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Energy-Efficient Task Scheduling based on Whale Optimization Algorithm Cyber-Physical Systems 2

Energy-Efficient Task Scheduling based on Whale Optimization...

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2nd IEEE international conference on Integrated Circuits and Communication Systems, ICICACS 2024

作者： Al-Jawahry, Hassan M. Shankar, Smitha Sharath Chythanya, Kanegonda Ravi Maharajan, K. Swathi, Akuthota The Islamic University Najaf Iraq Nitte Meenakshi Institute of Technology Department of Electronics and Communication Engineering Bengaluru India Sr University School of Computer Science and Artificial Intelligence Warangal India Kalasalingam Academy of Research and Education School of Computing Department of Computer Science and Engineering Krishnankoil India Cvr College of Engineering Department of CSE Telangana India

ISBN: (纸本)9798350317558

The extensive application of Cyber-Physical Systems (CPS) requires efficient optimization of computational units and physical plants. Task scheduling (TS) is critical to improving resource utilization and system efficacy within CPS. Standard task schedulers in embedded real-time techniques fall short of achieving CPS performance criteria due to task diversity and system heterogeneity. This research proposes a novel task scheduling technique called Whale Optimization Algorithm based Energy-Efficient TS (WOA-EETS) that is specifically designed for the CPS context. WOA-EETSS involves assigning number of autonomous tasks to various resources during the TS process. The objective function focuses on reducing task completion time and optimizing resource use. A series of simulations were run to demonstrate WOA-EETSS' improved performance. The comparison analysis demonstrated that the WOA-EETSS system performed better across a variety of parameters. The proposed approach attains the better performance in Task Execution (ms) of 347ms, convergence Rate of 511ms and Average Load Balancing Ratio (%) of 69.91% from 100 iterations. © 2024 IEEE.

关键词： Cyber-Physical Systems Energy Efficient Task Diversity Task Scheduling Whale Optimization Algorithm

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Accelerated Flag Qubit Allocation for Fault-Tolerant Quantum Error Correction

Accelerated Flag Qubit Allocation for Fault-Tolerant Quantum...

引用

international conference on Information and Communication technology convergence (ICTC)

作者： Eunyoung Cho Gyuil Cha Cloud Computing Core SW Research Section Future Computing Research Division Artificial Intelligence Research Laboratory Electronics and Telecommunications Research Institute Daejeon Korea

ISBN: (数字)9798350364637

ISBN: (纸本)9798350364644

In quantum computing, achieving error correction comparable to classical systems is crucial for long-term success. This involves measuring and extracting error syndromes to prevent error propagation. However, the constraints of quantum hardware necessitate optimizing these circuits for spatial and temporal efficiency. This paper presents a case study on a flag-based syndrome extraction technique, which aims to maximize parallelism while minimizing resource overhead. To address the scalability of quantum systems and adapt to various hardware constraints, a novel qubit allocation acceleration technique using precalculated unit-cells is proposed. This method enhances both the efficiency and flexibility of quantum compilers, making them more adept at handling quantum error correction challenges.

关键词： Fault tolerance Quantum system Scalability Qubit Circuits Fault tolerant systems Measurement uncertainty Hardware Error correction Resource management

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Lightweight pothole detection method based on improved YOLOv5 3

Lightweight pothole detection method based on improved YOLOv...

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3rd IEEE international conference on Information technology, Big Data and Artificial Intelligence, ICIBA 2023

作者： Zou, Yang Wan, Jiangcheng Wang, Binjie Chongqing University of Posts and Telecommunications School of Computer Science and Technology Chongqing400065 China

ISBN: (纸本)9781665490788

Aiming at the problems of large model weight volume, low detection accuracy and slow detection speed in traditional road Pothole detection methods, which are not easy to be deployed in embedded mobile devices with limited computing resources, a lightweight YOLOv5s road pothole detection model based on attention mechanism was proposed. The YOLOv5s model is taken as the basic model. Firstly, the C3 module in the backbone network is combined with the SimAM attention mechanism, which is improved to the SiMAM-C3 module, which enhances the capability of pothole feature extraction without introducing additional parameters. Then, CARAFE is used to replace the original nearest neighbor interpolation method as the upsampling method to further improve the detection accuracy of the model. Finally, SIoU loss function is used instead of CIoU loss function as bounding box regression loss function to accelerate the convergence of the model. Training and testing on datasets collected from the Internet and self-made data sets. Experimental results show that: With the same data set and the same experimental equipment, the accuracy and average accuracy of the improved YOLOv5s model reached 90.7% and 89.1%, respectively, which were 20.4% and 2.8% higher than those of the YOLOv5s model, and the parameter size was reduced by 1.1MB, showing obvious improvement effect. It can meet the accuracy and real-time requirements of pothole detection in complex environment. © 2023 IEEE.

关键词： Roads and streets

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Asymmetric Weight Pruning for Resource-Limited IoT Devices in Semantic Communications

Asymmetric Weight Pruning for Resource-Limited IoT Devices i...

