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Blockchain-Driven Key Management for Secure IoT

Blockchain-Driven Key Management for Secure IoT

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Blockchain and Distributed Systems Security (ICBDS), IEEE International Conference on

作者： Brunda Sri Lakshmi K N Bettahally N. Keshavamurthy Dept of Computer Science and Engineering National Institute of Technology GOA Goa India

ISBN: (数字)9798350354348

ISBN: (纸本)9798350354355

Rapid growth of Internet of Things(IoT) technology has resulted an extraordinary surge in number of interconnected devices, raising significant concerns regarding security and privacy. Group key management plays a crucial role in securing communication among IoT devices within a network. In this paper, a novel approach to group key management is proposed leveraging blockchain technology to address the challenges of scalability, decentralization, and trust in IoT environments. Our solution employs blockchain as a distributed ledger to securely store and manage group keys, ensuring transparency, integrity, and resilience against attacks. We present a detailed architecture and protocol for group key generation, distribution, and revocation using smart contracts on the blockchain. Furthermore, we evaluate performance and security of our approach through simulation and analysis, demonstrating its effectiveness in providing secure and efficient key management for IoT devices. This research advances IoT security, providing a foundation for creating IoT ecosystems that are resilient and trustworthy.

关键词： Performance evaluation Distributed ledger Scalability Smart contracts Systems architecture computer architecture Blockchains Security Internet of Things Surges

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A New Half-Bridge/Dual-Stacked-Switches Structured Electrolytic Capacitor-less AC/DC Bi-Directional On-Boad Charger for High-Voltage EV Battery

A New Half-Bridge/Dual-Stacked-Switches Structured Electroly...

引用

IEEE Energy Conversion Congress and Exposition, ECCE

作者： Siamak Derakhshan John Lam Dept. Electrical Engineering and Computer Science York University Toronto Canada

ISBN: (数字)9798350376067

ISBN: (纸本)9798350376074

This paper introduces a novel half-bridge (HB)/dual-stacked-switches based electrolytic capacitor-less bidirectional AC/DC converter for high voltage (HV) electric vehicle (EV) systems. The proposed two-stage AC/DC converter incorporates a closed-loop Power Factor Correction (PFC) mechanism based on the dq model of the input LCL filter, ensuring high-quality input current with minimized Total Harmonic Distortion (THD) during both conversion and inversion operations of the converter. Moreover, the front-end HB PFC circuit operates in Continuous Conduction Mode (CCM) that results in the elimination of the high peak current in the switches. Additionally, a dedicated closed-loop control system is deployed in the dual-stacked-switches resonant converter to significantly reduce the output voltage ripple, thus eliminating the use of bulky electrolytic-type storage capacitors. Output voltage regulation is realized by using Variable Frequency Modulation (VFM). Soft-switching operation is guaranteed for all the switches and diodes using CLLC resonant circuit for both conversion and inversion modes of operation. The proposed circuit’s performance is verified through a 1kW, 120Vrms/800Vdc proof-of-concept prototype.

关键词： AC-DC power converters Total harmonic distortion Capacitors RLC circuits Switches Resonant converters High-voltage techniques Voltage control Integrated circuit modeling Power harmonic filters

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An Innovative Approach for Detection of Motorcyclists Helmet Non-Compliance using Edge Computing

An Innovative Approach for Detection of Motorcyclists Helmet...

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Data Intelligence and Cognitive Informatics (ICDICI), International Conference on

作者： Mulpuri Jithendra M. Madhava Krishna M. Charan Sai P. Sri Harsha Supriya M Dept. of Computer Science and Engineering Amrita School of Computing Bengaluru India

ISBN: (数字)9798350389609

ISBN: (纸本)9798350389616

Using innovative technologies and creative strategies can help reduce fatalities and lessen the financial impact of accidents. This suggests a significant improvement in global traffic law enforcement and road safety advocacy. The study proposed in this paper uses an innovative approach to address the challenge of motorcyclists neglecting traffic regulations through a deep learning-driven framework. Using advanced pre-trained object detection models, the system accurately identifies helmetless motorcyclists and number plates. In addition, the proposed model does the Optical Character Recognition (OCR) to extract the license plate information from the identified motorcyclists who are not wearing helmets. The architecture was tested using an NVIDIA Jetson as the edge device, delivering quick performance, efficiently managing large data volumes, and optimizing resource use. Extensive testing and in-depth analysis show that the proposed approach is robust and highly beneficial. The proposed system integrates OCR algorithms, edge computing capabilities, and advanced deep learning techniques to offer a scalable solution for assisting law enforcement entities and enhancing traffic safety initiatives.

