检索结果-内蒙古大学图书馆

6th International conference on internet of Things, Automation and Artificial Intelligence, IoTAAI 2024

作者： Yu, Yanye Li, Huangju Wang, Jianhua Zhou, Min Zuo, Jiawei Huzhou University School of Engineering Huzhou China Huzhou Key Laboratory of Intelligent Sensing and Optimal Control of Industrial Systems Huzhou China

ISBN: (纸本)9798350386974

The problem of resilient robust model predictive control (RMPC) with hard constraints based on an adaptive event-triggered mechanism for a class of polytopic uncertain systems is investigated in this article. To reduce the burden of network communication, a novel adaptive event-triggering mechanism is introduced between the sensor and the controller (S/C) network. The objective of this paper is to design a resilient RMPC controller while considering the system performance degradation caused by the adaptive event-triggered mechanism, aiming to maintain high system performance while conserving communication resources. Moreover, by solving an online optimization problem, sufficient conditions are obtained to ensure that all states of the system are in the time-varying terminal constraint set. To conclusively validate the efficacy of the proposed resilient RMPC algorithm under the adaptive event-triggered mechanism, we adopt a specific distillation parameter example for illustration. © 2024 IEEE.

关键词： Predictive control systems

来源：评论

学校读者我要写书评

暂无评论

Prevention of Security Against Attacks in Order to Balance the Syatem Using Optimisation

Prevention of Security Against Attacks in Order to Balance t...

引用

2023 International conference on Power Energy, Environment and Intelligent control, PEEIC 2023

作者： Kannagi, A. Kumar, Santosh Vishwakarma, Priya Department of Computer Bangalore India Noida Institute of Engineering and Technology Department of Internet of Things Uttar Pradesh Greater Noida India Tula's Institute Department of Computer Science and Engineering Dehradun India

ISBN: (纸本)9798350357769

The need to adaptively manage computer systems and networks so as to offer good quality of Service (QoS) and quality of Experience (QoE) with secure operation at relatively low levels of energy consumption is challenged by their sheer complexity and the wide variability of the workloads. A possible way forward is through self-awareness, whereby self-measurement and self-observation, together with on-line control mechanisms, operate adaptively to attain the required performance and QoE. We survey the premises for these ideas arising from cognitive science and active networks and review recent work on self-aware computer systems and networks, including those that propose the use of softwaredefined networks as a means to implement these concepts. Then we provide some examples from the literature on selfaware systems to illustrate the performance gains that they can provide. Finally, we detail an example system and its working algorithms to allow the reader to understand how such a system may be implemented. Measurements showing how it can react rapidly to changing network conditions regarding QoS and security are presented. Some conclusions and suggestions for further work are listed. © 2023 IEEE.

关键词： active networks cognitive psychology decentralized decision making energy consumption network performance overlay networks and cloud task management systems quality of Experience (QoE) quality of Service (QoS) reinforcement learning Self-aware networks self-measurement self-optimization software-defined networks (SDN)

来源：评论

学校读者我要写书评

暂无评论

A Comprehensive Survey on IPv6 Over Low Power Wireless Personal Area networks (6LoWPAN) 1

A Comprehensive Survey on IPv6 Over Low Power Wireless Perso...

引用

1st International conference on Social and Sustainable Innovations in Technology and Engineering (SASI-ITE)

作者： Veenadhari, C. H. Lakshmi Kandasamy, Selvaradjou Puducherry Technol Univ Dept ofComputer Sci & Engn Pondicherry 605014 India

ISBN: (纸本)9798350360806;9798350360790

The internet of Things (IoT) refers to a network of physical objects embedded with sensors, software, and networking capabilities, enabling them to collect and exchange data over the internet. Within IoT applications, Low Power Wireless Personal Area networks (LP-WPANs) hold significant importance. One of the primary challenges in IoT implementation involves connecting devices that possess low power and limited resources. To overcome this challenge, the IPv6 Over Low Power Wireless Personal Area networks (6LoWPAN) protocol stack was developed. Within 6LoWPAN networks, congestion arising from excessive network traffic leads to a notable decline in network performance and negatively impacts quality of Service (QoS). This paper presents the 6LoWPAN network, specifically designed to facilitate the transmission of IPv6 packets over IEEE 802.15.4 wireless networks. It outlines the components of the 6LoWPAN protocol stack, including the adaptation layer, network layer, and transport layer. Additionally, the paper explores congestion within 6LoWPAN networks and discusses various congestion control algorithms proposed to mitigate this issue. The paper explores the applications of GLoWPAN, focusing on its potential in the IoT ecosystem. It highlights the benefits of 6LoWPAN for IoT applications, including its ability to support large numbers of low-power devices and compatibility with existing IPv6-based systems. Finally, the paper discusses security issues in 6LoWPAN.

