical resistance

As the most popular microcontroller, Arduino is gaining interest for prototyping and optimizing instruments. Here, we report for the first time the use of Arduino as a standalone instrument to measure cell barrier integrity. Specifically, a transendothelial/epithelial resistance (TEER) meter was fabricated with an Arduino unit. TEER is an essential biophysical measurement to evaluate the integrity of biological barriers. Characterization of the device showed that the meter could accurately measure TEERs between 132 and 82,500 Ω·cm2 with <3% errors, which covers the typical TEER ranges. The temporal resolution, measurement duration, and electrode configurations can be customized to meet a broad range of experimental designs. We have successfully applied the meter to measure the TEERs of endothelial cell monolayers, finding that cells treated with histamine had lower TEER values compared to untreated cells (793.4 ± 190.5 Ω·cm2 vs. 3027.5 ± 664.4 Ω∙cm2; p < 0.001), which were validated and consistent with literature standards. In conclusion, we invented a low-cost Arduino-based TEER sensor capable of accurately measuring TEERs in relevant biological ranges. We also included detailed tutorials in the supplementary information to promote translation of the technology.

IoT Based Framework for Remote Monitoring of Heartbeat Alert System by Using Arduino

The heart is one of the most important organs of the human body. It acts as a pump that circulates the blood, carrying oxygen and nutrients in the body to keep it functioning. Today, several advanced methods have emerged in the field of medicine, which aim to improve the efficiency of the medical services. And these new methods are akin to a technical identification of the disease with high precision, a new radiological technology and a fashionable identification of medical health. In the medical department, several studies are underway to provide the latest technologies. There are some people suffer from bradycardia or tachycardia. Might their lives are in danger if the patients are alone. In this research paper, a framework is developed to solve and improve equipment that constantly monitors heart rate and prevent casualties due to the heart related problems. The research is done to reduce deaths resulting from irregular heartbeat. Uneven heart rating which is greater than determined value raises an alert and messages with heart readings are sent to doctors and other related persons. It also provides this information via Bluetooth and exposes it in the Arduino application platform to ensure the information is not corrupted and recalled on demand. Doctor can remotely access the sensing element. For this reason, Arduino-based sensors dealing with these problems are used in this smart application. An Arduino-based sensing element evaluates the patient's heart rate. Coupled with this, doctors remotely follow the patient and take quick action to save patients life.

Arduino-based Sensor Device – An Engineering Physics Second-year Design Project within the Engineering Design and Practice Sequence (EDPS) of the Queen’s Engineering Professional Spine

In 2011, Queen’s Engineering began rollout of its "Engineering Design and Practice Sequence (EDPS)". The EDPS is a "professional spine" sequence of courses over four years, meant to address and incorporate into all of its engineering programs the majority of the 12 Graduate attributes required by the Canadian Engineering Accreditation Board (CEAB). In year 1, the first EDPS course – Engineering Practice I - introduces students to engineering design and problem solving, but with little formal instruction in the design process and engineering tools. Formal instruction in these aspects comes in second year, in Engineering Design and Practice II (course number APSC200). Finally, in third and fourth year, students undertake significant design projects in their discipline. &#x0D; The second-year version of the professional spine, APSC200, is a one-term course taken by all students. This begins with a 6-week Faculty-wide course module, followed by a 6-week program-specific module. In the first Faculty-wide segment, students learn the design process – problem definition and scope, idea generation and broadening tools, decision-making tools, economic analysis, stakeholders, risk, and safety. Students are exposed to the necessity of formal design techniques via a zero-level "P0" project, and taught these techniques during a more extensive P1 project.&#x0D; The second 6 weeks of APSC200 involves a discipline-specific project (P2) in which the student teams practice the skills introduced in the earlier portion of the course while working through a design project chosen to emphasize the skills of their program. This paper focusses on the development and implementation of the P2 project for students in the Queen’s Engineering Physics program. The goal of this project is to introduce discipline-specific tools and techniques, to excite students in their chosen engineering discipline, and to put into practice the formal design techniques introduced earlier.&#x0D; The P2 project developed for Engineering Physics was entitled a "Compact Environmental Monitoring Station". The premise was that the Ontario Ministry of the Environment (MOE) issued an RFP for small, cheap sensor devices that could be provided to every Ontario household, and set up to "crowdsource" environmental data for the MOE. Student teams were required to research and justify which environmental parameters would be appropriate for their monitoring device, decide on parameters to monitor, design the device, and build a working prototype of the device.&#x0D; The device specifications required the use of an Arduino-based platform, interfacing the chosen sensor(s) to a laptop computer using MatLab. Since only some students were familiar with Arduinos and MatLab, two "just in time" workshops were delivered on these topics, using a "flipped lab" approach.&#x0D; For the prototype design and build, students had only 4 weeks and a budget of $100. Arduino boards and some basic sensors were supplied, with students able to source and purchase other components within their budget. The prototype-build provided the students with a valuable hands-on experience and also helped them to fully appreciate unexpected practical design constraints. Given the short timeframe (4-weeks) for the design and build, prototypes were very impressive, with many including solar power or rechargeable batteries, Bluetooth connectivity, 3-D printed packaging, IPhone or Android apps, as well as calibration functions.&#x0D; This paper will summarize the development of this Engineering Physics P2 module, and will report on the first year of offering it in its current format.&#x0D;

Arduino-based intelligent gases monitoring and information sharing Internet-of-Things system for underground coal mines

