Search Results (59)

Real-time media streaming over publish–subscribe platforms is increasingly vital in scenarios that demand the scalability of event-driven architectures while ensuring timely media delivery. This is especially true in multi-modal and resource-constrained environments, such as IoT, Physical Activity Recognition and Measure (PARM), and Internet of Video Things (IoVT), where integrating sensor data with media streams often leads to complex hybrid setups that compromise consistency and maintainability. Publish–subscribe (pub/sub) platforms like Kafka and MQTT offer scalability and decoupled communication but fall short in supporting real-time video streaming due to platform-dependent design, rigid optimization, and poor sub-second media handling. This paper presents FrameMQ, a layered, platform-agnostic architecture designed to overcome these limitations by decoupling application logic from platform-specific configurations and enabling dynamic real-time optimization. FrameMQ exposes tunable parameters such as compression and segmentation, allowing integration with external optimizers. Using Particle Swarm Optimization (PSO) as an exemplary optimizer, FrameMQ reduces total latency from over 2300 ms to below 400ms under stable conditions (over an $80\%$ improvement) and maintains up to a $52\%$ reduction under adverse network conditions. These results demonstrate FrameMQ’s ability to meet the demands of latency-sensitive applications, such as real-time streaming, IoT, and surveillance, while offering portability, extensibility, and platform independence without modifying the core application logic. Full article

In the present work, a sensor network design for monitoring carbon dioxide (CO₂) pollution in Portoviejo City, Ecuador, is evaluated through a methodology that combines simulation and physical implementation. This methodology involves the development and evaluation of two scenarios: an initial scenario (A), developed through both physical implementation and simulation, and another simulation scenario (B). Both simulated scenarios are created using CupCarbon version 6.51 software. In these scenarios, the functionality of Wireless Sensor Networks (WSNs) is analyzed by implementing the LoRaWAN communication technology. Furthermore, the MQ-135 sensor is used to obtaining data on the PPM of (CO₂) in order to examine the areas that concentrate the most significant amount of this atmospheric pollutant. The proposed networks are evaluated using the packet loss metric during data transmission. After implementation, analysis, and respective evaluation, it can be concluded that the network simulated in Scenario B is suitable for monitoring (CO₂) and other pollutants that can be analyzed within the urban environment. Full article

Most people utilize their time indoors, either at home or in the workplace. However, certain human interventions badly affect the indoor atmosphere, causing potential health problems for occupants. This study aims to propose an air monitoring device integrated with an air purifier that monitors the pollutants affecting the indoor environment and automatically turns on/off the air purifier based on the pollution level. In the system, MQ7, MQ2, DHT11, and GP2Y1010AU0F sensors are integrated with ESP32 to detect indoor air pollutants, e.g., carbon monoxide (CO), methane (CH₄), temperature, humidity, and PM2.5. Data were collected for 30 days by mounting a proposed device in different indoor locations, including a poorly ventilated average living room, an indoor kitchen, and a crowded office space. The emission of carbon monoxide (CO) and methane (CH₄) was at 29.4 ppm and 10.9 ppm, PM2.5 was detected as 3 µg/m³, and the temperature and humidity were at 23 °C and 28%, respectively. Utilizing the Wi-Fi ability of ESP32, the data were transferred to the ThingSpeak IoT platform for the live tracking and analysis of the indoor atmosphere. Observing the measured data, the proposed system’s accuracy was calculated by comparing the results against a known standard device, which was estimated to be 95%. To protect the designed system, a protective case was also designed. Full article

To promote sustainable development, adopting circular economy principles is crucial for preserving natural resources and ensuring environmental continuity. Among solid waste management strategies, composting plays a significant role by converting biodegradable waste into eco-friendly biofertilizers. Traditional composting methods, which rely on open-window techniques, face challenges in controlling critical physico-chemical parameters such as temperature, humidity, and gaseous emissions. Additionally, these methods require significant labor and over 100 days to achieve compost maturity. To address these issues, we propose an intelligent, automated composting system leveraging the Internet of Things (IoT) and wireless sensor networks (WSNs). This system integrates sensors for real-time monitoring of key parameters: DS18b20 for waste temperature, HD-38 for humidity, DHT11 for ambient conditions, and MQ sensors for detecting CO₂, NH₃, and CH₄. Controlled by an ESP32 microcontroller unit (MCU), the system employs a mixer and heating elements to optimize waste degradation based on sensor feedback. Data transmission is managed using the MQTT protocol, allowing real-time monitoring via a cloud-based platform (ThingSpeak). Furthermore, the degradation process was analyzed during the first 24 h, and a recurrent neural network (RNN) algorithm was employed to predict the time required for reaching optimal compost maturity, ensuring an efficient and sustainable solution. Full article