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international conference on Information and Communication technology convergence (ICTC)

作者： Su-Jin Lee Ye Hoon Lee Department of Electronic Engineering Seoul National University of Science & Technology Seoul South Korea

ISBN: (数字)9798350364637

ISBN: (纸本)9798350364644

In this paper, we explore the application of semantic communication in Internet of Things (IoT) networks, taking into account the limited computing resources of IoT devices. We propose a resource-efficient asymmetric autoencoder framework that leverages weight pruning and quantization techniques to compress the encoder model on IoT devices. Our simulation results demonstrate that the proposed encoder-only pruning model achieves up to 98% model compression without any performance degradation. Furthermore, even with 8-bit quantization applied, the pruned model exhibits no performance loss.

关键词： Performance evaluation Degradation Quantization (signal) Computational modeling Simulation Autoencoders Semantic communication Information and communication technology Internet of Things convergence

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Smart Contract - Security Assessment Integrated Framework (SC-SIF) for Hyperledger Fabric 7

Smart Contract - Security Assessment Integrated Framework (S...

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7th IEEE international conference for convergence in technology, I2CT 2022

作者： Jain, Samyak Shah, Neelkumar K. Kurariya, Pavan Vohra, Neeti Nandukar, Sachin Harne, Niteshkumar Maalik, Mohammad Aziz Bodhankar, Jahnavi Kumar, Ajai Indraveni, Chebolu Centre for Development of Advanced Computing Applied Artificial Intelligence Group Maharashtra Pune India

ISBN: (纸本)9781665421683

In the era of ever-changing technology, we people are adopting new technologies/domains to make a better world. In recent times, Blockchain technology is known for its distributed, permissioned/permission-less and immutability nature. Likewise, the Hyperledger fabric is a permission-based framework that can be used in cross-industry applications for Blockchain technology. The logic behind the cross-industry applications is written in a specific language called a smart contract. Though Hyper ledger fabric is permissioned and is considered immutable, detecting architectural threats on the security and privacy mechanisms of Hyperledger Fabric is a challenge. In this paper, we are proposing the Security Assessment Integrated (SC-SIF) Framework for smart contract assessment in the Hyperledger fabric. This can be used to analyze, scan, and evaluate the smart contracts security vulnerability written in NodeJS/Go/script language. Furthermore, framework provide an integration platform to define and configure multiple security tools including communication protocol, exchange policy and security rules to obtain the security vulnerability on provided smart contracts. It also provides an option to evaluate the tools security assessment report and generate smart contract assessment report in human readable form (JSON/XML) that can help research community, developer, and analyst to improve their smart contract. This paper also briefs literature survey of existing security assessment smart contract and incorporates the working of the ledger, channels, endorsement policy roles, and the transaction flow. © 2022 IEEE.

关键词： Smart contract

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Three-Time Levels Compact Scheme for Pricing European Options Under Regime Switching Jump-Diffusion Models 9th

Three-Time Levels Compact Scheme for Pricing European Optio...

引用

The 9th international conference on Mathematics and computing (ICMC 2023)

作者： Sahu, Pradeep Kumar Patel, Kuldip Singh Behera, Ratikanta Department of Mathematics International Institute of Information Technology Chhattisgarh Naya Raipur493661 India Department of Mathematics Indian Institute of Technology Patna Bihar Bihta801106 India Department of Computational and Data Sciences Indian Institute of Science Karnataka Bangalore560012 India

ISBN: (纸本)9789819930791

This article presents three-time levels compact scheme for solving the partial integro-differential equation (PIDE) arising in European option pricing under jump-diffusion models in the regime switching market. A diagonally dominant system of linear equations is achieved for a fully discrete problem by eliminating the second derivative approximation using the unknown itself and its first derivative approximation. Moreover, the problem’s initial condition is smoothed to assure the fourth-order convergence of the proposed three-time levels implicit compact scheme. Numerical illustrations for solving PIDE are obtained and results are compared with the three-time levels finite difference scheme. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2023.

关键词： Finite difference method

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Context-aware Online Federated Split Learning in 5G Networks 5

Context-aware Online Federated Split Learning in 5G Networks

引用

5th international conference on Computer, Big Data and Artificial Intelligence, ICCBD+AI 2024

作者： Wang, Shuang Bao, Wei Huang, Zhongming Ye, Binbin Tang, Jiashun Xu, Bo State Grid Henan Electric Power Company Zhengzhou Power Supply Company Zhengzhou450015 China Nanjing Nanrui Information and Communication Technology Co. Ltd. Nanjing211100 China Nanjing University of Posts and Telecommunications Nanjing210046 China

ISBN: (纸本)9798331533991

In order to meet the diverse needs of 5G intelligent applications, Federated Learning (FL) based on collaborative training across multiple devices can ensure data privacy while increasing the computational load for local model updates. On the other hand, Split Learning (SL), when properly implemented with model splitting, can adapt to the computing and transmission capabilities of different devices. This paper focuses on a Semi-decentralized Hybrid Federated Split Learning (SD-HFSL) framework that combines the advantages of FL and SL by breaking away from the limitations of a single central server and allowing shared split models to be aggregated among multiple edge servers. To maximize long-term training efficiency, we propose an online optimization problem that includes local model splitting and device association based on evaluated convergence performance. We also introduce a context-aware online training algorithm based on Contextual Multi-armed Bandit (CMAB) framework, where edge servers can observe context information at device sites for latency estimation and perform iterative optimization based on evaluated information in different contexts. Our experiments with multiple neural network models demonstrate significantly superior performance compared to other benchmarks. © 2024 IEEE.

