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检索条件"任意字段=3rd International Symposium on Information Processing in Sensor Networks"

共 1445 条记录，以下是1-10 订阅

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ESMDP: Encrypted ML Framework for Secure Medical Data processing 3rd

ESMDP: Encrypted ML Framework for Secure Medical Data Proces...

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3rd international Conference on Network Security and Blockchain Technology

作者： Reddy, B. Pradeep Kumar Chatterjee, Ayantika Indian Instutite Technol Kharagpur Kharagpur W Bengal India

ISBN: (纸本)9789819780501;9789819780518

In this age of Internet of Things (IoT), remote health monitoring and out-of-hospital treatment is not a distant dream. Wearable medical sensors and smartphones in the current IoT-driven environment allow for real-time monitoring of important health markers, with data quickly sent to distant specialists for analysis. Moreover, due to the onset of technological advancements like Electronic Health Records (EHR), IoT-enabled devices, Public Open health Data (POD), Linked Opened Data (LOD), remote health monitoring, vast quantities of information created in this domain as big data in health care. Intelligent storage and analysis of this big data may lead to generate critical insights for future predictions. The storage challenge of big data can be mitigated by the use of cloud computing. However, processing of Personal Health information (PHI) on remote medical servers or cloud raises serious concern about the security of critical PHI. One immediate solution is for the information proprietors to transmit and store their delicate information in encrypted form. However, traditional encryption schemes do not support processing on that encrypted form of data. To take full advantage of cloud computing and outof-hospital treatment, there should be a solution to process on that encrypted form of data directly. In this paper, we propose a framework which supports secure data transmission from medical sensors to distant cloud server or remote medical organizations, secure processing on that data to provide distant medical treatments and perform secure machine learning-based analytics on such sensor-generated datasets. For this secure processing, we use Fully Homomorphic Encryption(FHE) scheme, which supports direct processing on encrypted data. Our implementation shows the proposed secure framework is capable of disease detection remotely in an encrypted domain confirming PHI data confidentiality. The framework also supports performing encrypted machine learning algorithm on clo

关键词： IoT Security eHealth Fully homomorphic encryption (FHE)

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Natural Language processing in Geographic information Systems 3

Natural Language Processing in Geographic Information System...

引用

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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Towards Energy-Efficient Optimized Localization in Wireless sensor networks

Towards Energy-Efficient Optimized Localization in Wireless ...

引用

Device Intelligence, Computing and Communication Technologies, (DICCT), international Conference on

作者： Hina Singh Preeti Yadav Mano Yadav Abhishek Gautam Ashutosh Shankhdhar Vinay Rishiwal Deptt. of CSIT M.J.P. Rohilkhand University Bareilly India Deptt. of Computer Science Bareilly College Bareilly India

ISBN: (数字)9798331543358

ISBN: (纸本)9798331543365

Wireless sensor networks (WSNs) are essential in monitoring the environment to gather real-time data. Localization of sensor nodes is crucial for efficient data information processing and transmission. The traditional localization techniques face many challenges, like high energy consumption and computational complexity, resulting in inefficacies in the outcome. To overcome the challenges of traditional localization techniques, this paper proposes an Optimized Energy-Efficient Localization Technique (OEELT) scheme. The proposed scheme is a combination of the Enhanced Distance Vector (DV-Hop) algorithm with Particle Swarm Optimization (PSO) and Grey Wolf Optimizer (GWO) algorithms. The simulation results show that the proposed scheme outperforms the state-of-the-art techniques in terms of accuracy, energy consumption, and computation time. It reduces localization error to 4.56m, energy consumption from 0.85J to 0.51J, and improves the computation time from 2.4s to 1.5s.

关键词： Location awareness Wireless sensor networks Energy consumption Simulation Energy efficiency Vectors Real-time systems Particle swarm optimization Optimization Monitoring

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

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 Baddi 174103 India School of Engineering and Technology Jain (Deemed to be University) 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 Rajpura 140417 India Siksha 'O' Anusandhan (Deemed to be University) 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： Communicate information Infrastructure networks Troubleshooting

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A Visionary Analysis on Ways and Techniques Used for Identification of Citrus Plant Disease

A Visionary Analysis on Ways and Techniques Used for Identif...

