Real-time Sensor Data Research Articles

IoT-Integrated Home Energy Management System with Real-Time Monitoring and Solar Panel Optimization

In this study, an IoT-integrated Home Energy Management System (HEMS) was developed using solar panels as the primary energy source. The system employs an ESP32 microcontroller as the core controller, equipped with DHT22, LDR, and INA219 sensors to monitor temperature, humidity, light intensity, voltage, and current. Real-time sensor data is presented on a web interface, allowing users to monitor system status and control devices like fans and lights either manually or automatically. The system demonstrated stable performance with a control response time of under one second and effective energy management aligned with environmental conditions. However, a key limitation was the limited capacity of the 10 Wp solar panel, particularly during low sunlight periods. To address this, enhancements such as improved load management or increased solar panel capacity are recommended. The system successfully implemented real-time monitoring and automated control, activating the fan at temperatures above 30 degrees Celsius and turning on lights when light intensity is below 1000 lux. This research highlights the potential of IoT technology in achieving efficient and sustainable home energy management.

Artificial Intelligence (AI) to Build Climate Models to Improve Weather Forecasting as Torrential Rains, Floods and Droughts Proliferate Across the Vast Country

Global warming has triggered more intense clashes of weather systems in India in recent years, increasing extreme weather events, which the independent Centre for Science and Environment estimates have killed nearly 3,000 people this year. Weather agencies around the world are focussing on AI, which can bring down cost and improve speed, and with a recent Google-funded model found to have outperformed conventional methods. Accurate weather forecasting is particularly crucial in India, a country of 1.4 billion people, many impoverished, and the world's second-largest producer of rice, wheat and sugar Using AI with an expanded observation network could help generate higher-quality forecast data at lower cost. The increasing frequency and intensity of torrential rains, floods, and droughts due to climate change present significant challenges to weather forecasting and disaster preparedness. Artificial Intelligence (AI) offers transformative potential in building advanced climate models to address these challenges. By integrating vast datasets, including satellite imagery, historical weather records, and real-time sensor data, AI enhances the accuracy and resolution of climate predictions. Machine learning algorithms can identify complex patterns, simulate localized weather phenomena, and predict extreme weather events with greater precision. This paper explores the role of AI-driven climate models in improving weather forecasting, enabling proactive responses to mitigate the impacts of climate-induced disasters across diverse regions. Enhanced forecasting through AI not only safeguards lives and livelihoods but also supports sustainable resource management in the face of escalating climate uncertainties

Intelligent Valve Control Mechanism for Irrigation in Precision Agriculture

Precision agriculture, powered by IoT and machine learning, offers a promising solution to optimize water usage and enhance crop yields. This research presents an innovative Intelligent Irrigation System (IIS) that leverages real-time sensor data, predictive analytics, and mobile app interaction to achieve efficient and sustainable water management. Unlike traditional systems, our IIS employs a Raspberry Pi to collect critical data, including soil moisture, temperature, humidity, and rainfall. Advanced machine learning algorithms, such as Random Forest, analyze this data to accurately predict irrigation needs. The system's predictive capabilities enable precise valve control, delivering water to plants based on their specific requirements.By integrating IoT and machine learning, our IIS surpasses the limitations of existing solutions. It empowers farmers to make informed decisions, reduce water waste, and improve crop productivity. The system's user-friendly mobile app interface facilitates remote monitoring and control, enhancing accessibility and convenience. Our research aligns with the findings of previous studies (Abuzanouneh et al., 2022),(S, n.d.) (Water and Agriculture in India, n.d.)which demonstrate the potential of IoT-based irrigation systems in optimizing water usage. However, our IIS distinguishes itself by incorporating a comprehensive approach that combines sensor data, predictive analytics, and mobile app interaction. By adopting this intelligent irrigation solution, farmers can contribute to sustainable agriculture and address the challenges posed by climate change and water scarcity.