关键词： Federated learning

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Classical music information generating system using AI Video Classification 13

Classical music information generating system using AI Video...

引用

13th international conference on Information and Communication technology convergence, ICTC 2022

作者： Yoo, Joo Hun Jeong, Harim Chung, Tai-Myoung Sungkyunkwan University Department of Artificial Intelligence Suwon Korea Republic of Sungkyunkwan University Department of Interaction Science Suwon Korea Republic of College of Computing and Informatics Sungkyunkwan University Suwon Korea Republic of

ISBN: (数字)9781665499392

ISBN: (纸本)9781665499392

Video classification is a machine learning method designed to analyze video format input data. Our team suggests a new online classical music watching system, providing musical information classified and generated by a depthwise convolution-based neural network model. Classical music is still limited to a few people, and there are difficult to fully enjoy the music even in the concert. Since the lack of musical knowledge is the biggest challenge, we introduce a video classification system that provides additional information to listeners in real-time. For every 50 frames of the video, our deep neural network model generated by training various classical music performance video data serves as a classifier and provides explanations of the instrument currently playing on the screen. Results of test accuracy of 95.34%, average sensitivity of 95.42%, and an average specificity of 99.68% demonstrate that efficient information can be provided to users with a low error rates. Our research team hopes that this new system, which provides additional real-time information on online classical music, will help audiences understand music deeply and even be used for educational purposes. © 2022 IEEE.

关键词： Music

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An SDN Traffic Engineering Approach Based on Traffic Unsupervised Contrastive Representation and Reinforcement Learning 6

An SDN Traffic Engineering Approach Based on Traffic Unsuper...

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6th international conference on Next Generation Data-Driven Networks, NGDN 2024

作者： Yang, Ruiyu Tang, Qi Guo, Yingya College of Computer and Data Science Fuzhou University Fuzhou China Fujian Provincial Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University Fuzhou China

ISBN: (纸本)9798350388374

With the rapid development of Internet technology and the continuous explosive growth of network traffic, Traffic Engineering (TE), as a viable method for optimizing network traffic distribution and improving network performance, attracts widespread attention from both industry and academia. Software Defined Networks (SDN), which decouples the data plane and the control plane, realizes a flexible routing and improves the TE performance. Existing TE approaches in SDN mainly utilize Reinforcement Learning (RL) methods to learn the mapping relationship between network traffic and routing policies. However, due to the continuous expansion of network size and dynamic changes in traffic, the enlargement of traffic state space hinders RL from converging to the optimal routing policy, leading to a decline in network performance. To address these issues, this paper presents a TE method based on unsupervised contrastive representation and RL. This method first shrinks the original traffic state space by efficiently extracting traffic features through Contrastive Learning (CL), aiding quick convergence of RL. It then uses RL to directly learn the mapping from traffic features to traffic splitting policies. Finally, through numerous experiments on real network traffic and topology, it demonstrates that the proposed TE method can effectively achieve load balancing of network traffic under complex and volatile dynamic traffic demands, thereby enhancing network performance. © 2024 IEEE.

关键词： Reinforcement learning

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Federated Deep Q-network: A Dynamic Task Allocation Strategy for UAV-Assisted Cell-Free Networks 4th

Federated Deep Q-network: A Dynamic Task Allocation Strategy...

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4th international conference on Frontiers of Electronics, Information and Computation Technologies, ICFEICT 2024

作者： He, Jian Pan, Chunyu Wang, Jincheng Lu, Cunbo Chen, Shuo The Key Laboratory of Information and Communication Systems Ministry of Information Industry and the Key Laboratory of Modern Measurement and Control Technology Ministry of Education Beijing Information Science and Technology University Beijing100101 China Information Science Academy of China Electronics Technology Group Corporation Beijing China

ISBN: (纸本)9789819653133

A dynamic resource allocation problem for jointly optimizing access point selection and task offloading is proposed for a mobile edge computing system in an unmanned aerial vehicle (UAV)-assisted cell-free (CF) networks for the purpose of delay reduction in an urban scenario. On the one hand, given that various resources are coupled in the optimization problem, exhibiting non-convex traits and thus cannot be directly separated. On the other hand, to expedite algorithm convergence while ensuring user data security, we propose a method that combines the federated learning (FL) framework with deep reinforcement learning (DRL). Specifically, we introduce a federated deep Q network (F-DQN) algorithm to execute the dynamic resource allocation strategy for the proposed problem. Simulation results show that the proposed algorithm has better convergence performance than traditional Q-learning and deep Q network (DQN), and the proposed strategy has lower system delay with a maximum gain of 60.5% compared to other baseline strategies. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Deep reinforcement learning

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