IoT-based Data Research Articles

Design of a low-cost IoT-based data logger for monitoring the palm sugar drying system using ESP32 and Modbus/MQTT conversion

Design of a low-cost IoT-based data logger for monitoring the palm sugar drying system using ESP32 and Modbus/MQTT conversion

Advancing Small and Medium-Sized Enterprise Manufacturing: Framework for IoT-Based Data Collection in Industry 4.0 Concept

Currently, industrial engineers are witnessing a rapid development of innovation in modern industry and the integration of critical elements of Industry 4.0 into production processes in order to remain competitive. Such changes are characterized by a large amount of effort and financial resources. To successfully deploy these changes requires not only the purchase of hardware and software but also the training of employees and the modification of the company’s organizational structure. The main objective of the article is to propose a framework for the modernization of SMEs to a level close to the Smart Factory by using the necessary attributes of Industry 4.0. The framework design is based on the initial state of a standard SME and consists of the design of fitting new IoT devices for efficient data collection, the design of a data warehouse for storing process data using Hadoop, and the integration of process- and operational-level data into the prepared data warehouse. The resulting design is developed in the form of a methodology and is generalized for use in manufacturing enterprises. The universal design is independent of the initial state of the enterprise.

IoT-Based Data Mining Framework for Stability Assessment of the Laser-Directed Energy Deposition Process

Additive manufacturing processes are prone to production errors. Specifically, the unique physical conditions of Laser-Directed Energy Deposition (DED-L) lead to unexpected process anomalies resulting in subpar part quality. The resulting costs and lack of reproducibility are two major barriers hindering a broader adoption of this innovative technology. Combining sensor data with data from relevant steps before and after the production process can lead to an increased understanding of when and why these process anomalies occur. In the present study, an IoT-based data mining framework is presented to assess the stability of processing Ti6Al4V on an industrial-grade DED-L machine. The framework employs an edge-cloud computing methodology to collect data efficiently and securely from various steps in the part lifecycle. During manufacturing, multiple sensors are employed to monitor the essential process characteristics in situ. Mechanical properties of the 160 printed specimens were obtained using appropriate destructive testing. All data are stored on a central database and can be accessed via the web for data analytics. The results prove the successful implementation of the proposed IoT framework but also indicate a lack of process stability during manufacturing. The occurring part errors can only be partially correlated with anomalies in the in situ sensor data.

Carbon based sensors for air quality monitoring networks; middle east perspective.

IoT-based Sensors networks play a pivotal role in improving air quality monitoring in the Middle East. They provide real-time data, enabling precise tracking of pollution trends, informed decision-making, and increased public awareness. Air quality and dust pollution in the Middle East region may leads to various health issues, particularly among vulnerable populations. IoT-based Sensors networks help mitigate health risks by offering timely and accurate air quality data. Air pollution affects not only human health but also the region's ecosystems and contributes to climate change. The economic implications of deteriorated air quality include healthcare costs and decreased productivity, underscore the need for effective monitoring and mitigation. IoT-based data can guide policymakers to align with Sustainable Development Goals (SDGs) related to health, clean water, and climate action. The conventional monitor based standard air quality instruments provide limited spatial coverage so there is strong need to continue research integrated with low-cost sensor technologies to make air quality monitoring more accessible, even in resource-constrained regions. IoT-based Sensors networks monitoring helps in understanding these environmental impacts. Among these IoT-based Sensors networks, sensors are of vital importance. With the evolution of sensors technologies, different types of sensors materials are available. Among this carbon based sensors are widely used for air quality monitoring. Carbon nanomaterial-based sensors (CNS) and carbon nanotubes (CNTs) as adsorbents exhibit unique capabilities in the measurement of air pollutants. These sensors are used to detect gaseous pollutants that includes oxides of nitrogen and Sulphur, and ozone, and volatile organic compounds (VOCs). This study provides comprehensive review of integration of carbon nanomaterials based sensors in IoT based network for better air quality monitoring and exploring the potential of machine learning and artificial intelligence for advanced data analysis, pollution source identification, integration of satellite and ground-based networks and future forecasting to design effective mitigation strategies. By prioritizing these recommendations, the Middle East and other regions, can further leverage IoT-based systems to improve air quality monitoring, safeguard public health, protect the environment, and contribute to sustainable development in the region.