关键词： internet of Things Low power and Personal Area networks congestion control Protocol stack

来源：评论

学校读者我要写书评

暂无评论

Improving Transferability of network Intrusion Detection in a Federated Learning Setup 1

Improving Transferability of Network Intrusion Detection in ...

引用

1st IEEE International conference on Machine Learning for Communication and networking (IEEE ICMLCN)

作者： Ghosh, Shreya Jameel, Abu Shafin Mohammad Mahdee El Gamal, Aly Purdue Univ Sch Elect & Comp Engn W Lafayette IN 47907 USA

ISBN: (纸本)9798350343205;9798350343199

network Intrusion Detection systems (IDS) aim to detect the presence of an intruder by analyzing network packets arriving at an internet connected device. Data-driven deep learning systems, popular due to their superior performance compared to traditional IDS, depend on availability of high quality training data for diverse intrusion classes. A way to overcome this limitation is through transferable learning, where training for one intrusion class can lead to detection of unseen intrusion classes after deployment. In this paper, we provide a detailed study on the transferability of intrusion detection. We investigate practical federated learning configurations to enhance the transferability of intrusion detection. We propose two techniques to significantly improve the transferability of a federated intrusion detection system. The code for this work can be found at https://***/ghosh64/transferability.

关键词： network intrusion detection Transferability Federated learning

来源：评论

学校读者我要写书评

暂无评论

SEAL-CC: Scalable Latency Evaluation Methodology for internet-of-Things Services 24

SEAL-CC: Scalable Latency Evaluation Methodology for Interne...

引用

14th International conference on the internet of Things, IoT 2024

作者： Horvath, Kurt Klaus Kimovski, Dragi Spiess, Bernd Hohlfeld, Oliver Prodan, Radu ITEC University of Klagenfurt Klagenfurt Austria Distributed Systems group University of Kassel Kassel Germany

ISBN: (纸本)9798400712852

Traditional network measurement campaigns suffer from the lack of control over network infrastructure and the inability to evaluate communication performance directly, especially for the placement of highly distributed internet of Things (IoT) services. In response, we propose a novel Scalable Latency Evaluation Methodology for the Computing Continuum (SEAL-CC). SEAL-CC extends beyond short-term evaluations by capturing the long-term responsiveness of networks supporting IoT services on the computing continuum. It organizes and evaluates a network of nodes, offering insights for optimized IoT service placement in urban and international settings. Our contributions include a novel evaluation methodology tailored for IoT services over the computing continuum, a comprehensive framework for transparent network evaluation using distributed internet measurement platforms, and a real-life case-study validation with recommendations for IoT service placement. © 2024 Copyright held by the owner/author(s).

关键词： Velocity measurement

来源：评论

学校读者我要写书评

暂无评论

Smart and Adaptive Routing Architecture: An internet-of-Things Traffic Manager Based on Artificial Neural networks

Smart and Adaptive Routing Architecture: An Internet-of-Thin...

引用

1st IEEE International conference on Software Services Engineering (IEEE SSE)

作者： Amiri, Amirali Zdun, Uwe Univ Vienna Software Architecture Grp Doctoral Sch Comp Sci Vienna Austria Univ Vienna Software Architecture Grp Vienna Austria

ISBN: (纸本)9798350340754

Many studies have been performed on integrating the internet of Things (IoT) with cloud services. As these systems become widely used, quality metrics are of concern. For example, users might specify access control to restrict their sensitive data being processed in the cloud. Routers, e.g., API gateways, message brokers, or sidecars, can provide this access control by blocking or routing device data to a specific cloud service. However, a static routing application might not suit the dynamic behavior of IoT applications well. For example, in a centralized schema, where all device data is routed to a component for control checking, performance can be an issue. On the other hand, distributed routing can harm the reliability of a system, as device data might be lost due to an unresponsive service. We present the Smart and Adaptive Routing (SAR) architecture that creates an optimal reconfiguration solution using a deep neural network based on the quality metrics of an IoT application. To design our architecture, we give a background of the published studies and a review of the gray literature, e.g., practitioner blogs, to categorize the knowledge in the domain of IoT-cloud traffic management. We systematically evaluate our approach in an extensive evaluation of 4500 cases and compare SAR with an empirical data set of 1200 hours. The results show that our approach significantly improves quality-of-service measures by adapting the IoT-cloud system at runtime.