A novel hybrid deep learning model for real-time monitoring of water pollution using sensor data

This study developed a hybrid model for predicting dissolved oxygen (DO) using real-time sensor data for thirteen parameters. This novel hybrid model integrated one-dimensional convolutional neural networks (CNN) and long short-term memory (LSTM) to improve the accuracy of prediction for DO in water. The hybrid CNN-LSTM model predicted DO concentration in water using soft sensor data. The primary input parameters to the model were temperature, pH, specific conductivity, salinity, density, chlorophyll, and blue-green algae. The model used 38,681 water quality data for training and testing the hybrid deep learning network. The training procedure for the model was successful. The training and test losses were both nearly zero and within a similar range. With a coefficient of determination (R2) of 0.94 and a mean squared error (MSE) of 0.12, the hybrid model indicated higher performance compared to the classical models. The normal distribution of residual errors confirmed the reliability of the DO predictions by the hybrid CNN-LSTM model. Feature importance analysis indicated pH as the most significant predictor and temperature as the second important predictor. The feature importance scores based on extreme gradient boosting (XGBoost) for the pH and temperature were 0.76 and 0.12, respectively. This study indicated that the hybrid model can outperform the classical machine learning models in the real-time prediction of DO concentration.

Real-Time Soil Nutrient Monitoring Using NPK Sensors: Enhancing Precision Agriculture

Prediction of various parameters in the agriculture field using sensors is a significant topic nowadays. However, in many scenarios, the sensor data does not accurately detect the real parameter(/s) in the agriculture field. The sensor data may vary due to various external factors, whereas the real parameters don’t vary too much for a particular agriculture field. The present work introduces a modified neural network approach to predict real agricultural parameters from sensor data with accuracy caused by several external factors and demonstrates enhanced predictive accuracy and adaptability. The neural network takes the sensor data as input in various weather conditions and tries to find out the original real parameters of that sensor data. The real-time sensor data was collected from multiple agricultural sites. The results demonstrated high predictive accuracy, with the neural network outperforming traditional statistical methods in forecasting soil moisture and other vital variables. Additionally, the model’s ability to generalize across different environmental conditions enhances its applicability in various crop management scenarios. The study concludes that neural networks hold significant potential for improving the efficiency of smart agriculture systems by providing timely, data driven insights for farmers and agronomists. Further research will explore the integration of deep learning models and edge computing to enhance scalability and realtime responsiveness in field applications. The aforementioned research highlights the significance of NPK sensors in sustainable farming methods, namely in enabling accurate nutrient management via real-time data.

Optimized Data Transmission and Signal Processing for Telepresence Suits in Multiverse Interactions

With the rapid development of the metaverse, designing effective interfaces in virtual and augmented environments presents significant challenges. Additionally, keeping real-time sensory data flowing from users to their virtual avatars in a seamless and accurate manner is one of the biggest challenges in this domain. To this end, this article investigates a telepresence suit as an interface for interaction within the metaverse and its virtual avatars, aiming to address the complexities of signal generation, conversion, and transmission in real-time telepresence systems. We model a telepresence suit framework that systematically generates state data and transmits it to end-points, which can be either robotic avatars or virtual representations within a metaverse environment. Through a hand movement study, we successfully minimized the volume of transmitted information, reducing traffic by over 50%, which directly decreased channel load and packet delivery delay. For instance, as channel load decreases from 0.8 to 0.4, packet delivery delay is reduced by approximately half. This optimization not only enhances system responsiveness but also improves accuracy, particularly by reducing delays and errors in high-priority signal paths, enabling more precise and reliable telepresence interactions in metaverse settings.

Positioning Sensor Data Mining and Correlation Analysis Based on Multi-mode Radio Frequency Identification

Real-time positioning and sensor data mining are critical for various Internet of things (IoT) applications, but current approaches face challenges such as limited accuracy, high cost, and energy inefficiency. This paper proposes a novel fusion intelligent structure that combines radio frequency identification (RFID) technology and wireless sensor networks (WSN) to enable accurate and efficient positioning and data mining. The proposed method consists of two key components: an energy heterogeneity strategy for optimizing network energy consumption, and a data association feature analysis technique for mining sensor data. The results demonstrated that the proposed approach achieved superior positioning accuracy compared to other algorithms, with an average error of less than 0.300 under various conditions. The data detection accuracy was also high, with a maximum false detection rate of 3%. Furthermore, the proposed energy heterogeneity strategy significantly improved network energy utilization and stability. Real-world scenario testing validated the practicality of the proposed approach, with an average positioning error of less than 0.500 meters. These results validate the effectiveness of the proposed RFID-WSN fusion structure for real-time positioning and sensor data mining, providing a promising solution for IoT applications.

