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MQTT and blockchain sharding:An approach to user-controlled data access with improved security and efficiency

Blockchain(Research and Applications) 2023年第4期4卷 86-98页

作者： P.S.Akshatha S.M.Dilip Kumar Department of Computer Science and Engineering University of Visvesvaraya College of EngineeringBangalore UniversityBengaluruKarnataka 560056India

The rapid growth of the Internet of Things(IoT)has raised security concerns,including MQTT protocol-based applications that lack built-in security features and rely on resource-intensive Transport Layer Security(TLS)*** paper presents an approach that utilizes blockchain technology to enhance the security of MQTT communication while maintaining *** approach involves using blockchain sharding,which enables higher scalability,improved performance,and reduced computational overhead compared to traditional blockchain approaches,making it well-suited for resource-constrained IoT *** approach leverages Ethereum blockchain’s smart contract mechanism to ensure trust,accountability,and user ***,we introduce a shard-based consensus mechanism that enables improved security while minimizing computational *** also provide a user-controlled and secured algorithm using Proof-of-Access implementation to decentralize user access control to data stored in the blockchain *** proposed approach is analyzed for usability,including metrics such as bandwidth consumption,CPU usage,memory usage,delay,access time,storage time,and jitter,which are essential for IoT application *** analysis demonstrated that the approach reduces resource consumption,and the proposed system outperforms TLS and existing blockchain approaches in these metrics,regardless of the choice of the MQTT ***,thoroughly addressing future research directions,including issues and challenges,ensures careful consideration of potential advancements in this domain.

关键词： Blockchain Internet of Things MQTT Publish/Subscribe MQTT brokers HiveMQ Mosquitto EMQx Eclipse Sharding

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EDUVERSE: Inclusive and Immersive Metaverse Learning Environment for Education

EDUVERSE: Inclusive and Immersive Metaverse Learning Environ...

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2025 IEEE International Students' Conference on Electrical, Electronics and computer science, SCEECS 2025

作者： Madhubala, R. Gomathi, V. Jothikumar, Karthikeyan National Engineering College Department of Computer Science and Engineering Tamil Nadu Kovilpatti India

ISBN: (纸本)9798331529833

Eduverse is an advanced Virtual Reality (VR) platform developed to transform traditional education by creating interactive and immersive virtual learning environments. Designed to support both students and teachers, Eduverse simulates real-world classrooms, discussion rooms, and conference halls, enabling real-time communication and collaboration. One of its standout features is the accessibility tools, such as text-to-speech (TTS) and speech-to-text (STT) systems, which make the platform highly beneficial for students with disabilities. The TTS system helps students with speech impairments by converting their typed messages into spoken words, while the STT system transcribes spoken communication into text for students who have difficulty *** addition to accessibility, Eduverse offers customizable avatars, allowing students to represent themselves digitally, which helps reduce social anxiety and encourages participation. The platform also provides teachers with speech management tools, enabling them to moderate discussions, assign speaking privileges, and manage class activities. This ensures that every student has the opportunity to contribute, even in larger classes. Eduverse has been tested within a set of students, showing significant improvements in student engagement, participation, and inclusivity compared to traditional online learning platforms. Students with disabilities reported that the integrated accessibility tools allowed them to engage more fully in class discussions, making Eduverse a valuable solution for inclusive education. By combining a collaborative and supportive learning space, Eduverse demonstrates the potential to improve the way students learn and interact in the virtual world. © 2025 IEEE.

关键词： Students

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A Comprehensive Survey on Traffic Light Detection Sensors for Vehicle Safety and Automatic Emergency Braking Systems Using Deep Learning Techniques 3rd

A Comprehensive Survey on Traffic Light Detection Sensors fo...

