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A Novel Hybrid Memory System based on Non-Volatile Memory for Edge intelligent IoT Devices 2

A Novel Hybrid Memory System based on Non-Volatile Memory fo...

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2nd international conference on intelligent Data Communication Technologies and Internet of Things, IDCIoT 2024

作者： Sun, Hao Hao, Xiaoran Ni, Mao School of Information and Control Engineering China University of Mining and Technology Xuzhou China Institute of Microelectronics Chinese Academy of Sciences Beijing China

ISBN: (纸本)9798350327533

The rapid progression of edge intelligence has led to a notable increase in the prominence of deploying artificial intelligence applications on edge devices. To overcome the performance limitations of edge device memory systems, this research study introduces Non-Volatile Memory (NVM) and establishes a hybrid structure with traditional Dynamic Random-Access Memory (DRAM), resulting in a low-power memory system. In order to further optimize the performance of this hybrid storage system, the paper proposes weight coefficients for read and write operations, taking into account the potential negative impacts of these operations on NVM devices. Additionally, a method is designed to consider access frequency and utilize a migration algorithm to prevent redundant migrations. From a system perspective, an in-memory file system is implemented to enable rapid file access, significantly enhancing the storage speed of applications for artificial intelligence. Experimental results demonstrate that the proposed hybrid memory architecture provides an average energy and delay product advantage of over 30%. Furthermore, the integration of the in-memory file system yields substantial enhancements in file access speed, resulting in a manifold increase. This approach presents a new methodology for implementing edge artificial intelligence on Internet of Things (IoT) devices. © 2024 ieee.

关键词： Edge devices Edge intelligence Hybrid memory Internet of Things Non-volatile memory

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Application of Transformer Models for Autonomous Off-Road Vehicle control: Challenges and Insights 4

Application of Transformer Models for Autonomous Off-Road Ve...

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4th international conference on intelligent cybernetics technology and Applications, ICICyTA 2024

作者： Bin Azhar, M. A. Hannan Meszaros, Zoltan Islam, Tasmina School of Engineering Technology and Design Canterbury Christ Church University Canterbury United Kingdom King's College London Department of Informatics London United Kingdom

ISBN: (纸本)9798331506490

This paper addresses the critical challenge of advancing autonomous vehicle control in off-road environments, where traditional driver assistance technologies often prove inadequate. While AI-powered systems in modern vehicles have become highly effective at navigating structured urban landscapes, adapting these technologies for rural and off-road settings remains a complex and necessary undertaking due to varied and unpredictable obstacles. Off-road scenarios present unique challenges, such as dense vegetation, rugged terrain, uneven surfaces, and water bodies, which demand robust detection and classification capabilities beyond those found in urban areas. This study explores the application of state-of-the-art machine learning models, particularly transformer-based architectures, to enhance feature recognition and classification in rural contexts. We evaluate several advanced models, including hybrid architectures that combine convolutional neural networks (CNNs) with transformers, to determine their effectiveness in identifying complex off-road features. Findings reveal that, although current data limitations restrict the development of fully autonomous systems for off-road navigation, meaningful progress can still be achieved to improve driver assistance functionalities. This paper emphasises the urgent need for broader, more diverse datasets to ensure model robustness and generalizability for autonomous navigation in unstructured, unpredictable environments. Ultimately, this work highlights a promising path toward safer, more effective driver assistance technologies tailored specifically for challenging off-road applications and scenarios. © 2024 ieee.

关键词： Off road vehicles

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Reproduction of Experimental Tests for Tree-Line Conflicts in 10kV Distribution Lines 3

Reproduction of Experimental Tests for Tree-Line Conflicts i...