IoT-Enhanced local attention dual networks for pathological image restoration in healthcare

High-resolution pathological images play a pivotal role in accurate disease diagnosis and are important in precision medicine. However, obtaining real-time high-resolution images faces challenges due to hardware limitations and scanning time constraints. Conventional image super-resolution restoration algorithms struggle to provide satisfactory results for pathological images, resulting in blurred and unrealistic restorations. In response to this challenge, this research proposes a pioneering approach by integrating Internet of Things (IoT) technology with Local Attention Dual Network (IOT-LAT) for super-resolution restoration of pathological images. The enhanced IOT-LATincorporates IoT-based data acquisition and processing, IoT-Enhanced non-local attention mechanisms, Gaussian constraint, and parameter-sharing strategies in up-sampling and down-sampling branches. This integration enables real-time super-resolution restoration of pathological images with improved accuracy. The reconstructed images exhibit a structural similarity of 0.914 and a peak signal-to-noise ratio of 30.84 dB. These results validate the effectiveness of the suggested approach in precisely reconstructing high-frequency details and improving modelling efficiency via a lightweight non-local attention mechanism enhanced by the Internet of Things. This work discusses IoT and non-local attention dual networks to improve pathological image super-resolution restoration. This allows for faster and more accurate disease diagnosis and treatment in precision medicine.

Enhancing 5G communication in business networks with an innovative secured narrowband IoT framework

Abstract These days, 5G mobile technology highly influences the wireless communication process, which is more responsive than the other generation. However, the 5G communication process handles the speed and latency issues while transmitting data over the network. Especially in rural areas, most people face this speed and latency problem while connecting to the network. Therefore, the network uses massive amounts of data to analyse this problem by integrating 5G with the Internet of things (IoT). The IoT-based data transmission and collection helps manage the data capacity, connection, and transmission rate. Therefore, this study uses the novel secured narrowband IoT framework for 5G communication and operational goals based on their position in the market. The 5G network uses business connectivity and access information to manage the data transmission. The network uses cellular bandwidth and IoT devices to connect more during this process. According to the connection, the information is transmitted with the minimum delay. Thus, experimental results have proposed these parameters: an accuracy ratio of 92.5%, a security ratio of 94.4%, a reliability ratio of 93.5%, a latency ratio of 91.8%, a cost-effective ratio of 20.5%, and a throughput ratio of 25.4% compared to other methods.

XAI-empowered IoT multi-sensor system for real-time milk adulteration detection

In response to the critical issue of milk adulteration jeopardizing both the nutritional integrity of milk and the health of consumers, this paper presents an innovative Artificial Intelligence (AI) enabled Internet of Things (IoT) based multi-sensor system. The escalating consumption of milk as a pivotal nutritional source necessitates robust measures to ensure its safety and quality. Traditional methods of detecting adulteration have shown limitations, prompting the development of an automated and advanced approach. The proposed system integrates various sensors capable of real-time measurement, including pH, electrical conductivity (EC), temperature, gas parameters, and Volatile Organic Compounds (VOC) parameters. This comprehensive approach extends to measuring key constituents of milk samples like Fat, Protein, Solids Not Fat (SNF), Lactose, and Gravity values. To address specific adulterants—Urea, Starch, Sodium Bicarbonate, Maltodextrin, and Formaldehyde—a machine learning-based ensemble technique is employed for classification. This ensemble method outperforms conventional algorithms like RF, Light GBM, and Extra Trees Classifiers, achieving an impressive 96% accuracy rate in detecting adulterants within the milk dataset. The pivotal contribution of this study lies in the development of an IoT-based data acquisition device that seamlessly integrates with the sensor system, enabling efficient and precise measurements. Additionally, XAI is used to analyse the results obtained by the proposed model. For this, a framework called SHAP (SHapley Additive exPlanations) analysis is employed to elucidate the decision-making process of the ensemble model, enhancing the interpretability of results. By virtue of its real-time monitoring capabilities and accurate classification, the AI-enabled IoT-based multi-sensor system emerges as a promising solution for addressing milk adulteration. This innovation holds the potential to bolster milk quality control measures in the dairy industry. The system's ability to swiftly detect and categorize adulterants underscores its significance in combating the pervasive issue of compromised milk quality, thereby ensuring consumer safety and fostering industry integrity.

