Sensor Data Analysis Research Articles

IoT Sensor Data Retrieval and Analysis in Cloud Environments for Enhanced Power Management

The Internet of Things (IoT) brings new products to everyone to improve daily life. Concurrently other emerging technologies, including Big Data, Cloud Services, and surveillance, can participate through these technological advances. This research work explores the synergies among four systems to identify their shared functionalities and integrate them to create useful potential applications. Despite the limitations of the smart city concept, researchers would seek innovative methods to collect and process sensor information within an IoT-enabled smart building. A cornerstone of the proposed system is the utilization of cloud services as the foundational technology for a schema management platform. This platform efficiently gathers data generated by sensors within smart industrial units. Leveraging the capabilities of IoT technology, the data can be remotely managed and accessed using mobile devices with network connectivity. The proposed work addresses the challenges related with the smart city perception and supports for revolutionary approaches in the collection and manipulation of IoT Sensor data. By doing so, it imagines the creation of green, smart schemes that contribute to sustainable urban development.

General Review of Smartphone Usability in Noise Mapping

The global urban population is steadily increasing, with more than half of the world's population currently residing in cities, and this trend is expected to double by 2050. As urbanization continues, noise pollution becomes a significant concern, affecting over 60% of major city dwellers and impacting human health on both physiological and psychological levels. To address this issue, governments and organizations are striving to develop effective noise assessment, regulation, and mitigation policies. This literature review explores the role of noise mapping and the potential of smartphones in collecting noise data to inform these policies. Traditional noise mapping techniques and smartphone-based data collection methods are discussed, along with their importance in urban planning, environmental studies, and public health. Key research questions are identified, including the methodologies employed for smartphone-based noise mapping, the accuracy of smartphone-collected data compared to traditional measurements, practical applications, challenges, and emerging trends. The review reveals that smartphones offer a cost-effective and widespread means of gathering noise data, enabling real-time insights and enhancing various domains' practical applications. However, challenges such as data accuracy, privacy concerns, and device limitations must be addressed. The future of smartphone-based noise mapping looks promising, with advancements in sensor technologies, artificial intelligence, and data analysis tools empowering researchers, urban planners, and policymakers to make informed decisions about noise pollution in urban environments.

Optimized Design of EdgeBoard Intelligent Vehicle Based on PP-YOLOE.

Advances in deep learning and computer vision have overcome many challenges inherent in the field of autonomous intelligent vehicles. To improve the detection accuracy and efficiency of EdgeBoard intelligent vehicles, we proposed an optimized design of EdgeBoard based on our PP-YOLOE+ model. This model innovatively introduces a composite backbone network, incorporating deep residual networks, feature pyramid networks, and RepResBlock structures to enrich environmental perception capabilities through the advanced analysis of sensor data. The incorporation of an efficient task-aligned head (ET-head) in the PP-YOLOE+ framework marks a pivotal innovation for precise interpretation of sensor information, addressing the interplay between classification and localization tasks with high effectiveness. Subsequent refinement of target regions by detection head units significantly sharpens the system's ability to navigate and adapt to diverse driving scenarios. Our innovative hardware design, featuring a custom-designed mainboard and drive board, is specifically tailored to enhance the computational speed and data processing capabilities of intelligent vehicles. Furthermore, the optimization of our Pos-PID control algorithm allows the system to dynamically adjust to complex driving scenarios, significantly enhancing vehicle safety and reliability. Besides, our methodology leverages the latest technologies in edge computing and dynamic label assignment, enhancing intelligent vehicles' operations through seamless sensor integration. Our custom dataset, specifically designed for this study, includes 4777 images captured by intelligent vehicles under a variety of environmental and lighting conditions. The dataset features diverse scenarios and objects pertinent to autonomous driving, such as pedestrian crossings and traffic signs, ensuring a comprehensive evaluation of the model's performance. We conducted extensive testing of our model on this dataset to thoroughly assess sensor performance. Evaluated against metrics including accuracy, error rate, precision, recall, mean average precision (mAP), and F1-score, our findings reveal that the model achieves a remarkable accuracy rate of 99.113%, an mAP of 54.9%, and a real-time detection frame rate of 192 FPS, all within a compact parameter footprint of just 81 MB. These results demonstrate the superior capability of our PP-YOLOE+ model to integrate sensor data, achieving an optimal balance between detection accuracy and computational speed compared with existing algorithms.