Artificial intelligence diagnosis and analysis in roto-dynamic machinery faults recognition using AI techniques

In the domain of industrial machinery, predictive maintenance plays a pivotal role in minimizing downtime and optimizing operations. This comprehensive research explores an innovative approach to predictive maintenance by harnessing the vibration sensor signals analysis. The primary goal is to autonomously predict the timing and root causes of faults in roto-dynamic machinery, revolutionizing maintenance practices. The methodology involves advanced artificial intelligence techniques, with a focus on deep learning, to analyse real-time vibration sensor data. Cutting-edge equipment with high-resolution data acquisition capabilities is employed, enhancing signal investigation and discrimination. Machine learning models like Convolutional Neural Networks (CNNs) [19] and Transfer Learning are integrated for accurate fault prediction. The research demonstrates the effectiveness of deep learning algorithms in predictive maintenance. These algorithms can autonomously identify fault timing and causes, reducing reliance on human expertise and enhancing accuracy. A practical case study involving a centrifugal pump within a Reverse Osmosis process validates the system’s capabilities[26]. The benefits of predictive maintenance using vibration sensor signals are substantial, including cost savings, reduced unplanned downtime, and improved operational safety. The study emphasizes the importance of proactive maintenance in optimizing industrial efficiency and ensuring the longevity of critical equipment. The device underwent rigorous testing at the Jubail pilot plant in WTIIRA premises, where it was installed in the pump of the nano unit for an extensive one-month trial period. During this testing phase, the device demonstrated its capabilities by promptly detecting an alarm signal related to the bearing of the pump. This early warning provided crucial insights into the machinery’s health and potential issues, identifying breakdowns and facilitating timely maintenance interventions. The successful trial at the Jubail pilot plant underscored the device’s practical applicability and its potential to revolutionize predictive maintenance practices in the industrial sector. This research showcases the transformative potential of artificial intelligence and deep learning in predictive maintenance. Proactive maintenance practices are highlighted as essential for operational efficiency and the preservation of vital industrial equipment.

Adaptive FPGA-Based Accelerators for Human-Robot Interaction in Indoor Environments.

This study addresses the challenges of human-robot interactions in real-time environments with adaptive field-programmable gate array (FPGA)-based accelerators. Predicting human posture in indoor environments in confined areas is a significant challenge for service robots. The proposed approach works on two levels: the estimation of human location and the robot's intention to serve based on the human's location at static and adaptive positions. This paper presents three methodologies to address these challenges: binary classification to analyze static and adaptive postures for human localization in indoor environments using the sensor fusion method, adaptive Simultaneous Localization and Mapping (SLAM) for the robot to deliver the task, and human-robot implicit communication. VLSI hardware schemes are developed for the proposed method. Initially, the control unit processes real-time sensor data through PIR sensors and multiple ultrasonic sensors to analyze the human posture. Subsequently, static and adaptive human posture data are communicated to the robot via Wi-Fi. Finally, the robot performs services for humans using an adaptive SLAM-based triangulation navigation method. The experimental validation was conducted in a hospital environment. The proposed algorithms were coded in Verilog HDL, simulated, and synthesized using VIVADO 2017.3. A Zed-board-based FPGA Xilinx board was used for experimental validation.

Development of 3D UDTs for Traffic Monitoring in Unreal 5 Game Engine

Abstract. Digital Twins recently, are a much regarded research topic in geospatial engineering, offering great potential for optimization of a variety of interrelated processes in urban environments. A major component of a Digital Twin use case is real-time sensor data input and analysis, which is used as foundation for simulations that lead to decision-support for authorities or even automatic response into the physical environment. The use of game engines as platform for urban Digital Twin use cases is promising due to superior visualization and animation capabilities, that facilitate diverse monitoring use cases for highly dynamic sensor data inputs. This contribution evaluates the fit for purpose of the Unreal Engine 5 game engine for digital twin use cases, by conducting a case study on urban traffic monitoring in the city of Liverpool Australia. The case study proved, that the necessary connections of static data provided via a geodatabase and dynamic real-time data from public portals can be integrated into the engine to establish an interactive Digital Twin use cases for evaluating traffic user interactions.

