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Real-Time Monitoring of Structural Health with Wireless sensor networks in IoT Infrastructure 3

Real-Time Monitoring of Structural Health with Wireless Sens...

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3rd IEEE International conference on Automation and Computation, AUTOCOM 2025

作者： Bhushan, Bharat Pushparajesh, V. Bethanney Janney, J. Sahoo, Girija Shankar Aggarwal, Prateek Das, Dibyasundar Chitkara University Chitkara Centre for Research and Development Himachal Pradesh Baddi174103 India Karnataka Bangalore India Sathyabama Institute of Science and Technology Department of Biomedical Engineering Tamil Nadu Chennai India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India Chitkara University Centre of Research Impact and Outcome Punjab Rajpura140417 India Centre for Cyber Security Odisha Bhubaneswar India

ISBN: (纸本)9798331542375

Wireless sensor networks for structural health monitoring of infrastructure are now becoming an IoT. It provides the ability to collect and analyze survivability data in real-time to enable continuous structural health monitoring for buildings, bridges, and other critical infrastructure. The purpose of this abstract is to introduce Real-time Monitoring with Wireless sensor networks in IoT Infrastructure. Wireless sensor networks are used in structural health monitoring. They are typically composed of many distributed, cheap sensors embedded in the structure that communicate wirelessly to a central monitoring station. Those sensors, including temperature, strain and vibration, are critical in detecting potential structural problems. This data gets transmitted to a cloud for processing, which provides indications of an unusual characteristic or predictive failure. Compared to the traditional methods, there are several advantages of applying WSN for real-time structural health monitoring. Lead leads provide real-time updates on the condition of a bridge that allows for continuous monitoring, with advanced sensors pinpointed through its structure. It will assist in keeping the infrastructure safe and affordable by guaranteeing rapid time-to-resolution. In addition, WSNs have low implementation costs and do not interfere too much with the structure of large structures. © 2025 IEEE.

关键词： Wireless sensor networks

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Framework for Cloud-Based Data Management in Autonomous Vehicles

Framework for Cloud-Based Data Management in Autonomous Vehi...

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2025 IEEE International Students' conference on Electrical, Electronics and Computer Science, SCEECS 2025

作者： Pallapothu, Harshith Gopi Krishna, G. Sunil, Digvijay Teja Yadav, S. Nagasundari, S. Pes University Dept. of Computer Science & Engineering Bengaluru India Research Center for Information Security Forensics and Cyber Resilience Pes University Dept. of Computer Science & Engineering Bengaluru India

ISBN: (纸本)9798331529833

The increasing adoption of autonomous vehicles has driven the need for robust data management solutions that support real-time operations and ensure vehicle safety and efficiency. This work introduces a cloud-based framework for management of sensor data from autonomous vehicles, focusing on optimizing payload transmission rates. The framework leverages AWS IoT Core and AWS IoT Analytics to ensure efficient data flow from vehicle sensors to cloud storage. This framework demonstrates its potential for scalable deployment in real-world autonomous vehicle networks, contributing to the evolution of connected vehicle technologies and intelligent transport systems. This framework ensures reliable data transmission up to 250 payloads per second, beyond which data loss occurs. © 2025 IEEE.

关键词： Cloud platforms

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Natural Language processing in Geographic information Systems 3

Natural Language Processing in Geographic Information System...

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3rd International conference on intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Kushmitha, S. Kathrine, Jaspher W. Abisha, M.B. Department of Computer Science and Engineering Karunya Institute of Technology and Sciences Coimbatore India