关键词： Self-Adaptive systems Dynamic Routing Architectures internet of Things Reliability and performance Trade-Offs IoT-Cloud Traffic Management Artificial Neural networks

来源：评论

学校读者我要写书评

暂无评论

Machine Learning-Assisted Mobile network Speed Prediction: A Real-World Case Application 32

Machine Learning-Assisted Mobile Network Speed Prediction: A...

引用

32nd IEEE Signal Processing and Communications Applications conference (SIU)

作者： Ozkan, Emrecan Yazici, Ibrahim Engin, Semih Istinye Univ Elekt Elekt Muhendisligi Bolumu Istanbul Turkiye Turk Telekom ArGe Operasyonlari Mudurlugu Istanbul Turkiye Gazi Univ Bilgisayar Muhendisligi Bolumu Istanbul Turkiye

ISBN: (纸本)9798350388978;9798350388961

In recent times, as the use of mobile devices has significantly increased, there is a growing expectation for uninterrupted and high-quality wireless mobile network services in all areas. In line with this expectation, ongoing performance measurements and analyses are conducted. However, conducting such studies in an area where no measurements have been taken would result in a loss of both cost, increased workload, and time. To prevent such losses, machine learning was developed to predict the speed test results based on data collected previously with traditional measurement devices. This machine learning model has been designed to anticipate the speed test outcomes in an area where no measurements have yet been taken, relying on the similarity to previously measured parameters. In this study, a variety of data from different sources has been used. Various parameters such as different cities, residential areas, mobile device brands, and various generations of mobile networks were utilized as inputs in the machine learning modeling. The performance metric of the mobile network speed test is used as output. Various models were applied to this dataset. The achieved results were compared, leading to the selection of the algorithm that provided the most suitable results for this study. The results overall suggest that the Random Forest Regression model generally performed better in predicting mobile network performance in these cities, while AdaBoost and XGBoost had varying degrees of accuracy and predictive power in different regions. The study can be used in service quality prediction for internet providers by integrating relevant machine learning models into real systems with application programming interfaces (API).

关键词： Machine learning wireless network performance evaluation mobile network

来源：评论

学校读者我要写书评

暂无评论

Enhancing Energy Efficiency of Underwater Sensor network Routing Aiming to Achieve Reliability 3

Enhancing Energy Efficiency of Underwater Sensor Network Rou...

引用

3rd International conference on Distributed Computing and High performance Computing, DCHPC 2024

作者： Tarif, Mehran Effatparvar, Mehdi Moghadam, Babak Nouri Islamic Azad University Ardabil Branch Department of Computer Engineering Ardabil Iran

ISBN: (纸本)9798350381580

In response to the demands and harsh conditions of underwater environments, developing sensor networks and underwater internet of Things (IoT) has paved the way for wireless communication, ocean exploration, and various research applications. However, challenges such as increasing failure rates and decreasing network efficiency remain open issues, hindering the achievement of quality of service (QoS) in underwater sensor networks. Addressing resource constraints, including processing power, memory, bandwidth, and energy resources, adds to the complexity of supporting network service quality. This study aims to investigate the service quality requirements in protocols and highlight the weaknesses of ongoing research in this field. Finally, a routing solution based on fuzzy logic is proposed, which provides better results than recent methods in tests of energy consumption rate, Jain's fairness index, efficiency, and reliability. © 2024 IEEE.

关键词： quality of service

来源：评论

学校读者我要写书评

暂无评论

IoV-BERT-IDS: Hybrid network Intrusion Detection System in IoV Using Large Language Models

引用

IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 2025年第2期74卷 1909-1921页

作者： Fu, Mengyi Wang, Pan Liu, Minyao Zhang, Ze Zhou, Xiaokang Nanjing Univ Post & Telecommun Sch Modern Posts Nanjing 210003 Peoples R China Shiga Univ Fac Data Sci Hikone 5228522 Japan RIKEN RIKEN Ctr Adv Intelligence Project Tokyo 1030027 Japan Kansai Univ Fac Business Data Sci Osaka 5650823 Japan