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3rd international conference on intelligent Power and systems, ICIPS 2023

作者： Guo, Bosen Li, Tianyou Huang, Chaoyi Wang, Lianhui Chen, Shichuan School of Electrical Engineering and Automation Xiamen University of Technology Xiamen China State Grid Fujian Electric Power Company Fuzhou China

ISBN: (纸本)9798350328691

With the continuous construction of the power grid, a large number of power lines pass through forested areas. Electric sparks caused by power line faults may lead to forest fires, while forest fires can also cause power line failures. However, recording long-term waveforms of tree line faults and investigating the causes of forest fires are challenging in practice. To address this issue, we have chosen medium-voltage lines as the research focus and developed a simulation platform to simulate the interaction between power lines and trees. We have also established corresponding Simulink simulation circuits to compare measured data from the simulation platform with idealized conditions, allowing us to study the electrical parameter characteristics when power lines come into contact with trees. Additionally, we conducted experiments on distribution line faults under flame exposure and compared the observable traces on conductors between scenarios of contact with trees and exposure to flames. The findings of this research can serve as a reference for analyzing the combustion traces of steel-cored aluminum stranded wires in fire environments and identifying the causes of fires. ©2023 ieee.

关键词： Simulation platform

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Optimized Power Quality Enhancement in PV-Integrated UPQC systems Using Chaotic PSO-Based MPPT Algorithm

Optimized Power Quality Enhancement in PV-Integrated UPQC Sy...

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2024 international conference on Advancement in Renewable Energy and intelligent systems, AREIS 2024

作者： Pandikumar, M. Jenisha, K.J. Baskar, M. Swathi, Madamanchi Rayaguru, N.K. Baskaran, S. Saveetha School of Engineering Simats Department of Electrical Power & Energy Conversion Chennai602105 India Government College of Technology Department of Electrical Engineering Coimbatore641013 India Chennai Institute of Technology Department of Mechatronics Chennai India Annamacharya University Dept. of Eee Andhra Pradesh Rajampet516126 India VelTech Rangarajan Dr Sangunthala R& D Institute of Science & Technology Dept. of Eee Chennai600062 India Jeppiaar Institute of Technology Dept. of Eee India

ISBN: (纸本)9798350387230

Power Quality (PQ) issues have garnered significant attention due to growing consumer awareness. They remain prominent among the rapid advancements in distributed generation, and the use of sensitive power electronic equipment integrated with Renewable Energy Resources (RES). Various control strategies, including Flexible AC Transmission systems (FACTs) devices, are employed to effectively manage PQ challenges. Among these, Unified Power Quality controllers (UPQC) stand out for their superior performance in mitigating PQ issues compared to traditional methods. This study focuses on Photovoltaic (PV) integrated UPQC for the mitigation of PQ issues and also boosts the voltage from PV by using interleaved Single Ended Primary Inductor Converter (SEPIC) converter. Moreover, Chaotic Particle Swarm Optimization (CPSO) based Maximum Power Point Tracking (MPPT) have been used to enhance performance of interleaved SEPIC converter. The UPQC provides a reduced harmonics with increased power factor. A Proportional Integral (PI) controller based on DQ theory is used to regulate the operation of UPQC. The proposed design of PV Integrated UPQC is evaluated and the simulation results are obtained in Matlab platform with reduced Total Harmonic Distortion (THD) of 3.37% with better efficiency. © 2024 ieee.

关键词： Flexible AC transmission systems

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Intrusion Detection System in IoT Smart city Environment using Tree-based Approach with Swarm-based Optimization for Multi-Step cyber-Attack Dataset 1

Intrusion Detection System in IoT Smart city Environment usi...

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1st international conference on Circuits, Power, and intelligent systems, CCPIS 2023

作者： Reddy, Dukka Karun Kumar Nayak, Janmenjoy Mishra, Manohar Dr. Lankapalli Bullayya College Department of Cse AP Visakhapatnam India University Department of Computer Science Odisha Baripada India Soa University Department of Eee Iter Bhubaneswar India

ISBN: (纸本)9798350315769

With the popularity of IoT technologies, security has become a significant concern. IDS is a crucial component that analyses IoT network activity and categorizes it as either normal or anomalous due to susceptible attacks from hackers. An IDS safeguards a system's availability, integrity, and confidentiality. Because of resource constraints and algorithmic complexity, traditional ML IDS have drawbacks when applied to IoT networks. A lightweight IoT IDS that is optimal, inexpensive, and can minimize the loss function. So, to find the optimal model parameter values of the ML algorithm, hyperparameter tuning is performed by using populationbased swarm algorithms. The swarm-based algorithms control the learning process to extract the ML algorithms' relevant parameters and ultimately learn. A comparative study between the proposed hyperparameter tuning and default parameters predictions is made. The experimental results confirmed that the suggested strategy improves the security of the IoT environment. This approach is evaluated and determined to be promising on the Multi-Step cyber-Attack (MSCA) benchmark IDS dataset. Additionally, the suggested method outperforms standard Tree-based ML parameters in accuracy while achieving a satisfactory trade-off between effectiveness and productivity. © 2023 ieee.