Smart Building Digital Twin for Interior Water Distribution System Management

Abstract. A smart campus integrates the Internet of Things, artificial intelligence, and real-time sensor data to optimize campus functions and create a context-aware decision-support platform for effective campus management. A crucial aspect of a smart campus is the water distribution system, which faces several challenges due to bursts, leaks, and water quality issues. This study uses Digital Twin technology to address these challenges through real-time monitoring, detection, and management of campus water distribution networks. A building-level digital twin is developed for the interior water networks of the Daphne Cockwell Complex at Toronto Metropolitan University. The major components include a 3D network model capable of analysis and simulations, a smart water management system, and real-time visualization. A 3D static model of the interior water utility network is created using the Industrial Foundation Class data. The semantic and topological models based on graph models are developed for network analysis. The models are integrated into the ESRI ArcGIS Pro to facilitate BIM-GIS integration. A dynamic model is created by incorporating automatic meter readings based on IoT. Combined with the 3D model of the utility network, the digital twin monitors and visualizes building water consumption, facilitating anomaly detection and real-time maintenance by the facilities management department. This study provides practical guidance for managing water distribution systems in university buildings, with potential applications in other complex environments. Future work aims to develop a system for detecting complex event processes by integrating different utilities.

EBH-IoT: Energy-efficient secured data collection and distribution of electronics health record for cloud assisted blockchain enabled IoT based healthcare system

The integration of Health IoT (H-IoT) and blockchain technologies are being heavily exploited and used in many domains, especially for e-healthcare to collect the data i.e electronic health record (EHR) from the patient. The H-IoT devices have the ability to provide real-time sensory data from patients to be processed and analyzed, and distributed. Blockchain is providing decentralized computation, distribution and storage for EHR data. Therefore, the integration of H-IoT and Blockchain technologies can become a reasonable choice for the design of decentralized H-IoT-based e-healthcare systems. But the H-IoT network has some intrinsic challenges like low computation, energy constraint, security, energy optimization, data storage, and real-time data analytic. Also, conventional EHR-based systems suffer from issues such as the potential loss of data, inadequate security and consensus on the unchangeable nature of health records, fragmented connections between different institutions, and ineffective clinical data retrieval methods, among other challenges. In this article, first, we study the performance of blockchain technology in the healthcare system. Second, we propose an improved Harris Hawk Optimization algorithm (HHO) based clustering mechanism for the collection and sharing of EHR. The proposed system was tested and validated using the Hyperledger-fabric based electronic healthcare record (EHR) sharing system along with Matlab. The proposed system achieves 12%, and 7% incremental improvement in terms of latency, throughput for the Blockchain networks. While the proposed clustering technique achieves 10%, 12%, 14%, and 16% improvements in alive node, energy consumption, throughput and average transmission delay compared to existing state of the art.

Development of an Optimization Software for Bioremediation of Hydrocarbon-Contaminated Soils mechanisms

A sophisticated software solution designed to enhance bioremediation processes in hydrocarbon-contaminated environments. This Advanced Bioremediation Optimization Software combines complex algorithms, real-time sensor data integration, and a user-friendly interface to deliver customized solutions for environmental restoration projects. The software utilizes predictive modeling to forecast remediation outcomes, optimizes treatment strategies based on ongoing data analysis, evaluates microbial communities through metagenomic sequencing data, and generates evidence-based recommendations to improve bioremediation efficiency. This tool represents a significant advancement in environmental restoration technology, offering practitioners a means to enhance the efficacy and cost-effectiveness of bioremediation projects. It also provides detailed economic projections to support informed decision-making by stakeholders, making it a valuable asset in the field of environmental remediation.

Developing Advanced Navigation Algorithms for Autonomous Vehicles to Enhance Safety and Efficiency in Urban Environments Using Real-Time Sensor Data and Machine Learning