ISBN: (纸本)9798331527549

The integration of Geographic information Systems (GIS) with Natural Language processing (NLP) has transformed how spatial data is accessed and utilized. By enabling GIS interfaces to interpret natural language queries, NLP enhances user experience, democratizes geospatial data, and improves query accuracy. This paper reviews state-of-the-art methodologies for geo-parsing, geo-coding, and spatial query interpretation, addressing challenges like geographic ambiguity, imprecise spatial relationships, and scalability limitations. Key contributions include a systematic literature review identifying research gaps such as insufficient context-awareness and a lack of annotated datasets, alongside case studies demonstrating the benefits of NLP integration in applications like disaster management and urban planning. Future opportunities are explored in leveraging deep learning, multimodal data integration, and adaptive user interfaces to further enhance GIS accessibility and usability. Future opportunities are explored in leveraging deep learning for spatial reasoning, integrating multimodal data sources for enhanced decision-making, and developing adaptive user interfaces tailored for diverse audiences. Furthermore, the paper highlights the potential of NLP in generating geo-descriptions for distributed sensor networks, supporting accessibility for impaired users, and enhancing real-time data analysis in smart city initiatives. Addressing these gaps and building scalable frameworks will pave the way for GIS systems that are not only powerful but also inclusive, fostering global accessibility and broader adoption of geospatial technologies. © 2025 IEEE.

关键词： Structured Query Language

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Adaptive Spider Monkey Optimization Algorithm Based Clustering and Routing for Cognitive Radio sensor networks

Adaptive Spider Monkey Optimization Algorithm Based Clusteri...

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International conference on 6G Communications Networking and Signal processing, SGCNSP 2023

作者： Nijaguna, G.S. Babu, J. Ananda Ramya, R. Saravanan, V. Department of Information Science and Engineering S.E.A. College of Engineering and Technology Bangalore India Department of ISE Malnad College of Engineering Hassan India Department of Nano Electronics Materials and Sensors Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai Thandalam India Department of Nano Electronics Materials and Sensors Saveetha School of Engineering Saveetha Institute of Medical and Technical Science Chennai Thandalam India

ISBN: (纸本)9789819778751

Cognitive radio sensor networks (CRSN) are severely energy constrained and radio frequency (RF) wireless energy harvesting (RFWEH) has been shown to improve network lifetime. In many CRSN applications, node mobility poses challenges due to changing network topology. In this research, the Adaptive Spider Monkey Optimization (ASMO) algorithm is proposed for obtaining the energy efficient Cluster Head (CH) selection and routing in CRSN. The consumption of energy in CRSN is achieved by the optimal routing method by utilizing the optimization algorithm. The optimization-based clustering and routing approach is developed for designing the optimal transmission path by CH to destination. Key parameters such as energy consumption (EC), packet delivery ratio (PDR), throughput, network lifetime (NL), and end-to-end delay (EED) are employed to evaluate suggested methodology. Comparative analysis shows that the ASMO methodology outperforms present methods in terms of performance. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Clustering algorithms

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Revolutionizing Water Management: Digital Twins and AI for Predictive Maintenance and Sustainability 5th

Revolutionizing Water Management: Digital Twins and AI for P...

引用

5th International conference on Protection of Historical Constructions, PROHITECH 2025

作者： Syed, Toqeer Ali Naqash, Muhammad Tayyab Siddiqui, Muhammad Shoaib Alqahtany, Saad Said Faizullah, Safiullah Formisano, Antonio Faculty of Computer and Information System Islamic University of Madinah Medina Saudi Arabia Civil Engineering Department Faculty of Engineering Islamic University of Madinah Medina Saudi Arabia University of Naples Federico II Piazzale Tecchio 80 Naples80125 Italy

ISBN: (纸本)9783031873119

The innovative investigation integrates digital and physical water networks to improve their management and predictive capabilities. The used approach applies advanced sensors for detecting leaks and smart meters that monitor water usage, send alerts, usage data and visual information to a digital platform. At the heart of this framework is a digital twin, a virtual replica that continuously reflects the real-time state of the physical network by processing incoming sensor data. A multimodal transformer model is employed to integrate and analyze diverse data streams, including numerical and visual inputs, enabling highly accurate leakage detection, future water usage forecasting, and contextual insights with a prediction accuracy of about 98%. This approach advances water management and provides valuable insights for preserving historical buildings that demonstrates optimize operational efficiency and ensure sustainable water use in smart water networks. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： information leakage

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Design and Implementation of intelligent Dressing Mirror Based on Raspberry Pi 4B

Design and Implementation of Intelligent Dressing Mirror Bas...