Integration of data science with the intelligent IoT (IIoT): current challenges and future perspectives

The Intelligent Internet of Things (IIoT) involves real-world things that communicate or interact with each other through networking technologies by collecting data from these “things” and using intelligent approaches, such as Artificial Intelligence (AI) and machine learning, to make accurate decisions. Data science is the science of dealing with data and its relationships through intelligent approaches. Most of the state-of-the-art focus is on the topic independently, either on data science or IIoT. Therefore, to address the gap, this article provides a comprehensive survey on the advances and integration of data science with the Intelligent IoT (IIoT) system by classifying the existing IoT-based data science techniques and presenting a summary of various characteristics. The paper analyzes the data science or big data security and privacy features, including network architecture, data protection, and continuous monitoring of data, which face challenges in various IoT-based systems. Extensive insights into IoT data security, privacy, and challenges are visualized in the context of data science for IoT. In addition, this study reveals the current opportunities to enhance data science and IoT market development. The current gap and challenges faced in the integration of data science and IoT are comprehensively presented, followed by the future outlook and possible solutions to the existing challenges.

IoT-based data and analytic hierarchy process to map groundwater recharge with stormwater.

The sustainable management of groundwater resources in developing countries is often challenging due to limited measurement and monitoring infrastructure to collect data necessary for decision support. To make a contribution towards addressing these challenges, this study investigated the use of Internet of Things (IoT) technology and low-cost sensors to collect the required groundwater-level data and develop a model to map the recharge potential with stormwater. The study focused on two stormwater ponds located in a highly urbanised area in Cape Town, South Africa. A combination of Geographic Information System and analytic hierarchy process was integrated to generate a groundwater recharge potential zone map of the study area. The IoT-based data were used to develop and calibrate a numerical groundwater model in MODFLOW. The study determined that retrofitted stormwater ponds are potential groundwater augmentation zones and can provide opportunity for stormwater recharge in urban areas. Overall, this study highlights the potential of IoT to collect hydrogeological data with low-cost sensors. Data can be collected at high temporal resolution, and the spatial scale can be increased due to availability of low-cost sensors.

Occupant behavior, thermal environment, and appliance electricity use of a single-family apartment in China

The household is the basic unit of a residential community or building. High-resolution, long-term open data are necessary to help study residential electricity consumption, smart home technologies, and electricity flexibility technologies at household level. This paper introduces an IoT-based data collection platform (IDCP) consisting of gateways, sensors, and cloud servers. This platform can collect data on the occupant presence, indoor environment, window-opening states, and appliance electricity consumption simultaneously. This study deployed the IDCP in a single-family apartment in Beijing, China, and compiled a dataset, namely, CN-OBEE, including data on the per-minute occupant behavior, thermal environment, and appliance electricity use of the apartment for an entire year (from May 31, 2021, to May 31, 2022), and hourly meteorological data collected by the nearest national weather station during the same period. This dataset is the first detailed and publicly available occupant behavior and electricity use dataset for Chinese homes. As a regional feature, the dataset compiled by this study includes window-opening behavior and the use of split air conditioners (ACs).

Internet of Things Enabled Based Arrhythmia Classification using Dandelion Optimization Algorithm with Ensemble Learning

Internet of Things (IoT) based Arrhythmia Classification is a cutting-edge algorithm that amalgamates the abilities of the IoT and advanced medical diagnosis to revolutionize the detection and classification of arrhythmias—irregular heartbeats that may indicate fundamental cardiovascular issues. This technique leverages IoT devices, namely connected health monitors and wearable sensors, to continuously gather electrocardiogram (ECG) information from individuals. This information, streamed in real-time, provides a great opportunity for timely and remote monitoring of cardiac health. Leveraging the abilities of deep learning and IoT, this technique provides an automated and more sophisticated means of classifying and detecting arrhythmias, improving the efficiency and accuracy of diagnoses. This article presents an Internet of Things Enabled Based Arrhythmia Classification using the Dandelion Optimization Algorithm with Ensemble Learning (AC-DOAEL) method. The presented AC-DOAEL technique utilizes IoT-based data collection with an ensemble learning-based classification process. For the arrhythmia detection and classification process, the AC-DOAEL technique follows an ensemble learning algorithm such as long short-term memory (LSTM), autoencoder (AE), and bidirectional LSTM (BiLSTM) models. To improve the recognition rate of the ensemble models, the AC-DOAEL technique uses DOA as a hyperparameter optimizer. The simulation outcomes of the AC-DOAEL method are well-studied on benchmark ECG data. The experimental result analysis inferred the greater performance of the AC-DOAEL algorithm with other techniques.