Steps and Challenges in Analyzing Real Sensor Data from a Productive Press Shop and its Value for Predictive Maintenance Application

This paper highlights the significance of AI-powered maintenance strategies in modern industry for operational optimization and reduced downtime. It emphasizes the crucial role of sensor data analysis in identifying anomalies and predicting failures. The research specifically examines sensor data from an automotive press shop, addressing questions related to data selection, collection challenges, and knowledge generation. By utilizing unsupervised learning on compressed air data from a press line, the study identifies patterns, anomalies, and correlations. The results offer insights into the potential for implementing an effective predictive maintenance strategy. Additionally, a systematic literature review underscores the importance of data analysis in production systems, particularly in the context of maintenance.

Smart Application to Avoid Road Accidents Caused by Speed Breakers

The intelligent application presented in this study aims to stop speed breakers from causing accidents on the road. The application employs on board sensors and GPS technologies to identify speed violators and notify drivers. The software detects speed breakers and alerts drivers in real time by combining sensor data analysis and machine learning methods. Our application aims to enhance road safety by notifying drivers about speed breakers. Therefore, reducing the possibility of accidents and making the road safer for everyone

Multi-Scale Fusion Siamese Network Based on Three-Branch Attention Mechanism for High-Resolution Remote Sensing Image Change Detection

Remote sensing image change detection (CD) is an important means in remote sensing data analysis tasks, which can help us understand the surface changes in high-resolution (HR) remote sensing images. Traditional pixel-based and object-based methods are only suitable for low- and medium-resolution images, and are still challenging for complex texture features and detailed image detail processing in HR images. At present, the method based on deep learning has problems such as inconsistent fusion and difficult model training in the combination of the difference feature information of the deep and shallow layers and the attention mechanism, which leads to errors in the distinction between the changing region and the invariant region, edge detection and small target detection. In order to solve the above problems of inconsistent fusions of feature information aggregation and attention mechanisms, and indistinguishable change areas, we propose a multi-scale feature fusion Siamese network based on attention mechanism (ABMFNet). To tackle the issues of inconsistent fusion and alignment difficulties when integrating multi-scale fusion and attention mechanisms, we introduce the attention-based multi-scale feature fusion module (AMFFM). This module not only addresses insufficient feature fusion and connection between different-scale feature layers, but also enables the model to automatically learn and prioritize important features or regions in the image. Additionally, we design the cross-scale fusion module (CFM) and the difference feature enhancement pyramid structure (DEFPN) to assist the AMFFM module in integrating differential information effectively. These modules bridge the spatial disparity between low-level and high-level features, ensuring efficient connection and fusion of spatial difference information. Furthermore, we enhance the representation and inference speed of the feature pyramid by incorporating a feature enhancement module (FEM) into DEFPN. Finally, the BICD dataset proposed by the laboratory and public datasets LEVIR-CD and BCDD are compared and tested. We use F1 score and MIoU values as evaluation metrics. For AMBMFNet, the F1 scores on the three datasets are 77.69%, 81.57%, and 77.91%, respectively, while the MIoU values are 84.65%, 85.84%, and 84.54%, respectively. The experimental results show that ABMFNet has better effectiveness and robustness.

Multivariate sensor data analysis for fault detection toward feedback loop-based continuous learning in FFF 3D printer

Multivariate sensor data analysis for fault detection toward feedback loop-based continuous learning in FFF 3D printer

Data-Driven Prediction Model for Analysis of Sensor Data

In view of Industry 4.0, data generation and analysis are challenges. For example, machine health monitoring and remaining useful life prediction use sensor signals, which are difficult to analyze using traditional methods and mathematical techniques. Machine and deep learning algorithms have been used extensively in Industry 4.0 to process sensor signals and improve the accuracy of predictions. Therefore, this paper proposes and validates the data-driven prediction model to analyze sensor data, including in the data transformation phase Principal Component Analysis tested by Fourier Transformation and Wavelet Transformation, and the modeling phase based on machine and deep learning algorithms. The machine learning algorithms used for tests in this research are Random Forest Regression (RFR), Multiple Linear Regression (MLR), and Decision Tree Regression (DTR). For the deep learning comparison, the algorithms are Deep Learning Regression and Convolutional network with LeNet-5 Architecture. The experimental results indicate that the models show promising results in predicting wear values and open the problem to further research, reaching peak values of 92.3% accuracy for the first dataset and 62.4% accuracy for the second dataset.