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Digital Analysis and processing, intelligent Computation (DAPIC), International conference on

作者： Chunliu Wang Keling Wu Yan Zhang School of Information Engineering Nanjing Normal University Zhongbei College Danyang Jiangsu Province China

ISBN: (数字)9798331536169

ISBN: (纸本)9798331536176

With the rapid advancement of smart home technologies, personalized home devices have garnered significant attention from users. This paper presents the design and implementation of an intelligent dressing mirror based on Raspberry Pi 4B, aimed at addressing the growing demand for personalized home environments. The system utilizes Raspberry Pi 4B as the central controller and integrates with Alibaba Cloud's MQTT service to store customized clothing items and images, categorized by gender. Real-time environmental data, including weather, time, temperature, humidity, and ultraviolet levels, are acquired via the wireless module integrated into the Raspberry Pi. Additionally, photoelectric sensors and cameras capture relevant user data. By analyzing these datasets, the system recommends suitable clothing combinations and displays personalized clothing suggestions, weather updates, date, and schedule information on the mirror interface. Experimental results demonstrate that the system not only exhibits low power consumption but also effectively integrates personal care with home automation technologies, providing users with a convenient and tailored experience.

关键词： Temperature sensors Human computer interaction Wireless communication Wireless sensor networks Temperature distribution Clothing Cameras Universal Serial Bus Mirrors intelligent sensors

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New Type of Insoles Based on Air Pressure Arrays for Fall Detection in the Elderly

New Type of Insoles Based on Air Pressure Arrays for Fall De...

引用

International conference on Applied Nonlinear Dynamics, Vibration and Control, ICANDVC 2024

作者： Xu, Yiwei Lu, Junjie Wan, Xiao Chang, Tao Lei, Qian Huang, Zixin School of Electrical and Information Engineering Wuhan Institute of Technology Wuhan430205 China Hubei Key Laboratory of Digital Textile Equipment Wuhan Textile University Wuhan430200 China Institute of Robotics and Automatic Information Systems College of Artificial Intelligence Tianjin300350 China

ISBN: (纸本)9789819633166

For traditional fall detection insoles, the number of sensors is large, the cost is high, the sensor must correspond to the detection site one by one, and the resolution is low when the distance is far from the geometric center of the sensor, and the high sensitivity cannot be maintained in a large range. The rigid conduction of the force formed by the sensor and the soles of the elderly leads to poor comfort and low trust of the machine. A new soft insole for fall detection, based on an air pressure array, was designed and produced. Insole uses BMP280 pressure sensor and a 3D printed mold for silicone multi-layer injection molding. With four built-in airbags in one foot, tactile pressure can be converted to air pressure. STM32 microprocessor is used to construct the pressure information acquisition and processing system. Finally, Kalman filter algorithm is used to process the collected information. The experiment shows that the pressure detection system based on pressure change has high sensitivity and small repeatability error, and can effectively detect the pressure change and distribution of the sole. And reduce the noise interference, effectively improve the comfort and improve the precision and stability of recognition and acquisition equipment at the same time reduce the cost. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Pressure sensors

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Geotagging in Livestock Farming: AI-Based Technologies, Applications, and Challenges 4th

Geotagging in Livestock Farming: AI-Based Technologies, Appl...

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4th International conference on Emerging Trends and Technologies on intelligent Systems, ETTIS 2024

作者： Verma, Shuchi Sharma, Neelotpal Trivedi, Gaurav Ramjas College Delhi University Delhi India IIT Guwahati Guwahati India