The article describes an Arduino-based, portable, remotely usable weather monitoring station capable of measuring temperature, relative humidity, pressure, and carbon monoxide (CO) concentration and transmitting the collected data to the Cloud through the mobile telephony network. The main modules of the station are as follows: a DHT11 sensor for temperature and relative humidity sensing, a BMP180 sensor for pressure monitoring (with temperature compensation), a MQ7 sensor for the monitoring of the CO concentration, an Arduino Uno board, a GSM SIM900 module, and a buzzer, which is activated when the temperature exceeds 35 °C. The station operates as follows: the Arduino Uno board gathers the data collected by the sensors and, by means of the GSM SIM900 module, it transmits the data to the Cloud by using the mobile telephony network as well as the ThingSpeak software which is an open-code IoT application that, among others, enables saving and recovering of sensing and monitoring data. The main novelty of this work is the combined use of the GSM network and the Cloud which enhances the portability and usability of the proposed system and enables remote collection of data in a straightforward way. Additional merits of the system are the easiness and the low cost of its development (owing to the easily available, low-cost hardware combined with an open-code software) as well as its modularity and scalability which allows its customization depending on specific application it is intended for. The system could be used for real-time, remote monitoring of essential environmental parameters in spaces such as farms, warehouses, rooms etc. Full article

The quality of raw meat is important for community health as its freshness is crucial to preventing foodborne illnesses. In the United States, the related illness cases were 9.4 million cases with 55,961 hospital admissions and 1351 deaths annually. This underscores the urgent need for improved meat quality monitoring. This study aims to develop an electronic nose (E-nose) that can differentiate between frozen–thawed and fresh pork meat samples, thereby enhancing food safety. We designed the E-nose using MQ series gas sensor array with temperature and humidity sensors, and an Arduino Uno microcontroller. Sensors were calibrated for accurate data collection. An adaptive support vector machine (ASVM) was used for data classification. We evaluated the model’s accuracy using a confusion matrix. The ASVM model exhibited robust performance, achieving an accuracy of 88%. Its performance was evaluated with recall, F1 scores, and precision. To further enhance the model’s performance, future studies are mandated to integrate additional gas sensors, increase sample sizes, advance data preprocessing techniques, and explore different machine learning algorithms or ensemble methods. Full article

Condiments such as vinegar are made and fermented manually with the help of the human nose. We developed an electronic nose to classify pure Filipino vinegar varieties for automated vinegar classification. MQ sensors were used to determine the sensitivity of gas content of different vinegar flavors, namely, Sinamak, Pinakurat, and Iloko. Linear discriminant analysis was conducted for dimensionality reduction. A support vector machine (SVM) was employed to utilize the data gathered and accurately identify the varieties. 360 samples were included in the training dataset, while 108 samples were included in the testing datasets. The accuracy was 78.7%. Full article

This study describes a sensor system for continuous monitoring of volatile organic compounds (VOCs) emitted from small industrial facilities in urban centers, such as automobile paint facilities and printing facilities. Previously, intermittent measurements were made using expensive flame ionization detector (FID)-type instruments that were impossible to install, resulting in a lack of continuous management. This paper develops a low-cost sensor system for full-time management and consists of multi-sensor systems to increase the spatial resolution in the pipe. To improve the accuracy and reliability of this system, a new reinforced adaptive neuro fuzzy inference system (RANFIS) model with enhanced preprocessing based on the adaptive neuro fuzzy inference system (ANFIS) model is proposed. For this purpose, a smart sensor module consisting of low-cost metal oxide semiconductors (MOSs) and photo-ionization detectors (PIDs) is fabricated, and an operating controller is configured for real-time data acquisition, analysis, and evaluation. In the front part of the RANFIS, interquartile range (IQR) is used to remove outliers, and gradient analysis is used to detect and correct data with abnormal change rates to solve nonlinearities and outliers in sensor data. In the latter stage, the complex nonlinear relationship of the data was modeled using the ANFIS to reliably handle data uncertainty and noise. For practical verification, a toluene evaporation chamber with a sensor system for monitoring was built, and the results of real-time data sensing after training based on real data were compared and evaluated. As a result of applying the RANFIS model, the RMSE of the MQ135, MQ138, and PID-A15 sensors were 3.578, 11.594, and 4.837, respectively, which improved the performance by 87.1%, 25.9%, and 35.8% compared to the existing ANFIS. Therefore, the precision within 5% of the measurement results of the two experimentally verified sensors shows that the proposed RANFIS-based sensor system can be sufficiently applied in the field. Full article