关键词： Law enforcement Computational modeling Writing Helmets Road safety Safety Time factors License plate recognition Edge computing Testing

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Study on Networking Models of SDN using Mininet and MiniEdit

Study on Networking Models of SDN using Mininet and MiniEdit

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

作者： Sreebha Bhaskaran Supriya M. Dept. of Computer Science and Engineering Amrita School of Computing Bengaluru India

Software Defined Networking (SDN) has brought about a revolutionary shift in network management and control. The driving force behind SDN's development is the need for enhanced network programmability and scalability to accommodate rapidly changing applications and services. By decoupling the control plane from the data plane, network managers gain centralized control and programmability over resources. This study explores the architecture, encompassing application, control layer, and data layer, while highlighting the pivotal role of controllers. In addition, this work also explores SDN using Mininet and MiniEdit emulators for designing custom topologies built based on real time scenarios. Consequently, the research aims to pinpoint the optimal topology suited for various applications.

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Automating Requirements Review in the Automotive Sector: A Tailored AI Approach

Automating Requirements Review in the Automotive Sector: A T...

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IEEE International Conference on Requirements engineering

作者： Sivajeet Chand Chang Li Cristina Martinez Montes Beatriz Cabrero-Daniel Jennifer Horkoff Dept. of Computer Science and Engineering Chalmers | University of Gothenburg Gothenburg Sweden

Requirements serve as the foundation for defining what a software product should accomplish, highlighting the importance of clear and well-written specifications [1]. Deficient requirements often lead to defects in de... 详细信息

ISBN: (数字)9798350395112

ISBN: (纸本)9798350395129

关键词： Reviews Software Requirements engineering Artificial intelligence Automotive engineering

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Developing a Self-Explanatory Transformer

Developing a Self-Explanatory Transformer

引用

IEEE/ACM Symposium on Edge Computing (SEC)

作者： Rasha Karakchi Ryan Karbowniczak Dept. of Computer Science and Engineering University of South Carolina Columbia USA

ISBN: (数字)9798350378283

ISBN: (纸本)9798350378290

While IoT devices provide significant benefits, their rapid growth results in larger data volumes, increased complexity, and higher security risks. To manage these issues, techniques like encryption, compression, and mapping are used to process data efficiently and securely. General-purpose and AI platforms handle these tasks well, but mapping in natural language processing is often slowed by training times. This work explores a self-explanatory, training-free mapping transformer based on non-deterministic finite automata, designed for Field-Programmable Gate Arrays (FPGAs). Besides highlighting the advantages of this proposed approach in providing real-time, cost-effective processing and dataset-loading, we also address the challenges and considerations for enhancing the design in future iterations.

关键词： Training Automata Transformers Real-time systems Natural language processing Encryption Security Internet of Things Field programmable gate arrays Edge computing

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An Efficient Deep Learning Approach for Jute Pest Classification Using Transfer Learning

An Efficient Deep Learning Approach for Jute Pest Classifica...

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International Conference on Electrical engineering and Information & Communication Technology (ICEEICT)

作者： Muhammad Tanvirul Islam Md. Sadekur Rahman Dept. of Computer Science and Engineering Daffodil International University Dhaka Bangladesh

ISBN: (数字)9798350385779

ISBN: (纸本)9798350385786

Jute is considered as one of the most vital crops in the world. For some countries jute is the principal source of earnings and GDP. One of the primary elements influencing jute yield is jute pests. Accurate pest identification makes it possible to take prompt preventative action to minimize financial losses. Considering the fact, to classify jute pests, the study suggests different jute pest classification models, which are based on transfer learning. The best model offers high performance and resilience. A VCI-validated dataset comprising 7235 images has been utilized in the analysis. The dataset encompasses images classified into 17 distinct jute pest classes. The dataset is already divided into three categories train, test and validation. To increase the dataset size, data augmentation is applied to the training set. To improve performance, all the models were integrated with the transfer learning model. VGG 16, ResNetl0l, DenseNet201, InceptionV3, Xception, and MobileN etV2 were used to train the parameters on the publicly available ImageN et dataset followed by some customized dense layers. The models were assessed using different types of metrics, including confusion matrix, F1 score, precision, and recall. Compared to other models DenseNet201 outclassed other models, acquiring 97% accuracy. The fundamental information and technical support for jute pest classification are provided by this study.

关键词： Training Measurement Deep learning Economic indicators Transfer learning Crops Data augmentation

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IoT-Enhanced Smart Bulletin Board: A Multi-Functional Connectivity Hub

IoT-Enhanced Smart Bulletin Board: A Multi-Functional Connec...