Filtering Big Data with Optimized Hybrid Algorithm for IoT-Based Data Selection

Data management across servers has grown problematic because of technological advancements in data processing and storage capacities. Data that is neither organized nor labelled adds an additional layer of difficulty to the storing and retrieving processes. This data, which is not tagged, requires analytic techniques that are more powerful and time efficient. Clustering has long been regarded as one of the most effective methods for managing large amounts of data; nonetheless, larger volumes can lead to unexpectedly poor accuracy when using conventional clustering methodologies. In this study, we suggest the use of a novel framework for the clustering of large amounts of data. The preprocessing stage is one of the most important parts in the data cleansing process; hence, a global stop-word list is used to filter the contents of the files before sending them on to the cluster distribution stage. A meta-heuristic focused Genetic Algorithm (GA) is utilized to eradicate the redundant information present in the datasets. In addition to the generalized attributable fitness function, an attribute-based innovative fitness function (f) is being developed. To determine how well proposed method performs, it is compared to a variety of alternative clustering approaches. When comparing the distributions of clusters for the purpose of evaluation, the Standard Error (SE), root mean squared error (RMSE), and corrected R squared error are all computed.

Secure-Tech Triad Enhancing Electronic Voting System Security through Integrated Blockchain, AI, and IoT Technologies

As electronic voting systems become increasingly prevalent, the urgent need for robust security measures to combat evolving cyber threats has never been more critical. This paper introduces a ground-breaking architectural framework Secure-Tech Triad that synergistically combines Blockchain technology with Machine Learning (ML) algorithms and Internet of Things (IoT) capabilities to enhance the security and efficiency of electronic voting systems. This architectural framework utilizes a modified Proof-of-Stake (PoS) Blockchain algorithm, a Random Forest ML model for real-time anomaly detection, and an MQTT protocol for IoT-based data collection to create a more secure, efficient, and responsive voting environment. Rigorous testing and evaluation show that the integrated framework significantly outperforms existing Blockchain-only solutions in key performance indicators, such as security breach detection rate, system latency, and cost efficiency. This integrated approach is the best-performing model, achieving a 97% security breach detection rate, a 30% reduction in system latency (down to 2.3 seconds), and a 25% decrease in operational costs. These results underscore the combined effectiveness of Blockchain, AI, and IoT in enhancing security, speed, and cost-effectiveness. Specifically, the Random Forest algorithm has been instrumental in achieving an exceptional security breach detection rate, while IoT data collection has played a pivotal role in enabling real-time anomaly detection and proactive threat mitigation.

IoT-based automatic deep learning model generation and the application on Empty-dish Recycling Robots

As a modern computer vision technology, deep learning (DL) revolutionizes our lives and reshapes our world with its high performance. Usually, to accomplish each task, researchers need to collect the dataset and modify the basic DL model to optimize the model. In addition, collecting the dataset is time-consuming, and building separate Artificial Intelligence (AI) models for each task is significantly ineffective Therefore, AI research faces challenges in realizing effective data collection and DL model generation. To solve these problems, we design an Internet of Things (IoT)-based automatic DL model generation strategy which also includes an IoT-based data collection method. In detail, we design an Android application that captures images and sends them to a cloud server to create a dataset. Additionally, we apply a two-level checking function to filter out anomalous data and ensure the dataset’s correctness. Next, the cloud server uses the dataset to train and generate a DL model automatically. We have prepared the state-of-the-art DL components in the cloud server and propose an automatic DL model creation process for model generation. We have applied the proposal to Empty-dish Recycling Robots for demonstration and evaluation purposes to easily understand the proposal and measure the performance. The experimental results show that the system successfully collects the dataset and automatically generates the DL model. Furthermore, the checking function deployed on Android devices requires only 0.84 MB and achieves 99.86% accuracy. During training, the time spent on each automatic DL model generation is evidently decreased by about 9.00% to 28.00%.