With greenhouse gas emissions and climate change continuing to be major global concerns, researchers are increasingly focusing on reducing energy consumption as a key strategy to address these challenges. In recent years, various devices and technologies have been developed for residential buildings to implement energy-saving strategies and enhance energy efficiency. This paper presents a real-time IoT-based smart monitoring system designed to optimize energy consumption and enhance residents’ safety through efficient monitoring of home conditions and appliance usage. The system is built on a Raspberry Pi Model 4B as its core platform, integrating various IoT sensors, including the DS18B20 for temperature monitoring, the BH1750 for measuring light intensity, a passive infrared (PIR) sensor for motion detection, and the MQ7 sensor for carbon monoxide detection. The Adafruit IO platform is used for both data storage and the design of a graphical user interface (GUI), enabling residents to remotely control their home environment. Our solution significantly enhances energy efficiency by monitoring the status of lighting and heating systems and notifying users when these systems are active in unoccupied areas. Additionally, safety is improved through IFTTT notifications, which alert users if the temperature exceeds a set limit or if carbon monoxide is detected. The smart home monitoring device is tested in a university residential building, demonstrating its reliability, accuracy, and efficiency in detecting and monitoring various home conditions. Full article

Monitoring the quality of engine oil improves engine efficiency and reduces engine maintenance costs. Several methods have been proposed for this purpose; however, most of them take too long to test oil quality. This paper introduces a fast, simple, and accurate method to determine oil quality using an electronic nose and artificial intelligence. The TU5 engine and 10-40W “Behran Super Pishtaz” engine oil were used in the experiments. Tests were conducted at six different quality levels. Oil properties such as viscosity, density, flash point, and freezing point were measured at each level. Additionally, oil smell signals were recorded using an olfactory machine at these quality levels. The fraction method was employed to adjust the sensors’ responses. Five statistical features were extracted from each signal, and these features were used to train and test a support vector machine (SVM) for classifying oil quality using the five-fold cross-validation method. The results indicated a statistically significant change in viscosity and density with variations in oil quality. The density increased as the quality decreased. Viscosity, however, initially decreased and then increased at later stages. An analysis of the sensory outputs revealed that changes in oil quality also affected these outputs, with the most pronounced sensitivity observed in the MQ135 and MQ138 sensors. The final accuracies of the SVM in classifying oil quality were 68.22%, 85.86%, and 95.44% for linear, radial basis function (RBF), and polynomial kernels, respectively. The SVM sensitivities for oil qualities A, B, C, D, E, and F were 97.99%, 97.37%, 95.51%, 92.67%, 94.48%, and 94.59%, respectively. Full article

The need for safe and healthy air quality has become critical as urbanization and industrialization increase, leading to health risks and environmental concerns. Gas leaks, particularly of gases like carbon monoxide, methane, and liquefied petroleum gas (LPG), pose significant dangers due to their flammability and toxicity. LPG, widely used in residential and industrial settings, is especially hazardous because it is colorless, odorless, and highly flammable, making undetected leaks an explosion risk. To mitigate these dangers, modern gas detection systems employ sensors, microcontrollers, and real-time monitoring to quickly identify dangerous gas levels. This study introduces an IoT-based system designed for comprehensive environmental monitoring, with a focus on detecting LPG and butane leaks. Using sensors like the MQ6 for gas detection, MQ135 for air quality, and DHT11 for temperature and humidity, the system, managed by an Arduino Mega, collects data and sends these to the ThingSpeak platform for analysis and visualization. In cases of elevated gas levels, it triggers an alarm and notifies the user through IFTTT. Additionally, the system includes a microphone and a CNN model for analyzing audio data, enabling a thorough environmental assessment by identifying specific sounds related to ongoing activities, reaching an accuracy of 96%. Full article

Cloud Manufacturing enables the integration of geographically distributed manufacturing resources through advanced Cloud Computing and IoT technologies. This paradigm promotes the development of scalable and adaptable production systems. However, existing frameworks face challenges related to scalability, resource orchestration, and data security, particularly in rapidly evolving decentralized manufacturing settings. This study presents a novel nine-layer architecture designed specifically to address these issues. Central to this framework is the use of Apache Kafka for robust, high-throughput data ingestion, and Apache Spark Streaming to enhance real-time data processing. This framework is underpinned by a microservice-based architecture that ensures a high scalability and reduced latency. Experimental validation using sensor data from the UCI Machine Learning Repository demonstrated substantial improvements in processing efficiency and throughput compared with conventional frameworks. Key components, such as RabbitMQ, contribute to low-latency performance, whereas Kafka ensures data durability and supports real-time application. Additionally, the in-memory data processing of Spark Streaming enables rapid and dynamic data analysis, yielding actionable insights. The experimental results highlight the potential of the framework to enhance operational efficiency, resource utilization, and data security, offering a resilient solution suited to the demands of modern industrial applications. This study underscores the contribution of the framework to advancing Cloud Manufacturing by providing detailed insights into its performance, scalability, and applicability to contemporary manufacturing ecosystems. Full article