Autonomous vehicles (AVs) have the ability to completely change how people get around cities. They could make transportation safer and more efficient while also reducing pollution and traffic. But their broad utilize depends on the creation of progressed direction frameworks that can dependably discover their way around complicated urban settings. An inventive way to illuminate this issue is portrayed in this think about. It employments real-time sensor information and machine learning to form and utilize cutting-edge direction strategies that work well in cities. There are five primary steps within the study process. To begin with, exhaustive information collection strategies are utilized to urge valuable sensor information from numerous places, such as GPS, LiDAR, webcams, and more. Another, progressed machine learning models are made and instructed with this information so that AVs can make smart choices and find their way around. These models are made to require into consideration things just like the environment, activity, how individuals walk, and other changing factors to form beyond any doubt they work well in complicated urban circumstances. A number of distinctive measures are utilized to judge the suggested algorithms, such as their security, adequacy, steadfastness, and capacity to alter. Re-enactment circumstances are utilized to test the strategies in a run of urban settings, from swarmed city zones to calm roads within the rural areas. When compared to conventional strategies, the comes about appear enormous picks up in following execution. The equations make it more secure and more productive to utilize them in cities. The modern strategies are too compared to current direction frameworks to appear how much way better they are at being precise, adaptable, and fast to reply. The research also finds possible problems and restrictions, like the need for real-time processing and complicated computations, and suggests ways to fix them.

Cyber-Physical Perception Interface for Co-Simulation Applications.

Co-simulation can bring improvements to the development of cyber-physical perceptive systems (CPPS) in critical fields, allowing uninterrupted system operation and flexibility to use both real-time sensor data and non-real-time data. This paper proposes a co-simulation approach that integrates physical systems and communication systems, including both hardware and software components. This study demonstrates how systems of different natures with discrete or continuous events can be simulated using three methods: time stepped, global event driven, and variable stepped. Through two case studies from the medical and energy fields, CPPS and co-simulation reveal their importance for the future by improving precision and efficiency, which leads to more accurate diagnoses and personalized treatments in the medical field and increases the stability of energy networks.

Research on an adaptive prediction method for restaurant air quality based on occupancy detection

This study evaluates the impact of personnel on air quality in air-conditioned restaurant environments during winter, summer, and transitional seasons, characterized by the peculiarities of personnel movement, high concentration of personnel in a short period of time, poor ventilation and high personnel density, leading to the accumulation of pollutants that can severely damage personnel health. This study examines carbon dioxide (CO2) and particulate matter (PM) concentrations and proposes an online adaptive model for their prediction.Environmental influence factors are analyzed through correlations of measured data, and appropriate input parameters (time-lagged cumulative number of people) are selected. The Auto-Regressive with Exogenous Inputs (ARX) model is used to predict the change of air quality with the movement and number of people, compared with classical models (LR, DT, SVM, ENS, GPR, NN, VARX). Results show that the ARX model performs excellently in predicting restaurant environments; the effect of different environmental factors on the predictive effectiveness of the model was also explored. When combined with real-time sensor data and AIC-based adaptive optimization, the prediction accuracy is further improved by more than 25 %. The R2 of the ARX model is 0.9549, the MAE is 0.8352 μg/m3 and the MAPE is 3.17 %. The adaptively optimized model can be adapted to different environments, overcoming the problem of poor adaptability of such models. Our results enable effective environmental control strategies in public spaces, contributing to Sustainable Development Goal (SDG) 3: Good Health and Well-being by reducing health risks associated with indoor air pollution.

Enhancing security through continuous biometric authentication using wearable sensors

The paper presents a novel approach for biometric continuous driver authentication (CDA) for secure and safe transportation using wearable photoplethysmography (PPG) sensors and deep learning. Conventional one-time authentication (OTA) methods, while effective for initial identity verification, fail to continuously verify the driver’s identity during vehicle operation, potentially leading to safety, security, and accountability issues. To address this, we propose a system that employs Long Short-Term Memory (LSTM) models to predict subsequent PPG values from wrist-worn devices and continuously compare them with real-time sensor data for authentication. Our system calculates a confidence level representing the probability that the current user is the authorized driver, ensuring robust availability to genuine users while detecting impersonation attacks. The raw PPG data is directly fed into the LSTM model without pre-processing, ensuring lightweight processing. We validated our system with PPG data from 15 volunteers driving for 15 min in varied conditions. The system achieves an Equal Error Rate (EER) of 4.8%. Our results demonstrate that the system is a viable solution for CDA in dynamic environments, ensuring transparency, efficiency, accuracy, robust availability, and lightweight processing. Thus, our approach addresses the main challenges of classical driver authentication systems and effectively safeguards passengers and goods with robust driver authentication.

Enhanced Harmonic Partitioned Scheduling of Periodic Real-Time Tasks Based on Slack Analysis.