Water contamination analysis in IoT enabled aquaculture using deep learning based AODEGRU

Water contamination presents a significant challenge in aquaculture, impacting the sustainability of ecosystems and the health of aquatic organisms. Precisely assessing water contamination levels is crucial for effective monitoring and safeguarding aquatic life within the aquaculture industry. Traditional methods for evaluating water contamination are characterized by their costliness, time-consuming nature, and susceptibility to errors. Integrating computer technologies such as Artificial Intelligence (AI), the Internet of Things (IoT), and Data Analytics offers promising potential in addressing this issue. Nevertheless, current deep learning solutions have limitations related to data variability, interpretability, and performance. To address these limitations, this study proposes a comprehensive framework that incorporates IoT-based data collection and data segregation techniques to enhance the accuracy of water contamination classification in aquaculture. Real-time data collected through IoT devices, encompassing parameters like temperature, pH levels, dissolved oxygen, nitrate concentration, and other water quality indicators, enables a holistic evaluation of water quality. By considering predefined acceptable ranges for aquatic life, this framework calculates a water contamination index, facilitating the classification of data into categories such as contaminated and non-contaminated. To ensure robust classification, the study introduces an innovative attention-based model known as the Ordinary Differential Equation Gated Recurrent Unit (AODEGRU). This attention mechanism directs the model's focus towards salient features associated with water contamination, while the AODEGRU architecture captures temporal patterns within the data. Experimental results underscore the effectiveness of the proposed model. It demonstrates its superiority with high performance, achieving an accuracy rate of approximately 98.69% on a publicly available dataset and an impressive 99.89% accuracy on a real-time dataset, clearly outperforming existing methodologies.

The application of traditional Chinese cultural elements in urban street landscape using the Internet of Things and deep learning

The street design and landscape in China include cultural elements representing the Heritage and history of this generation. Such designs are planned, fabricated, and implemented based on previous elements and novel findings from the past. The novel findings are identified using sophisticated technologies like the Internet of Things (IoT). Therefore, this article introduces a Cultural Design Planning Method (CDPM) for Street Landscape (SL) in maintaining the renowned Heritage of Chinese roads. The proposed method relies on IoT-based data and cultural elements from the previous design and its impact on society. In this case, the impact is computed using attraction and cultural progression from the tourists and location. The cultural element’s connectivity and resemblance to the current location display the cultural progression. Such progression and impacts are recurrently validated using deep learning; the learning process identifies the elements and their associated impact on society. The previous and current street designs are augmented in the learning process to leverage placement and street design precision. The landscapes are periodically validated based on the current trends and associations.

Power Consumption Minimization of a Low-Cost IoT Data Logger for Photovoltaic System

This paper introduces an innovative IoT-based data logger for photovoltaic (PV) system monitoring, emphasizing low power consumption and affordability. The system comprises a PV panel, a charging controller, and a backup battery, focusing on monitoring their voltages and currents through a network of voltage and current sensors. Data is stored on an SD card and displayed in real-time on a web server. The FireBeetle 2 ESP32-E microcontroller, chosen for its efficient deep-sleep mode power management, is central to the data logger's design. This study employs several low-power strategies, notably reducing supply voltage and CPU frequency to decrease power consumption significantly. A data buffering mechanism stores sensor readings in the microcontroller's flash memory, transferring them to the SD card hourly to balance power efficiency and data security. Wi-Fi connection intervals are optimized to 45 seconds, balancing power use and system monitoring frequency. The data logger averages a power consumption of 122.78 mW and demonstrates its efficacy against the commercial DI-145 model. Priced at C$ 55.05, the system is both cost-effective and scalable, capable of monitoring multiple PV panels and batteries, reducing per-unit costs. The study underscores the successful integration of affordability, low-power operation, and efficient monitoring in a PV system data logger, showcasing its potential in future renewable energy research.