Two-dimensional molybdenum disulfide (MoS₂) exhibits interesting properties for applications in micro and nano-electronics. The key point for sensing properties of a device is the quality of the material’s surface. In this study, MoS₂ layers were deposited on polymers by pulsed laser deposition (PLD). This process was monitored by a mass quadrupole spectrometer to record the emissions of MoS₂ and evaluate the amount of molybdenum and sulfur compounds generated. The changes in laser parameters during the PLD strongly affect the properties of the formed MoS₂ film. The exploration of the composition and structure of the films was followed by Attenuated Total Reflectance–Fourier Transform Infrared (ATR-FTIR), Atomic Force Microscopy (AFM), and mass quadrupole spectrometer (MQS). The possible application of the fabricated composite as a sensor is preliminarily considered. Full article

An advanced IoT-based Liquefied Petroleum Gas (LPG) cylinder monitoring and safety system is presented in this work. The proposed technique provides continuous monitoring of residential gas usage and detects any potential leakage. It utilizes an MQ135 gas sensor for gas leakage detection, a load cell to monitor the weight of the cylinder, and a DHT22 sensor for temperature sensing. The sensors are mounted on a customized trolley for domestic LPG cylinders. All the sensors are connected to a NodeMCU microcontroller, which exchanges sensor data with a cloud platform using HTTP GET and POST methods to transmit the data to a cloud-based MySQL database. Unlike other existing methods, the proposed approach does not necessitate any modifications to the existing setup, which includes the gas cylinder, regulating valve, and distribution pipe. Furthermore, a mobile application that emphasizes the needs of the user is developed to enable a wider range of functionalities using cloud data collected from the sensors. The software facilitates the real-time monitoring of gas levels, provides comprehensive usage records for daily, weekly, and monthly intervals, issues immediate alarms in the event of gas leakage and low gas levels, and detects any unauthorized movement of the LPG cylinder, such as theft. The proposed technique not only improves user safety but also streamlines gas cylinder management with predictive analytics based on gas consumption trends and projected days of usage. Moreover, the application includes functionality that automatically orders a new cylinder with the vendor when the gas level drops below a predetermined threshold, therefore ensuring continuous availability of gas supply. Full article

Air pollution is a growing concern in rural areas where agricultural production can be reduced by it. This article analyses data obtained as part of a research project. The aim of this study is to understand the influence of atmospheric pressure, air temperature, air relative humidity, longitude and latitude of the location, and indoor and outdoor environment on local rural workplace diversity of air pollutants such as carbon monoxide (CO) and suspended particulate matter (SPM), as well as the contribution of these variables to changes in such air pollutants. The focus is on four topics: motivation, innovation and creativity, leadership, and social responsibility. Furthermore, this study developed an artificial neural network (ANN) model to predict CO and SPM concentrations in the air based on data collected from the mentioned inputs. The related sensors were assembled on an Arduino Mega 2560 board to form a field-portable device to detect air pollutants and meteorological parameters. The sensors included an MQ7 sensor for CO concentration measurement, a Sharp GP2Y1010AU0F dust sensor for SPM concentration measurement, a DHT11 sensor for air temperature and air relative humidity measurement, and a BMP180 sensor for air pressure measurements. The longitude and latitude of the location were measured using a smartphone. Measurements were conducted from 20 December 2021 to 16 July 2022. Results showed that the overall average outdoor CO and SPM concentrations were 10.97 ppm and 231.14 μg/m³ air, respectively. The overall average indoor concentrations were 12.21 ppm and 233.91 μg/m³ air for CO and SPM, respectively. Results showed that the ANN model demonstrated acceptable performance in predicting CO and SPM in both the training and testing phases, exhibiting a coefficient of determination (R²) of 0.575, a root mean square error (RMSE) of 1.490 ppm, and a mean absolute error (MAE) of 0.994 ppm for CO concentrations when applying the testing dataset. For SPM concentrations, the R², RMSE, and MAE using the test dataset were 0.497, 30.301 μg/m³ air, and 23.889 μg/m³ air, respectively. The most influential input variable was air pressure, with contribution rates of 22.88% and 22.82% in predicting CO and SPM concentrations, respectively. The acceptable performance of the developed ANN model provides potential advances in air quality management and agricultural planning, enabling a more accurate and informed decision-making process regarding air pollution. The results of short-term estimation of CO and SPM concentrations suggest that the accuracy of the ANN model needs to be improved through more comprehensive data collection or advanced machine learning algorithms to improve the prediction results of these two air pollutants. Moreover, as even lower cost devices can predict CO and SPM concentrations, this study could lead to the development some kind of virtual sensor, as other air pollutants can be estimated from measurements of particulate matters. Full article