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

作者： Mallikarjuna Rao Gundavarapu VeerlapalIi Swapna Enugu Sriamsh Reddy Pindi Praveen Kumar Abhinay Prathapani Kothapally Shivaji Dept. of Computer Science and Engineering Gokaraju Rangaraju Institute of Engineering and Technology Hyderabad India

The problem with traditional notice boards is their static nature, leading to outdated information and manual effort to update. An IoT-based smart notice board solves this by providing real-time updates but requires addressing challenges such as connectivity, power management, compatibility, user interface, security, and maintenance. Addressing these challenges ensures an effective and user-friendly smart notice board. The proposed model involves using an LED dot matrix, ESP32, Kodular app, Firebase Cloud and DS3231 module. The app sends messages to the Firebase then it passes the message to ESP32, which displays message on the LED dot matrix, We use Firebase cloud which provides infinite range. Firebase provides a NoSQL database that allows developers to store and synchronize data in real-time. It is suitable for applications that require real-time updates, such as chat apps, collaborative tools, and live dashboards. The DS3231 module shows real-time information. This study implements a feature that allows the user to retrieve the current time and temperature by pressing a button in the Kodular app. When the button is pressed, a command is sent to the ESP32, which retrieves the relevant data from the DS3231 module and sends it to display. This study creates a buffer to store messages, accessible by indexes, allowing easy retrieval. The proposed system provides a comprehensive solution for displaying messages, real-time data, and efficient message storage. QR codes are also provided to scan.

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Enhancing Stock Price Predictions Through LSTM-based Recurrent Neural Networks 2

Enhancing Stock Price Predictions Through LSTM-based Recurre...

引用

2nd IEEE International Conference on Integrated Circuits and Communication Systems, ICICACS 2024

作者： Krishna Kumar, G.V. Natarajan, B. Venkatraman, K. Devi, S. Gayathri Selvam, Prabu Nagarajan, N.R. Amrita Vishwa Vidyapeetham Dept. of Artificial Intelligence Chennai India VIT University School of Computer Science and Engg. Chennai India Amrita School of Computing Amrita Vishwa Vidyapeetham Dept. of CSE Chennai India Sri Ramakrishna Institute of Technology Dept. of Computer Science and Engg. Coimbatore India K. Ramakrishnan College of Engineering Department of ECE Trichy India

ISBN: (纸本)9798350317558

Analyzing finances has become increasingly challenging in today's investment landscape, where making valuable and informed investment decisions is crucial. The fluctuation of share prices plays a pivotal role in determining investors' profits or losses. Current forecasting techniques encompass both linear and non-linear algorithms. However, these methods primarily emphasize forecasting changes in the stock index or forecasting prices for individual companies based on their daily concluding rates. The proposed methodology introduces a model-agnostic method. Rather than conforming data to a specific model, this approach aims to identify unseen trends inherent in the data through deep learning models. In this study, three distinct deep learning models - HMM, RNN, and LSTM - are employed to predict prices using the dataset from ICICI Bank. Their performances are compared, revealing that the LSTM model adeptly discerns evolving trends. The LSTM model displays the lowest error percentage at 2.36%, outperforming other models such as HMM (7.32%) and RNN (3.94%). © 2024 IEEE.

关键词： deep learning LSTM stock market stock Price prediction Time series

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Enhancing Road Safety Through Fitness-to-Drive Metrics: The NextPerception Project on Driver Behavior Analysis and Gamification

Enhancing Road Safety Through Fitness-to-Drive Metrics: The ...

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Consumer Communications and Networking Conference, CCNC IEEE

作者： Maria Mengozzi Manuel Andruccioli Silvia Mirri Giovanni Delnevo Roberto Girau DISI Dept. of Computer Science and Engineering University of Bolgna Bologna Italy

Driving involves numerous factors demanding a driver's attention. To enhance road safety, there has been a significant increase in the development and implementation of vehicle sensors. These sensors, in conjunction with mobile applications, can assess a driver's emotional state and focus, providing feedback to enhance their attention. This paper explores the monitoring of driver behavior, emphasizing the effects of distractions and emotions on driving performance. Stemming from the European initiative, NextPerception, this research focuses on advancing perception sensors and refining distributed intelligence models in various domains, including automotive. The initiative aims to develop a range of sensors, from obstacle detection tools to those monitoring a driver's eye movements and vital signs. A key goal is to define a “fitness-to-drive” metric, representing the driver's attentiveness level. The project also seeks to use gamification to emphasize the importance of this metric, increasing drivers' awareness of their driving skills. The ultimate aim is to create a system that determines fitness-to-drive based on distractions and emotions, integrating this into a prototype simulating sensor data, and introducing a web-based application to display this data for a community of drivers.

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