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3rd International Conference on Intelligent Systems and Sustainable Computing, ICISSC 2023

作者： Gowri, B. Shyamala Priya, G. Vishnu Anjana, S. Arjun, V. Kodal, Devendraa Department of Computer Science and Engineering Easwari Engineering College Tamil Nadu Chennai India

ISBN: (纸本)9789819783540

The integration of Traffic Light Detection (TLD) systems with Advanced Emergency Braking Systems (AEBS) marks a critical milestone in enhancing road safety and paving the way for advanced autonomous driving. This survey paper provides a panoramic and extensive overview of the state-of-the-art TLD solutions leveraging sensors and deep learning techniques. With an increasing emphasis on accident prevention and traffic management, the intersection of TLD and AEBS has become a focal point of research and development. This survey begins by elucidating the fundamental challenges associated with TLD, including varying environmental conditions, occlusions, and complex traffic scenarios. We explore the pivotal role of sensors such as cameras, LiDAR, and radar in providing the requisite data for TLD, and delve into the intricacies of sensor fusion techniques for enhanced perception. Deep Learning has emerged as a cornerstone technology in TLD, enabling robust object detection, classification, and real-time decision-making. We meticulously analyze a spectrum of deep learning architectures including Single-Shot Detectors (SSD), Faster R-CNN, YOLO, and custom-designed networks tailored for TLD applications. Furthermore, the survey examines critical components of the TLD pipeline, encompassing data collection, preprocessing, model training, real-time inference, and integration with AEBS. Emphasis is placed on real-time constraints, multi-modal sensor fusion, and adaptability to diverse traffic light configurations. The paper also delves into the significance of accurate traffic light state prediction, going beyond mere detection to anticipate traffic light changes and optimize vehicle control actions. Human-centric interaction and privacy concerns are addressed, encompassing driver warnings, user interfaces, and data anonymization strategies. Moreover, the survey discusses the importance of safety, validation, and collaboration within the TLD and AEBS ecosystem, emphasizing compl

关键词： Road and street markings

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High Degree Dependency Study of Ensemble Models in Credit Card Fraud Analysis 2

High Degree Dependency Study of Ensemble Models in Credit Ca...

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2nd International Conference on Computational Intelligence, Communication Technology and Networking, CICTN 2025

作者： Rani, Shalini Tiwari, Mayank Tyagi, Shiva Department of Computer Science and Engineering Ajay Kumar Garg Engineering College Ghaziabad India

ISBN: (纸本)9798331530389

Credit card fraud detection is an increasingly critical issue due to the growth of digital transactions and the sophistication of fraudulent activities. This study proposes a hybrid framework combining Graph Neural Networks (GNNs), ensemble methods (e.g., XGBoost, Random Forest), and polynomial feature engineering to address key challenges, including class imbalance, non-linear data relationships, and real-time applicability. The system achieved a detection accuracy of 96.7%, precision of 96.9%, recall of 96.4%, and a false-negative rate of 3.6% on benchmark datasets. These improvements are attributed to the integration of advanced sampling techniques, polynomial feature transformations, and the hybrid architecture. The findings demonstrate the effectiveness of the proposed approach in enhancing fraud detection systems and provide a scalable solution for real-world applications. © 2025 IEEE.

关键词： Graph neural networks

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Employing Vision Transformer for Monocular Depth Estimation 2

Employing Vision Transformer for Monocular Depth Estimation

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2nd International Conference on Computational Intelligence, Communication Technology and Networking, CICTN 2025

作者： Chaudhary, Anubhav Tripathi, Dev Brat Mishra, Harsh Pathak, Prasoon Dixit, Ashish Department of Computer Science and Engineering Ajay Kumar Garg Engineering College Ghaziabad India

ISBN: (纸本)9798331530389

Monocular depth estimation (MDE) is an important task in computer vision, it enables a range of applications like robotic navigation, augmented reality and also used in surgical guidance. This paper shows the use of ViTs for MDE and focuses on the Depth Anything V2(DA-V2) model. DAV2 model leverages a teacher-student training approach and uses the synthetic and unlabeled real images which are designed to enhance the accuracy of depth estimation. DA-V2 is fine-tuned on EndoSLAM dataset and contains challenging visual scenes from endoscopic images of the stomach, colon and small intestine to check the adaptability of the model to medical depth estimation. Our experiment shows that the fine-tuned DA-V2 outperforms all the conventional models like Endo-SfMLearner and achieves reductions in root mean squared error(RMSE) and absolute relative error, across all anatomical datasets. DA-V2 achieves the RMSE values 1.245, 1.028, and 2.021 for the stomach, colon, and intestine datasets, respectively as compared to the SfMLearner's 6.387, 6.657, and 14.787. The findings show that ViTs are capable of achieving higher accuracy and generalization in monocular depth estimation tasks. This study highlights the effectiveness of DA-V2 and Vision Transformers in Monocular Depth Estimation and opens up possibilities for further model optimization. © 2025 IEEE.