关键词： Internet of things

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Blockchain Ethereum-Based Decentralized Secure Ride-Sharing Framework for Carpooling System 4

Blockchain Ethereum-Based Decentralized Secure Ride-Sharing ...

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4th international conference on Ubiquitous Computing and intelligent Information systems, ICUIS 2024

作者： Wessly, J. Durga, R. Vels Institute of Science Technology and Advanced Studies Department of Computer science Tamil Nadu Chennai India

ISBN: (纸本)9798331529635

Carpooling involves sharing a car for a journey, typically with two or more individuals. Car-sharing systems play a crucial role in addressing urban challenges by providing shared vehicles and decreasing the dependence on private cars. Car-sharing systems present security concerns due to transmitting sensitive information through a shared centralized network, including identity, location, and access codes. It is crucial to develop secure authentication methods to prevent unauthorized access and use of this information for illegitimate purposes. To address the problem, this research presents a Blockchain Ethereum-based Decentralized Secure Ride-sharing Framework (BEDSRF) for carpooling systems. The study introduces a Role-based Access control (RBAC) method to determine user roles for accessing their information. Then, based on these roles, the data is encrypted using the Elliptic Curve Cryptography (ECC) method. The BEDSRF approach verifies each node and transmits the information within the decentralized network. Additionally, the Key Policy Authentication Scheme (KPAS) is utilized to authenticate the user's key and enable role-based decryption for the end user. Furthermore, experiments were carried out to confirm the efficiency and resilience of the proposed approach. The findings indicate that the proposed method outperforms existing carpooling schemes in terms of both security and authentication performance. © 2024 ieee.

关键词： Ethereum

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Applying Data Science to Assess cyber Security of Renewable Energy and Reserve Markets to Ensure System Reliability 9

Applying Data Science to Assess Cyber Security of Renewable ...

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9th international conference on technology and Energy Management, ICTEM 2024

作者： Rizi, Daryoush Tavangar Nazari, Mohammad Hassan Hosseinian, Seyed Hossein Gharehpetian, G.B. Fani, Maryam Khorsandi, Amir Amirkabir University of Technology Department of Physics and Energy Engineering Tehran Iran Smart Control Systems Department Tehran Iran Amirkabir University of Technology Department of Electrical Engineering Tehran Iran

ISBN: (纸本)9798350329797

cybersecurity plays a vital role in tackling threats and minimizing potential harm. The primary objective of the research is to investigate the impact of intervention on the energy market, specifically the unavailability of resources. This network serves as a small-scale representation of energy and reserve markets, encompassing both of the renewable recourse and lines. This is done by executing multiple situations in the network. After an attack in the system, minimizing costs is achieved through a problem solution approach. The CPLEX solver has used for solving MILP model. Furthermore, the gained optimal information is used for prediction and analysis, enabling the utilization of data science techniques, such as charts and machine learning methods, to accurately predict and identify various scenarios. The attack causes a shift in the peak and minimum of the load. Additionally, the examination of three machine learning algorithms has revealed the usage of support vector machine (SVM) in conjunction with neural networks such as RBF, a second-degree polynomial, and third-degree polynomial. According to the results, the highest performance accuracy of 86% is achieved by integrating SVM with a neural network structure. © 2024 ieee.

关键词： Clean energy

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An Ultra Smart Home Implementation Using IoT in Integration with Mobile Robot

An Ultra Smart Home Implementation Using IoT in Integration ...

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2023 international conference on Power Energy, Environment and intelligent control, PEEIC 2023

作者： Das, Sidhant Centre for Interdisciplinary Research for Business and Technology Chitkara University Institute of Engineering and Technology Chitkara University Punjab India

ISBN: (纸本)9798350357769

The potential of smart house transportable RFID-based Internet-of-things devices and services to enhance people's quality of life at home has drawn attention in recent years. RFID technology is getting more and more popular because of its affordability and adaptability. It has been used in many different applications. An overview of the evolution of mobile RFID-based smart home IoT platforms and services, spanning their architecture, essential parts, and diverse uses, is given in this study. This article also discusses the prospects and difficulties for mobile RFID-based smart home IoT devices and applications in the future, including security, privacy, interconnection, and standardization concerns. © 2023 ieee.