You just have to take a big leap! Authentic teaching and learning in the technology program at an upper secondary school

AbstractIn literature, teaching for an authentic learning experience is seen as rewarding and interesting, promoting better understanding of for example sustainable development and fostering students’ independence. Some upper secondary schools emphasise authentic teaching and learning on their websites, showcasing how students engage in authentic tasks. We are interested in examining how authentic teaching is implemented in practice and what enables it. Therefore, we investigate a specific practice where authentic teaching occurs, and we follow students and teachers. We want to understand what may be required to implement authentic teaching. The research questions were: What characterises authentic teaching and learning when students on the technology program work on their design projects and individual pieces of work? How is authentic teaching and learning facilitated? Students in Year 3 of the technology program engaged in a project where they studied residential apartments’ resource use and climate impact using sensors. They visited the energy research team, learned about the technology and data collection, and completed own upper secondary school projects related to sensor technology, programming, and data analysis. These were meant to be characterised by authenticity according to the school’s procedures. Data was collected through interviews, observations, and analysis of video material, sound recordings, and field notes. The analysis revealed that the teaching exhibited characteristics of authentic learning, facilitated by early exposure, teacher empowerment, and strong support from management and resources. The results thus show both the characteristics of how the teaching is organized and how teachers handle students’ learning, as well as the organizational factors at the specific school that enable it.

Day–Night architecture: Development of an ultra-low power RISC-V processor for wearable anomaly detection

In healthcare, anomaly detection has emerged as a central application. This study presents an ultra-low power processor tailored for wearable devices dedicated to anomaly detection. Introducing a unique Day–Night architecture, the processor is bifurcated into two distinct segments: The Day segment and the Night segment, both of which function autonomously. The Day segment, catering to generic wearable applications, is designed to remain largely inactive, awakening only for specific tasks. This approach leads to considerable power savings by incorporating the Main-CPU and system interconnect, both major power consumers. Conversely, the Night segment is dedicated to real-time anomaly detection using sensor data analytics. It comprises a Sub-CPU and a minimal set of IPs, operating continuously but with minimized power consumption. To further enhance this architecture, the paper presents an ultra-lightweight RISC-V core, All-Night core, specialized for anomaly detection applications, replacing the traditional Sub-CPU. To validate the Day–Night architecture, we developed a prototype processor and implemented it on an FPGA board. An anomaly detection application, optimized for this prototype, was also developed to showcase its functional prowess. Finally, when we synthesized the processor prototype using 45 nm process technology, it affirmed our assertion of achieving an energy reduction of up to 57%.

IOT Based Smart Plant Monitoring System

Abstract: The IoT-based Smart Plant Monitoring System represents a transformative approach to modern agriculture by leveraging the power of the Internet of Things (IoT) to enhance the monitoring and management of plants in agricultural settings. This system integrates advanced sensor technologies, wireless communication, and data analytics to provide real-time insights into the health and environmental conditions of plants, thereby optimizing resource utilization and improving overall crop yield. The key components of the proposed system include a network of sensors strategically placed within the agricultural field to capture vital data related to soil moisture, temperature, humidity, light intensity, and nutrient levels. These sensors are connected through IoT-enabled devices, forming a cohesive network that continuously collects and transmits data to a centralized cloud-based platform. The cloud platform acts as a repository for the acquired data, where advanced analytics algorithms process the information in real-time

Agricultural Environment and Intelligent Plant Protection Equipment

Intelligent plant protection equipment utilizes advanced sensor technology and data analysis algorithms to achieve real-time monitoring and precise management of crop growth status, pest and disease situations, and environmental parameters [...]