The adoption of multiprocessor platforms is growing commonplace in Internet of Things (IoT) applications to handle large volumes of sensor data while maintaining real-time performance at a reasonable cost and with low power consumption. Partitioned scheduling is a competitive approach to ensure the temporal constraints of real-time sensor data processing tasks on multiprocessor platforms. However, the problem of partitioning real-time sensor data processing tasks to individual processors is strongly NP-hard, making it crucial to develop efficient partitioning heuristics to achieve high real-time performance. This paper presents an enhanced harmonic partitioned multiprocessor scheduling method for periodic real-time sensor data processing tasks to improve system utilization over the state of the art. Specifically, we introduce a general harmonic index to effectively quantify the harmonicity of a periodic real-time task set. This index is derived by analyzing the variance between the worst-case slack time and the best-case slack time for the lowest-priority task in the task set. Leveraging this harmonic index, we propose two efficient partitioned scheduling methods to optimize the system utilization via strategically allocating the workload among processors by leveraging the task harmonic relationship. Experiments with randomly synthesized task sets demonstrate that our methods significantly surpass existing approaches in terms of schedulability.

Enhanced Fault Diagnosis in IoT: Uniting Data Fusion with Deep Multi-Scale Fusion Neural Network

One of the biggest challenges is figuring out when industrial IoT devices break. Notwithstanding these difficulties, one of the many advantages of using real-time sensor data from the Industrial Internet of Things (IIoT) is the ability to monitor events and respond promptly. The IIoT's sensor numbers may be analysed, combined with data from other sources, and applied quickly to make decisions. This manuscript proposes Enhanced Fault Diagnosis in IoT, Uniting Data Fusion with Deep Multi-Scale Fusion Neural Network (FD-IoT-DMSFNN). Initially, input sensor data are taken from the CWRU Dataset. Then, sensor data is normalised using Multivariate Fast Iterative Filtering. Then, the normalised sensor data are extensively dispersed and diverse; hence, the data outlier detection is performed using the Deep isolation forest (DIF) approach. Therefore, this manuscript proposes a Mexican Axolotl Optimization (MAO) for tuning the weight parameter of DMSFNN, which exhibits an enhanced fault detection process. Python is used to implement the recommended strategy. The proposed method's performance yields a lower completion time of 6.5%, 1.8%, and 4.13%; higher prediction accuracy of 2.26%, 6.71%, and 8.32% compared with existing approaches such as towards deep domain adaptability training methods for industrial IoT edge device soft real-time fault diagnosis (FD-IoT-LSTM), Intelligent Fault Quantitative Identification for the Industrial Internet of Things (IIoT) utilising a particular deep dual reinforcement learning model with insufficient samples (FD-IoT-DRL) and IIoT- based fault identification model for industrial use (FD-IoT-ML-LSTM) respectively.

Digital twin–driven optimization of laser powder bed fusion processes: a focus on lack-of-fusion defects

PurposeThe purpose of this research is to enhance the Laser Powder Bed Fusion (LPBF) additive manufacturing technique by addressing its susceptibility to defects, specifically lack of fusion. The primary goal is to optimize the LPBF process using a digital twin (DT) approach, integrating physics-based modeling and machine learning to predict the lack of fusion.Design/methodology/approachThis research uses finite element modeling to simulate the physics of LPBF for an AISI 316L stainless steel alloy. Various process parameters are systematically varied to generate a comprehensive data set that captures the relationship between factors such as power and scan speed and the quality of fusion. A novel DT architecture is proposed, combining a classification model (recurrent neural network) with reinforcement learning. This DT model leverages real-time sensor data to predict the lack of fusion and adjusts process parameters through the reinforcement learning system, ensuring the system remains within a controllable zone.FindingsThis study's findings reveal that the proposed DT approach successfully predicts and mitigates the lack of fusion in the LPBF process. By using a combination of physics-based modeling and machine learning, the research establishes an efficient framework for optimizing fusion in metal LPBF processes. The DT's ability to adapt and control parameters in real time, guided by machine learning predictions, provides a promising solution to the challenges associated with lack of fusion, potentially overcoming the traditional and costly trial-and-error experimental approach.Originality/valueOriginality lies in the development of a novel DT architecture that integrates physics-based modeling with machine learning techniques, specifically a recurrent neural network and reinforcement learning.