Hazardous accidents associated with the complex environment of underground gassy mines has urged researchers to constantly monitor mine environment for improved safety. The previously developed wireless monitoring systems for mine environment are not only expensive but also do not use the latest to ols for operational management and information sharing. Our system aims to integrate Arduino-based sensors, Internet-of-Things (IoT), and cloud computing for near real-time mine environment monitoring, ventilation control, emergency communications and information sharing in a more synchronous and cost-effective way to significantly enhance underground mine safety. The web 2.0 based main server integrates probabilistic theory and spatiotemporal data analysis techniques for ambient intelligence. This system has been validated in an actual working environment of an underground coal mine. The data comparison for one week of CO, CH4, and CO2 Arduino-based sensors to their counter commercial equivalents demonstrated high accuracy and excellent performance in the harsh mine environment. The overall response of the proposed system showed 87.5% sensitivity and 77.7% positive prediction value. This system is open-source and cost-effective and can effectively contribute to expressively enhance mine automation and control with early detection and warning.

Robot Lengan Pemindah Barang Otomatis Berbasis Arduino Menggunakan Sensor Warna

Robot arm transporter using Arduino-based sensor auto color, a tool designed to this aim to reduce human role in a grouping or sorting goods with the sort tool so that it can minimize the influence of inconsistencies humans in the event. This tool as well as a simulation as well as the media of instruction. Methods of data analysis used IE needs analysis that includes software that is the OS, and the Arduino Software, and hardware in the form of a Laptop, Arduino, Sensor, RGB, and Servo Motor. From the results of testing that has been done can be inferred that this Robot can run well at a time when reading the color box and put the table by the location of their vessels. The conclusion of the tool is designed as a common transport good; the robotic arm can be applied using several servos in the number of dof.

Monitoring heart rate and SpO2 using Thingsboard IoT platform for mother and child preventive healthcare

This paper describes the development of heart rate and oxygen saturation (SpO2) monitoring system using Thingsboard Internet of Things (IoT) platform. This research is part of development of IoT application for supporting mother and child health program in Indonesia. This IoT-based real time monitoring system enables preventive care and promotes automation to reduce the risk of human error. Measurement of heart rate and SpO2 is done by pulse oximetry method using sensor module and Raspberry Pi. The Arduino-based sensor module collects, calculates and sends heart rate and SpO2 data to Raspberry Pi for further analysis based on normal value reference table to detect abnormalities. Further heart rate, SpO2 and its abnormality status are delivered to Thingsboard using MQTT (Message Queuing Telemetry Transport) protocol. The results show that the system is functioned as expected. Heart rate value (in beat per minute) and SpO2 value (in percent) are presented while Photoplethysmogram signal (PPG) is presented as graphic on Thingsboard dashboard. The abnormalities status such as tachycardia, bradycardia and hypoxsemia are also featured on the dashboard. We also calculate uncertainty measurement of heart rate and SpO2 by the sensor module which are ±2.85 bpm and ±0.28%.

Low-Cost GRIN-Lens-Based Nephelometric Turbidity Sensing in the Range of 0.1-1000 NTU.

Turbidity sensing is very common in the control of drinking water. Furthermore, turbidity measurements are applied in the chemical (e.g., process monitoring), pharmaceutical (e.g., drug discovery), and food industries (e.g., the filtration of wine and beer). The most common measurement technique is nephelometric turbidimetry. A nephelometer is a device for measuring the amount of scattered light of suspended particles in a liquid by using a light source and a light detector orientated in 90° to each other. Commercially available nephelometers cost usually—depending on the measurable range, reliability, and precision—thousands of euros. In contrast, our new developed GRIN-lens-based nephelometer, called GRINephy, combines low costs with excellent reproducibility and precision, even at very low turbidity levels, which is achieved by its ability to rotate the sample. Thereby, many cuvette positions can be measured, which results in a more precise average value for the turbidity calculated by an algorithm, which also eliminates errors caused by scratches and contaminations on the cuvettes. With our compact and cheap Arduino-based sensor, we are able to measure in the range of 0.1–1000 NTU and confirm the ISO 7027-1:2016 for low turbidity values.

An Internet of Things System for Underground Mine Air Quality Pollutant Prediction Based on Azure Machine Learning.

The implementation of wireless sensor networks (WSNs) for monitoring the complex, dynamic, and harsh environment of underground coal mines (UCMs) is sought around the world to enhance safety. However, previously developed smart systems are limited to monitoring or, in a few cases, can report events. Therefore, this study introduces a reliable, efficient, and cost-effective internet of things (IoT) system for air quality monitoring with newly added features of assessment and pollutant prediction. This system is comprised of sensor modules, communication protocols, and a base station, running Azure Machine Learning (AML) Studio over it. Arduino-based sensor modules with eight different parameters were installed at separate locations of an operational UCM. Based on the sensed data, the proposed system assesses mine air quality in terms of the mine environment index (MEI). Principal component analysis (PCA) identified CH4, CO, SO2, and H2S as the most influencing gases significantly affecting mine air quality. The results of PCA were fed into the ANN model in AML studio, which enabled the prediction of MEI. An optimum number of neurons were determined for both actual input and PCA-based input parameters. The results showed a better performance of the PCA-based ANN for MEI prediction, with R2 and RMSE values of 0.6654 and 0.2104, respectively. Therefore, the proposed Arduino and AML-based system enhances mine environmental safety by quickly assessing and predicting mine air quality.