Mitigating urban heat island and enhancing indoor thermal comfort using terrace garden

The United Nations advocates for sustainable urban planning and design, emphasizing green infrastructure initiatives to mitigate urban heat island effects and enhance the resilience and livability of cities globally. To address urban heat challenges, a study was conducted in Chennai, India, from April to June 2023. The study focused on assessing temperature dynamics on a building's terrace by comparing a well-maintained garden area with an exposed region. Temperature and humidity sensors were deployed in both the garden and exposed areas of the terrace, as well as within rooms beneath it, to monitor hourly temperature fluctuations. The findings indicate a significant reduction in internal room temperatures in areas with rooftop gardens, ranging from 4 to 11 °C, depending on the time of year and sun's position, compared to rooms with fully exposed roof configurations. Additionally, simulation studies were performed to validate these findings, suggesting that optimizing the distribution of soil beds and plant density across the roof could yield an additional temperature reduction of 3–4 °C, resulting in an overall difference of up to 14–15 °C. The study highlights the efficacy of rooftop gardens in providing cooling effects during daylight hours and maintaining temperature parity post-sunset. Through analysis of sensor data, the research elucidates the intricate relationship between green infrastructure and thermal comfort, offering insights for energy-efficient building design and resilient urban planning. The findings underscore the potential of rooftop gardens in fostering a more comfortable, energy-efficient, and sustainable urban living environment.

Enhancing Sustainability: Exploring IoT Integration in Renewable Energy Infrastructure

The management and utilization of sustainable resources have undergone a significant transformation as a result of the integration of Internet of Things (IoT) technology into the renewable energy sector. IoT has simplified the optimization of renewable energy sources, including solar, wind, and hydropower, by leveraging sensor technology, data analytics, and connectivity. This optimization has resulted in major advances in energy production efficiency, equipment maintenance procedures, and the smooth integration of renewable energy into established power grids. Operators can optimize energy output while reducing waste by adjusting operations in real time through continuous monitoring and analysis of data gathered from IoT-enabled sensors. Moreover, by predicting and fixing possible problems before they arise, predictive maintenance algorithms significantly contribute to increased equipment reliability by lowering downtime and operating expenses. Furthermore, IoT systems allow for dynamic modifications to energy flow, guaranteeing the stability and dependability of the integration of renewable energy sources into the current power networks. This research investigates the use of IoT technology in the renewable energy sector in detail, exploring its several uses and assessing how it affects industry standards, sustainability goals, and the overall energy environment.

Sensor Data Analytics for Optimized Methane Leak Detection and Mitigation

Sensor Data Analytics for Optimized Methane Leak Detection and Mitigation

Wavelet-Based Machine Learning Algorithms for Photoacoustic Gas Sensing

The significance of intelligent sensor systems has grown across diverse sectors, including healthcare, environmental surveillance, industrial automation, and security. Photoacoustic gas sensors are a promising type of optical gas sensor due to their high sensitivity, enhanced frequency selectivity, and fast response time. However, they have limitations such as dependence on a high-power light source, a requirement for a high-quality acoustic signal detector, and sensitivity to environmental factors, affecting their accuracy and reliability. Machine learning has great potential in the analysis and interpretation of sensor data as it can identify complex patterns and make accurate predictions based on the available data. We propose a novel approach that utilizes wavelet analysis and neural networks with enhanced architectures to improve the accuracy and sensitivity of photoacoustic gas sensors. Our proposed approach was experimentally tested for methane concentration measurements, showcasing its potential to significantly advance the field of gas detection and analysis, providing more accurate and reliable results.

Storage and Analysis of Remote Sensing Data

Storage and Analysis of Remote Sensing Data

Utilization of Data Analytics to Enhance Operational Efficiency in Manufacturing Companies

In the digital era, manufacturing industries confront challenges like heightened global competition and intricate production processes, urging them to boost efficiency and productivity. Amidst these circumstances, Big Data emerges as a pivotal opportunity to enhance manufacturing performance. Big Data, characterized by vast volumes of data, utilizes advanced data mining to machine learning techniques for analysis. Data analytics, an interdisciplinary field, profoundly impacts manufacturing operations, enabling deeper insights into production processes. By analyzing production data, companies identify inefficiencies, streamline workflows, and enhance operational efficiency and productivity. Predictive maintenance through sensor data analysis prevents machine failures, while logistics data analysis optimizes supply chains and inventory management, reducing costs and enhancing competitiveness. However, implementing Big Data analytics presents challenges such as rapid data growth, diverse data sources, real-time insights, skill shortages, and data fragmentation. Overcoming these hurdles requires robust technology, skilled personnel, and effective data management strategies. Examples of Big Data analytics applications include customer behavior analysis by Amazon and Netflix, fraud detection in insurance, and urban mobility optimization. Success factors in data analytics implementation include effective data-driven communication, technology integration, and skill enhancement. In conclusion, implementing Big Data Analytics in manufacturing promises significant benefits in operational efficiency, product quality, and competitiveness. Overcoming challenges necessitates robust strategies and consideration of ethical and security issues, ensuring responsible data usage. With a deep understanding of Big Data Analytics, manufacturing companies can leverage this technology to achieve higher efficiency and competitiveness in the global market.