引用

international symposium on Advanced Computing and Communication (ISACC)

作者： Arundhati Bora Parismita Sarma Dept. of Information Technology Gauhati University Guwahati India

ISBN: (数字)9798331523893

ISBN: (纸本)9798331523909

This review paper discusses the identification of different leaf diseases in plants with specific reference to the lemon group. In recent times, it has been seen that leaf diseases can be identified very accurately using different machine learning and deep learning methods from the digital images of the infected leaves. The accuracy varies depending on the tagged sample size, variation of the samples, and method used. In this paper, the authors have analyzed almost forty (40) research papers and prepared a number of tables for easy understanding of techniques used and their performance in terms of percentage of accuracy. Segmentation of leaves is performed in most techniques in the image pre-processing phase. Analysis is carried out on the given tables from different perspectives and a future way of working is formulated from this survey of research.

关键词： Deep learning Surveys Plant diseases Accuracy Reviews Computational modeling Transfer learning Neural networks Data models Object recognition

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Smart IoT-enabled Water Quality Management using Generative AI and Super-Resolution GAN for Enhanced Monitoring and Prediction

Smart IoT-enabled Water Quality Management using Generative ...

引用

Integrated Circuits and Communication Systems (ICICACS), IEEE international Conference on

作者： Shofia Priyadharshini D G.P. Ramesh Department of Electronics and Communication Engineering St. Peter's Institute of Higher Education and Research Chennai India

ISBN: (数字)9798331508456

ISBN: (纸本)9798331508463

An essential component of sustainable development is water quality management, which requires accurate monitoring and forecasting capabilities. Conventional methods for assessing water quality often face challenges such as sparse data, sensor inaccuracies, and real-time processing limitations. To address these challenges, this study proposes a Smart IoT-Enabled Water Quality Management System that integrates Super-Resolution Generative Adversarial networks (SR-GAN) with artificial intelligence (AI) for enhanced monitoring and prediction. The system utilizes IoT -based smart sensors to collect real-time data on temperature, turbidity, oxygen concentration, pH levels, and other water quality ***-GAN enhances both the temporal and spatial resolution of low-quality sensor data, enabling better feature extraction and more precise information. Generative AI models are then employed for automated decision-making, anomaly detection, and forecasting, effectively identifying potential water pollution threats. Compared to conventional AI and deep learning methods, the proposed framework achieves superior performance in terms of response time efficiency (30% improvement), data quality, and prediction accuracy (98.7%). The integration of edge computing ensures low latency and real-time data processing. This innovative approach significantly enhances water quality monitoring accuracy, facilitating proactive measures and sustainable water resource management.

关键词： Temperature sensors Accuracy Generative AI Superresolution Water quality Generative adversarial networks Real-time systems Reliability Monitoring Anomaly detection

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sTransforming Radiology with IoT-Enabled Smart Wireless Solutions in Healthcare Infrastructure

sTransforming Radiology with IoT-Enabled Smart Wireless Solu...

引用

Disruptive Technologies (ICDT), international Conference on

作者： Karishma Sharma Amit Kumar Pathak Mangalayatam University Aligarh Uttar Pradesh India T S Mishra University Lucknow India

ISBN: (数字)9798331519582

ISBN: (纸本)9798331519599

This study aims to review the use of Internet of Things (IoT) technology for radiology applications in order to improve diagnostic accuracy, organizational workflow, and patient-centric otherwise known as patient-oriented care. The main intent is to evaluate the role of IoT solutions in changing radiology processes through wireless connectivity, intelligent diagnosis, and maintenance. Quantitative comparison of imaging performance was performed together with the qualitative data collected from healthcare practitioners. information was gathered from IoT infused radiology systems, including real-time data exchange capabilities, equipment availability and artificial intelligence derived diagnosis. The findings point that IoT systems enhance diagnostics productivity by minimizing processing time and offer offsite diagnostics across 5G networks and the cloud. Maintenance was done based on a prediction that reduced equipment time off and analytics threw light on early diseases stages with special focus on cancer treatments. Overcoming challenges with the use of SOP and improved security of systems will go further to advance the prospect of IoT in the healthcare industry. The study will highlight the need to create and sustain practice improvement for the enhancement of resilient patient-centered radiology.