关键词： Convolutional neural networks

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Optimized Hybrid Deep Learning Approach for Detecting DoS Attacks Using Feature Selection in Wireless Sensor Networks

Optimized Hybrid Deep Learning Approach for Detecting DoS At...

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2025 IEEE International Conference on Computational, Communication and Information Technology, ICCCIT 2025

作者： Sasson Taffwin Moses, S. Abraham David, L. Emil Selvan, G.S.R. Ramkumar, M.P. Thiagarajar College of Engineering Department of Computer Science and Engineering Tamil Nadu Madurai India

ISBN: (纸本)9798331512965

The rapid growth of smart cities, healthcare monitoring, and environmental sensing relies heavily on the real-time data processing capabilities of Wireless Sensor Networks (WSNs). However, these networks face significant security challenges, particularly from malicious activities like Denial of Service(DoS) attacks, which can disrupt critical operations. A hybrid intrusion detection model that combines Convolutional Neural Networks (CNN) and Deep Neural Networks (DNN) to improve the detection of such threats has been proposed. The preprocessing phase includes the Synthetic Minority Over-sampling Technique (SMOTE) which mitigates class imbalances ensuring robust performance across diverse attack scenarios. The model incorporates advanced feature selection techniques including SelectKBest and Grey Wolf Optimization (GWO) to identify the most critical features for enhanced detection accuracy. The CNN component effectively captures local traffic patterns, which are then refined by the DNN for accurate classification. This hybrid model's effectiveness in detecting diverse DoS attack scenarios is evaluated using WSN-DS dataset, ensuring robust security for WSNs. By achieving an impressive 98% detection accuracy, this hybrid model proves to be effective in intrusion detection. Its applications can significantly strengthen the security and reliability of WSNs, ensuring uninterrupted operations in key sectors such as smart cities, healthcare, and environmental monitoring. © 2025 IEEE.

关键词： Network intrusion

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A Secure and Efficient Cloud Storage System Using Advanced Encryption Standard Algorithm for Data Protection 5th

A Secure and Efficient Cloud Storage System Using Advanced E...

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5th EAI International Conference on Cognitive Computing and Cyber Physical Systems, IC4S 2024

作者： Babu, Gurujukota Ramesh Chintalapati, Phaneendra Varma Sree, Pokkuluri Kiran Satish Kumar, Kode Asha, A.V.S. Maneesha, Boddu Department of Computer Science and Engineering Shri Vishnu Engineering College for Women Bhimavaram India

ISBN: (纸本)9783031770777

The Cloud Computing (CC) is a model which treats the resources as an integrated entity on the internet, cloud. Cloud computing is an unique environment or network in which process, access and maintenance are done by anywhere from the world. The CC is a customized internet-based web server. Data security is receiving more and more attention with the expanding usage of cloud storage systems and due to its challenging circumstances. Any cloud data storage model’s fundamental objective is to provide simple data access without compromising the security of the data. The main concerns of any cloud storage system are ensuring safety and security. Big data security and privacy have emerged as a problem that prevents the usage of cloud services by the organization. Earlier privacy-preserving methods had a number of drawbacks includes completely dependence on third parties, a lack of performance efficiency, accurate data analysis, and data privacy. This analysis presents a secure and efficient cloud storage system that uses the advanced encryption standard algorithm for data protection. The user files are encrypted with the AES (Advanced Encryption Standard) standard. The efficiency of the method that is being provided is evaluated in terms of execution, decryption, and encryption times. © ICST Institute for computer sciences, Social Informatics and Telecommunications engineering 2025.

关键词： Cloud security

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An AI-Based Intrusion Prevention System to Enhance Cloud Security 4

An AI-Based Intrusion Prevention System to Enhance Cloud Sec...