Real-time air quality monitoring in Bull Trench Kiln-based Brick industry by calibrating sensor readings and utilizing the Serverless Computing

Bull Trench Kiln-based Brick industries are major contributors of Particulate Matter Pollutants (PM2.5 and PM10), which leads to deterioration of air quality, and hence needs to be monitored. As the static air monitoring stations require huge infrastructure and are sparsely located, it is necessary to find alternate ways using recent technologies like IoT, Machine Learning, and Serverless Computing. IoT devices being low cost and portable could be deployed near brick industries, however, they are sensitive to fluctuating ambient air temperature or humidity and hence have to be calibrated and there is a need for powerful back-end infrastructures for storage and analyses of data. The existing approach of transmitting IoT-based data to the cloud is time-consuming, energy inefficient, prone to scaling problems, has poor resource utilization, and is expensive. In this study, the IoT data are calibrated using ML Classifier algorithms and an Automatic Function Triggerer - Function-as-a-Service (AFT-FaaS) method is proposed, which is based on event-driven, Serverless method using AWS Lambda along with the Three-Tier Serverless architecture (Edge Tier, Fog Tire, and Cloud Tier) to deal with the challenges of delay, under-resource allocation, and to reduce the expenditures. The data transmitted to the cloud using the proposed AFT-FaaS method are indexed in ElasticSearch and Kibana is used for the analysis and visualization of data. While performing calibration, Random Forest Classifier was chosen as it had accuracy of above 98%. Finally, an in-depth pricing analysis is performed to better understand the difference between different configurations deployed for Serverless and non-Serverless Computing methods, which shows there is a significant reduction (7 times) of expenses incurred while using Serverless Computing than the non-Serverless Computing methods.

IoT-based data quality and data preprocessing of multinational corporations

Increasing numbers of devices that output large amounts of geographically referenced data are being deployed as the Internet of Things (IoT) continues to expand. Partly as a result of the IoT's dynamic, decentralized, and heterogeneous architecture. These are all examples of the Internet of items (IoT), despite the fact that we might be thinking that one of these items is different from the others. The physical and digital worlds are connected by the Internet of Things (IoT). Nowadays, one of the key goals of the Internet is its own development. This paper provides an in-depth analysis of IoT-based data quality and data preparation strategies developed with multinational corporations in mind. The goal is to make IoT data more trustworthy and practical so that MNCs may use it to their advantage in making educated business decisions. The proposed structure consists of three distinct actions: gathering data, evaluating data quality, and cleaning up raw data. Data preprocessing research is essential since it decides and significantly affects the accuracy of predictions made in later stages. Thus, the recommendation for a special and useful combination in the framework of different data preprocessing task types, which includes the following four technical elements and is briefly justified, is made. The Internet of Things (IoT) is a design pattern in which commonplace items can be equipped with classification, sensing, networking, and processing capabilities that will enable them to communicate with one another over the Internet to fulfill a specific function. The Internet of Things will eventually change physical objects into virtual objects with intelligence. In addition to a detailed analysis of the IoT layer, this article gives an overview of the existing Internet of Things (IoT), technical specifics, and applications in this recently growing field. However, this publication will provide future scholars who desire to conduct study in this area of Internet of Things with a better knowledge.

Internet of Things Assisted Solid Biofuel Classification Using Sailfish Optimizer Hybrid Deep Learning Model for Smart Cities

Solid biofuels and Internet of Things (IoT) technologies play a vital role in the development of smart cities. Solid biofuels are a renewable and sustainable source of energy obtained from organic materials, such as wood, agricultural residues, and waste. The integration of IoT technology with solid biofuel classification can improve the performance, quality control, and overall management of biofuel production and usage. Recently, machine learning (ML) and deep learning (DL) models can be applied for the solid biofuel classification process. Therefore, this article develops a novel solid biofuel classification using sailfish optimizer hybrid deep learning (SBFC-SFOHDL) model in the IoT platform. The proposed SBFC-SFOHDL methodology focuses on the identification and classification of solid biofuels from agricultural residues in the IoT platform. To achieve this, the SBFC-SFOHDL method performs IoT-based data collection and data preprocessing to transom the input data into a compatible format. Moreover, the SBFC-SFOHDL technique employs the multihead self attention-based convolutional bidirectional long short-term memory model (MSA-CBLSTM) for solid biofuel classification. For improving the classification performance of the MSA-CBLSTM model, the SFO algorithm is utilized as a hyperparameter optimizer. The simulation results of the SBFC-SFOHDL technique are tested and the results are examined under different measures. An extensive comparison study reported the betterment of the SBFC-SFOHDL technique compared to recent DL models.