ISBN: (纸本)9789819757022

The geotagging technology has emerged as a transformational tool for livestock management, especially in India, where agricultural technology is changing rapidly. The usage of geotagging technology that has been developed recently may bring changes in all agricultural activities, in livestock management, and in animal health monitoring system. The animal’s size, weight, and growth rate are the significant factors in determining a country’s economy in India. The applications of artificial intelligence (AI) merged with geotagging technology could be considered for discovering the disease surveillance, illegal trade in live animals, and control over livestock movement and overgrazing of pasture. The article discusses the use of geotagging in different animals like beef, pig, sheep, and buffalo industries in India through which cattle, pigs, sheep, and buffaloes can be tracked physically. The aim of this paper is to provide an overview of the potential uses, challenges, and advantages of combining geotagging with geographic information systems (GIS) for livestock management. In addition, this study focuses on the geotagging techniques operated through satellites, for instance, to sensor networks, machine learning and GPS, RFID, Bluetooth, and Wi-Fi items. With wide-range technologies, such as animal behavior sensors, health monitors, and environment data gathering ones like these can help the farmers to make accurate decisions for the productivity and manage resources in a better way. Nevertheless, many obstacles must be solved before geotagging could be very common. It is therefore clear that GPS-based information can facilitate hassle-free management of grazing lands and improve efficiency in livestock production across India as well as in the rest of the world. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： information management

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Incorporating LoRa Based Wireless sensor Network and Machine Learning Technologies to Improve Precision Agriculture 3rd

Incorporating LoRa Based Wireless Sensor Network and Machine...

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3rd International conference on intelligent Systems and Applications, ICISA 2024

作者： Shaikh, Aman Ranjan, Nihar M. Rai, Satayush Rao, Pranil Ashok Shinde, Ganesh Department of Information Technology JSPM’s Rajarshi Shahu College of Engineering Pimpri-Chinchwad India

ISBN: (纸本)9789819798384

In the contemporary landscape, the agricultural sector has undergone transformational changes, embracing progressive dynamics through the integration of cutting-edge technologies and methodologies. The global adoption of IoT, AI/ML, and similar innovations reflects their capacity to furnish precise, reliable, and consistent outcomes, enabling data-driven decision-making. The amalgamation of IoT and ML models emerges as a strategic choice to enhance data analytics, aiming at a substantial improvement in both the quantity and quality of agricultural yields. This paper seeks to elucidate the synergistic interaction of various agrarian elements, orchestrating the computational prowess facilitated by ML models and data derived from IoT sensors transmitted through LoRa-based Wireless sensor Network (WSN) infrastructure. The proposed system advocates a comprehensive three-tier architectural framework, wherein ML and IoT systems collaborate seamlessly to process data for field management. The envisioned system extends its utility by translating these predictions into visually comprehensible representations. The integrated approach signifies a departure from conventional methods, offering a novel pathway to ensure the sustainability of yield, encompassing both production and grading standards. This paper thus contributes to the discourse on leveraging advanced technologies for agricultural optimization in response to evolving global demands. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Decision making

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Research On Lane Change Fusion Decision Model For Complex Traffic 24

Research On Lane Change Fusion Decision Model For Complex Tr...

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3rd International conference on Signal processing, Computer networks and Communications, SPCNC 2024

作者： Chen, Yuanjing Zhang, Yongjie GuangXi Vocational Normal University Guangxi Nanning China Key Laboratory of Application Technology of Intelligent Connected Vehicle Guangxi Vocational Normal University Education Department of Guangxi Zhuang Autonomous Region Guangxi Nanning China

ISBN: (纸本)9798400710834

Aiming at the problem that driverless cars are unable to accurately detect the behavior of surrounding vehicles when performing lane changing in complex traffic. This paper proposes a lane changing fusion decision model to solve the problem of low effectiveness of driverless cars under complex traffic. The model improves the multi-objective trajectory prediction problem of driverless cars under complex traffic by using multi-source information fusion technology. At the same time, the input data is processed to realise the automatic learning and analysis of a large amount of data in combination with the deep learning algorithm of the artificial neural network. Statistical analysis of the model’s simulation experiment outcomes was conducted, and the fusion outcomes were then contrasted to the single-sensor situation. Under the optimized lane-changing model, the driverless car is improved in both lane-changing prediction and multi-vehicle perception, which solves the problems of inaccurate multi-objective trajectory prediction and inefficient lane-changing decision-making of the driverless car during lane-changing. © 2024 Copyright held by the owner/author(s).

关键词： Data fusion

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