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4th International Conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Sarumathy, P. Rajasree, S. Chandrasekar, A. St. Joseph's College of Engineering Computer Science and Engineering Chennai India

ISBN: (数字)9798331523923

ISBN: (纸本)9798331523923

Data breaches have become increasingly prevalent in cloud environments, demanding robust security measures. In an era where data breaches pose significant threats to organizational security and individual privacy, the protection of sensitive data has become paramount. A comprehensive strategy is proposed for analyzing sensitive data and preventing intrusions using a multi-modal framework. The methodology integrates AI techniques, Long Short-Term Memory (LSTM) networks for classification of data as sensitive/non-sensitive and Parameter Efficent Fine-Tuning(PEFT) for categorization of sensitive data. Additionally, organization-specific regular expression (regex) pattern matching algorithms are utilized. Together, these components form an Intrusion Prevention System (IPS) capable of quickly identifying and alerting against unauthorized changes, with a focus on detecting sensitive data. The system also addresses pivot attacks, where attackers exploit vulnerabilities to access data across servers within the same network. By representing system components and their relationships as graphs, our approach enhances the detection and prevention of such attacks. This graphical representation facilitates a better understanding of component relationships and helps identify redundant storage elements, optimizing resource allocation. Our rigorous evaluation demonstrates the effectiveness of our approach in mitigating data breach risks and protecting sensitive information. © 2025 IEEE.

关键词： Technological innovation Sentiment analysis Prevention and mitigation Cloud computing security Data breach IP networks Servers Resource management Pattern matching Long short term memory

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Implementing Machine Learning Techniques for Hair Health Prediction in Follicle Futures 2

Implementing Machine Learning Techniques for Hair Health Pre...

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2nd International Conference on Data science and Information System, ICDSIS 2024

作者： Duraisamy, Premkumar Vidhya, V. Preetha, T. Niranjani, V. Kpr Institute Of Engineering And Technology Department Of Computer Science And Engineering Coimbatore India Sri Eshwar College Of Engineering Department Of Computer Science And Engineering Coimbatore India

ISBN: (纸本)9798350381665

This study explores the effectiveness of various machine learning algorithms in forecasting hair health using a comprehensive dataset incorporating individual traits and lifestyle elements. Logistic regression, random forest, and AdaBoost were assessed, with random forest notably achieving a remarkable 94.6% accuracy. These results emphasize machine learning's potential for precise, non-invasive personalized hair health predictions. Key factors such as genetics, diet, and stress were pinpointed, informing targeted care approaches. The research underscores machine learning's promise in accurately anticipating hair loss and suggests future avenues for expanding datasets, integrating diverse data sources, and developing user-friendly tools for improved hair care management. © 2024 IEEE.

关键词： Support vector machines

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Traffic flow prediction based on graph convolutional networks with a parallel attention network and stacked gate recurrent units

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Multimedia Tools and Applications 2025年第15期84卷 14329-14358页

作者： Xia, Dawen Ao, Yuce Wei, Xiaoduo Li, Yunsong Chen, Yan Hu, Yang Li, Yantao Li, Huaqing College of Data Science and Information Engineering Guizhou Minzu University Guiyang550025 China Department of Automotive Engineering Guizhou Traffic Technician and Transportation College Guiyang550008 China College of Computer Science Chongqing University Chongqing400044 China College of Electronic and Information Engineering Southwest University Chongqing400715 China

Accurate traffic flow prediction is essential to address traffic issues and assist traffic managers make informed decisions in intelligent transportation systems. Extracting potential features from traffic data is challenging due to the complex topology of urban road networks and the time-varying traffic flow. To capture the global spatiotemporal characteristics of traffic flow, we propose a novel model based on graph convolutional networks with a parallel attention network and stacked gated recurrent units (PAGCN-SGRU). First, the parallel attention (PA) network enhances the feature representation of global traffic road nodes and road segments. Then, the graph convolutional networks (GCN) are designed to extract spatial characteristics. Next, the stacked gate recurrent units (SGRU) are employed to capture temporal features. Finally, PAGCN-SGRU discovers global spatiotemporal features for traffic flow prediction. The experimental results demonstrate that the accuracy of PAGCN-SGRU under the SZ-dataset is improved by 9.76%, 72.54%, 5.76%, 16.07%, 2.07%, 1.82%, 3.35%, and 6.59%, respectively, compared to that of HA, ARIMA, SVR, GCN, T-GCN, A3T-GCN, ST-GCN, and DCRNN. In the Los-dataset, the accuracy values increase by 7.11%, 8.94%, 6.66%, 7.77%, 3.43%, 2.45%, 2.28%, and 4.09%, respectively. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Forecasting

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