MACHINE LEARNING'S INFLUENCE ON SUPPLY CHAIN AND LOGISTICS OPTIMIZATION IN THE OIL AND GAS SECTOR: A COMPREHENSIVE ANALYSIS

Machine Learning (ML) is revolutionizing supply chain and logistics optimization in the oil and gas sector. This comprehensive analysis explores how ML algorithms are reshaping traditional practices, leading to more efficient operations and cost savings. ML enables predictive analytics, demand forecasting, route optimization, and inventory management, improving overall supply chain performance. Supply chain and logistics in the oil and gas sector are inherently complex, involving numerous interconnected processes and stakeholders. ML algorithms are adept at handling this complexity by analyzing vast amounts of data to identify patterns and optimize operations. By leveraging historical data, ML can predict future demand, enabling companies to adjust their inventory levels and production schedules accordingly. ML algorithms also play a crucial role in route optimization, helping companies minimize transportation costs and reduce carbon emissions. By analyzing factors such as traffic patterns, weather conditions, and road conditions, ML algorithms can determine the most efficient routes for transporting goods and equipment. Furthermore, ML enables predictive maintenance, which is essential in the oil and gas sector to prevent equipment failures and downtime. By analyzing sensor data from equipment, ML algorithms can predict when maintenance is required, allowing companies to schedule maintenance proactively and avoid costly disruptions. In conclusion, ML is transforming supply chain and logistics optimization in the oil and gas sector by enabling predictive analytics, demand forecasting, route optimization, and predictive maintenance. By leveraging the power of ML, companies in the oil and gas sector can improve operational efficiency, reduce costs, and enhance overall supply chain performance.
 Keywords: Machine’s Learning, Supply Chain, Logistics, Optimization, Oil and Gas.

ADVANCING FOOD SAFETY THROUGH IOT: REAL-TIME MONITORING AND CONTROL SYSTEMS

This paper proposes the development of an Internet of Things (IoT)-based real-time monitoring and control system for food safety management. By leveraging IoT sensors and devices throughout the food production and distribution process, this system aims to continuously monitor food safety parameters, providing immediate data to prevent contamination and ensure compliance with safety standards. In response to the critical need for enhanced food safety measures, this concept paper proposes the implementation of IoT (Internet of Things) technology to advance food safety through real-time monitoring and control systems. The global food industry faces significant challenges in ensuring food safety throughout the supply chain, including inefficiencies, delays in response to potential hazards, and increased risks of contamination. Traditional methods of monitoring and controlling food safety often lack real-time capabilities and are prone to human error. The proposed solution focuses on introducing an IoT-based system for real-time monitoring of critical food safety parameters such as temperature, humidity, and the presence of pathogens. This system will leverage a combination of sensor technologies, data analytics, and communication protocols to continuously monitor environmental conditions and potential hazards throughout the food supply chain. Key components of the proposed system include IoT sensors and devices, a centralized monitoring platform, data transmission infrastructure, and integration with existing food safety management systems. Advanced data analytics techniques, including machine learning algorithms, will be employed to analyze collected data, identify patterns, trends, and anomalies, and develop predictive models to anticipate potential hazards. The implementation strategy outlined in this concept paper includes comprehensive assessment and planning, deployment of IoT sensors, establishment of data collection and transmission infrastructure, data analysis and insights, integration with existing systems, and training and capacity building. By following this strategy, stakeholders can proactively manage food safety risks and ensure the integrity and safety of the food supply chain. Advancing food safety through IoT-based real-time monitoring and control systems holds great promise in revolutionizing the way food safety is managed, safeguarding public health, enhancing consumer trust, and ensuring a safer and more reliable food supply for consumers worldwide.
 Keywords: Food Safety, Real Time, Monitoring, IoT.