关键词： Applications Architecture Challenges In teroperability Internet-of-Things (IoT) Mobile RFID Opportunities Privacy Security Services Smart Home Standardization systems

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Network Intrusion Detection using Wide Tuned Convolutional Neural Network on IoT Network

Network Intrusion Detection using Wide Tuned Convolutional N...

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2023 international conference on Advanced Mechatronics, intelligent Manufacture and Industrial automation, ICAMIMIA 2023

作者： Hafiza, Lia Wahyuningrum, Reni Dyah Maliah Rachmawati, Syifa Salshabila, Sherfina Telkom University School of Electrical Engineering Jakarta Indonesia Institut Teknologi Telkom Purwokerto Telecommunication Engineering Purwokerto Indonesia Department of It Convergence Engineering Kumoh National Institute of Technology Gumi Korea Republic of

ISBN: (纸本)9798350309225

The Internet of Things (IoT) traffic is growing as a result of the enormous number of IoT devices. Intrusion detection systems (IDS) are necessary for detecting network threats. As a solution to the challenges, this research proposes a deep learning-based intrusion detection approach. A Wide Tuned Convolutional Neural Network (WTCNN) is designed to identify an attack in the real-time traffic of an IoT network environment. Data preprocessing and hyperparameter tuning are involved to achieve better performance. Based on result, the proposed method could achieve 0.99 of accuracy, 0.019 of loss, and 0.99 recall, precision, and F1 score. This proposed method gets the best result compared to existing algorithms. © 2023 ieee.

关键词： Network security

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An Improved Clone Node Detection System Based on Enhanced Neural Optimization Strategy Over Wireless Sensor Network Environment

An Improved Clone Node Detection System Based on Enhanced Ne...

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2024 international conference on Innovative Computing, intelligent Communication and Smart Electrical systems, ICSES 2024

作者： Sheela, D. Sagar, M. Vidya Rao, S Kondala Ranjith, S. Shahare, D.Y. Al Sherideh, A. Dept of Ece Chennai India Qis College of Engineering and Technology Department of Mba Pondur Road Vengamukkapalem Ongole Prakasam AndhraPradesh PrakasamDistrict523272 India Qis College of Engineering and Technology Department of S&h Pondur Road Vengamukkapalem Ongole Prakasam AndhraPradesh PrakasamDistrict523272 India Jeppiaar Engineering College Department of Ece Rajiv Gandhi salai Chennai119 India Yeshwantrao Chavan College of Engineering Department of Mechanical Engineering Nagpur India Zarqa University Faculty of Information Technology Department of Cyber Security Zarqa Jordan University of Business and Technology Jeddah21448 Saudi Arabia

ISBN: (纸本)9798331543617

Wireless Sensor Networks (WSNs) are widely used in diverse applications, including environmental monitoring and military surveillance. However, their open and distributed nature makes them vulnerable to clone node attacks, where an adversary replicates legitimate nodes to compromise network integrity. This paper presents an improved clone node detection system based on enhanced neural optimization strategies, utilizing various detection protocols such as Energy-efficient Ring-based Clone Detection (ERCD), Distributed Hash Table (DHT), Randomly Directed Exploration (RDE), Random Multicast (RM), and Randomized, Efficient, and Distributed (RED) methods. The performance of these protocols was evaluated based on detection accuracy, energy consumption, communication overhead, and memory usage across different network sizes. Results showed that the RED method achieved the highest detection accuracy of 99.3% in large networks (1000 nodes), followed closely by ERCD with 96.7%. The DHT and RDE protocols demonstrated lower energy consumption, making them suitable for large-scale, resource-constrained WSNs. The RM method had the lowest detection accuracy (94.3%) and the highest communication overhead. Overall, the proposed neural optimization strategy effectively balances accuracy and resource efficiency, enhancing the robustness of WSNs against clone node attacks. Future work will focus on integrating machine learning models to further optimize detection performance and minimize resource consumption. © 2024 ieee.

关键词： Network security

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