The traditional vehicular ad hoc network (VANET) gradually evolved into the internet of Vehicles (IoV), which has also become a potential target for attacks and faces security challenges in an open network environment. Intrusion detection systems (IDS) based on machine learning (ML) and deep learning (DL) are introduced to mitigate security threats. However, existing ML/DL-based IDS suffer from challenges in IoV environments. First, due to the limitations of ML/DL-based methods, classification performance is unsatisfactory when they extract only unidirectional contextual features or spatial characteristics. Second, existing research on in-vehicle network IDS often limits validation and testing to a static dataset of a single vehicle model. This approach may not adequately address diverse potential attacks in a dynamic environment. Third, few studies of hybrid IDS can simultaneously implement in-vehicle and extra-vehicle network intrusion detection. Large language models (LLM) have shown outstanding applications in fields such as natural language processing (NLP) and computer vision (CV). In particular, bidirectional encoder representations from transformers (BERT) obtain new state-of-the-art results on eleven famous NLP tasks. Consequently, this paper introduces a hybrid network IDS in IoV utilising LLM, denoted as IoV-BERT-IDS. This framework encompasses four modules: semantic extractor (SE), input embedding, IoV-BERT-IDS pre-training, and IoV-BERT-IDS fine-tuning. To conform to the BERT model, the semantic extractor is introduced to transform traffic data devoid of apparent semantics into contextual semantics, comprising bidirectional and unidirectional SE. Through SE, controller area network (CAN) data is transformed into a CAN byte sentence (CBS), while extra-vehicle network traffic data is transformed into a traffic byte sentence (TBS). Additionally, two pre-training tasks, the masked byte word model (MBWM) and next byte sentence prediction (NBSP) are proposed

关键词： Semantics Encoding Bidirectional control Task analysis Natural language processing Computational modeling Telecommunication traffic BERT intrusion detection system internet of Vehicle large language model pre-training model

来源：评论

学校读者我要写书评

暂无评论

Exploring NetGPT-Enabled Dynamic Resource Management for Mobile D2D in Industrial IoT

引用

IEEE network 2025年第3期39卷 37-46页

作者： Chen, Baotong Liang, Haiquan Wan, Jiafu Wang, Chuangjian Yan, Xuguo Xia, Xuhui Alqahtani, Salman A. Wuhan Univ Sci & Technol Key Lab Met Equipment & Control Technol Minist Educ Wuhan Peoples R China Wuhan Univ Sci & Technol Hubei Key Lab Mech Transmiss & Mfg Engn Wuhan 430081 Peoples R China South China Univ Technol Sch Mech & Automot Engn Guangzhou Peoples R China King Saud Univ Coll Comp & Informat Sci Comp Engn Dept Riyadh Saudi Arabia

AI large model-centered intelligent applications holds promises to revolutionize network quality of service (QoS). therefore, the deployment and utilization of such models in the Industrial internet of Things (Industrial IoT) are considered as an increasingly pressing priority. In particular, the integration of Generative AI (GenAI) into Industrial IoT, with a focus on establishing a robust NetGPT network architecture that leverages Mobile Edge Computing (MEC) and Device-to-Device (D2D) collaboration has been subjected to significant academic attention. Accordingly, this paper explores the optimization of dynamic network resource management in mobile D2D networks through the innovative application of NetGPT. First, a NetGPT-based system architecture for network resource management is proposed. Thereafter, mobile D2D network model is established with a detailed account of NetGPT training and NetGPT fusion. Finally, the workload scheduling strategy in industrial IoT based on Stackelberg game model is formulated though multi-agent GenAI and present the NetGPT-based orchestration of task co-offloading for mobile network nodes. Deep reinforcement learning is implemented at the edge side to compete and cooperate in addressing complex network problems, which enabling GenAI nodes in a co-offloading and load balancing manner. The implementation of NetGPT-enhanced D2D networks has been conducted in a Mininet simulation environment. Compared to traditional network solutions, our findings show significant improvements in several key performance indicators, including network response latency, task processing capacity, and transmission power.

关键词： Cloud computing Device-to-device communication Industrial internet of Things Training Servers Computational modeling Biological system modeling Collaboration Dynamic scheduling systems architecture Mobile computing Edge computing quality of service Resource management Telecommunication network management Generative AI Multi-access edge computing GenAI Industrial IoT NetGPT D2D Co-offloading

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：