关键词： Wireless communication Wireless sensor networks Accuracy Imaging Medical services Radiology Real-time systems Maintenance Internet of Things Communication system security

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Reinforcement Learning based Smart Energy Management System for Indoor Lighting

Reinforcement Learning based Smart Energy Management System ...

引用

international symposium on Advanced Computing and Communication (ISACC)

作者： Himanshu Ladhiya Rajib Malik Department of Electronics and Communication Engineering Indian Institute of Information Technology Guwahati Guwahati Assam India

ISBN: (数字)9798331523893

ISBN: (纸本)9798331523909

The design of energy-efficient indoor lighting systems is a very interesting research challenge. Although this field has experienced significant developments, Reinforcement Learning (RL) remains relatively under explored. With reward-based optimization, RL seems to be the promising approach toward achieving sustainable and adaptive lighting solutions. The proposed approach introduces an RL-driven lighting control system that dynamically adjusts the brightness and orientation of LED lighting to optimize energy consumption while ensuring comfort for the users. The approach employs the Soft Actor-Critic (SAC) algorithm for processing of sensor data, related to light intensity, glare, and user response, to come up with lighting strategies at any point in time. The prototype system developed in simulation tests reveal that, compared to existing systems, energy usage can be reduced up to 25% without compromising the level of user comport. The results demonstrate the next generation of energy-efficient interior lighting solutions by providing a scalable, intelligent solution to smart building applications.

关键词： Technological innovation Smart buildings Energy conservation Lighting Reinforcement learning Pulse width modulation Light emitting diodes Energy efficiency Lighting control Testing

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Natural Language processing in Geographic information Systems

Natural Language Processing in Geographic Information System...

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Intelligent Data Communication Technologies and Internet of Things (IDCIoT), international Conference on

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

ISBN: (数字)9798331527549

ISBN: (纸本)9798331527556

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 contextawareness 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 geodescriptions 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.

关键词： Deep learning Visualization Scalability Urban planning Disaster management User interfaces Natural language processing Spatial databases Geospatial analysis Usability

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Development of an Expert System for Advanced Podiatric Treatments and Ergonomics

Development of an Expert System for Advanced Podiatric Treat...

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international symposium on Advanced Computing and Communication (ISACC)

作者： P Vigneshwaran V Buvanesh Pandian M Radha B Mohan R Santhana Krishnan J Relin Francis Raj Department of Electronics and Communication Engineering P.S.R. Engineering College Sivakasi India Department of Electrical and Electronics Engineering Kalasalingam Academy of Research and Education Krishnankoil India Department of Electronics and Communication Engineering Francis Xavier Engineering College Tirunelveli India Department of Electronics and Communication Engineering SCAD College of Engineering and Technology Cheranmahadevi India Department of Electronics and Communication Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India

ISBN: (数字)9798331523893

ISBN: (纸本)9798331523909

An expert system tailored for foot pressure analysis and imprint detection, with a focus on enhancing podiatric treatments and ergonomics. The system comprises three key components: a foot pressure analyser, an image processing-based foot imprint analyser, and a machine learning-driven data analyser. The foot pressure analyser employs an array of FSR sensors with piezo resistive material Velostat, accommodating various foot types. The collected data is processed using thermal image algorithms to create foot pressure images. The image processing unit of the system utilizes edge detection techniques to extract relevant features from the foot pressure images, forming a dataset for machine learning. A neural network is trained using this dataset to identify lower limb abnormalities accurately. The expert system encompasses an extensive knowledge base, storing foot anatomy, pressure point norms, common foot conditions, footwear information, and also analyses lower limb extremities like arthritis and synovial accumulations etc., especially helps in geriatric care. The system generates comprehensive reports summarizing the analysis results, facilitating treatment planning for specific applications, age groups, genders, and foot shapes and giving real-time feedback and recommendations based on the Expert’s knowledge to aid in treatment planning, ensuring accessibility and data security. Future developments include 3D printing integration, research support, and machine learning enhancements. The system promises to advance foot health diagnostics and treatments significantly, benefiting healthcare professionals and patients.

关键词： Limbs Shape Medical services Machine learning Footwear Three-dimensional printing Planning Expert systems Foot sensor arrays

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