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Hierarchical Name-based Routing for Content Provider Mobility in ICN 19

Hierarchical Name-based Routing for Content Provider Mobilit...

引用

19th ieee international Wireless Communications and Mobile computing (ieee IWCMC)

作者： Sun, Yanbin Zhou, Jianxun Hu, Bo Zhou, Xiaoming Song, Wei Li, Mohan Tian, Zhihong Guangzhou Univ Cyberspace Inst Adv Technol Guangzhou Peoples R China State Grid Liaoning Elect Power Supply Co Ltd Shenyang Liaoning Peoples R China

ISBN: (纸本)9798350333398

ICN treats contents as the first citizens and faces serious scalability challenges. especially for the content provider mobility scenario, the routing updates and routing efficiency should be cost-effective. This paper proposes a hierarchical name-based routing (HNR) for provider mobility. HNR focuses on how to find the mobile provider when given an immutable provider name. Based on the idea that the content provider generally moves within a certain topology area over a period of time, HNR first divides the topology into multiple partition topologies, then it adopts a hierarchical routing scheme that combines two suitable routing schemes for local mobility and global mobility. The experiments by simulation demonstrate that HNR achieves a good tradeoff between efficiency and scalability.

关键词： ICN Mobility Name-based Routing Hierarchical

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Research on Knowledge Fusion Optimization System Based on Computer Large-Scale Heterogeneous Network Big Data

Research on Knowledge Fusion Optimization System Based on Co...

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2023 ieee international conference on Sensors, electronics and Computer engineering, ICSeCe 2023

作者： Xiaogan, Chen Peng, Shu Xiaohan, Lai Jiabin, Huang Qing, Nie State Grid Zhejiang Electric Power Co. Ltd. Hangzhou China Yingda Media & Investment Group Co. Ltd. Beijing China

ISBN: (纸本)9798350313734

Network big data refers to the big data generated by the interaction and fusion of "people, machines and things"in the network space and available on the Internet. In this paper, from the perspective of ontology-based knowledge representation, network knowledge is represented in the form of five-tuples such as instance, attribute, domain set, relation and concept. Then, from the perspective of complex system, the new research perspective of deep learning technology and the new technology trends of grain computing in information fusion are analyzed, and the feasibility of the integration and expansion of grain computing and deep learning is speculated. A new overlapping community detection algorithm based on modularity optimization is designed. It is found that the framework can solve the dynamic update of network big data and the integration and reorganization of existing professional knowledge base to a certain extent. It can effectively guarantee the construction of domain knowledge base and improve its coverage and timeliness. © 2023 ieee.

关键词： Heterogeneous networks

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A Petri-Net Model for Propagation of Corrupted Data in a Networked System 58

A Petri-Net Model for Propagation of Corrupted Data in a Net...

引用

58th Hawaii international conference on System sciences, HICSS 2025

作者： Woodard, Mark Sarvestani, Sahra Sedigh Hurson, Ali R. Sandia National Laboratories United States Missouri University of Science and Technology United States

ISBN: (纸本)9780998133188

This paper introduces a Petri net model for the propagation of corrupted data in networked systems, including cyber-physical systems. The model abstracts data storage, processing, and communication of a system to estimate the rate and extent of propagation of corrupted data. This facilitates simulation and analysis of the effects of data corruption without requiring a domain-specific simulator. We illustrate the approach by applying it to an ieee 57-bus smart grid system and analyzing the rate and extent of data corruption - measures of system survivability. The proposed model, with its potential to enable decision support crucial to the sustainability of critical infrastructures, also plays a vital role in shedding light on the propagation of corrupted data and the potential failures that result, providing valuable insights for system design and sustainable operation. © 2025 ieee Computer Society. All rights reserved.

关键词： grid computing

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A Multi-Objective Multi-Agent RL-Based Approach for Task Offloading in IoT-enabled Classrooms 25

A Multi-Objective Multi-Agent RL-Based Approach for Task Off...

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25th ieee international conferences on High Performance computing and Communications, 9th international conference on Data science and Systems, 21st ieee international conference on Smart City and 9th ieee international conference on Dependability in Sensor, Cloud and Big Data Systems and Applications, HPCC/DSS/SmartCity/DependSys 2023

作者： Zhang, Juli Huang, Shuo Niu, Zhenxing Lin, Yishuai Wang, Quan Xidian University Academy of Advanced Interdisciplinary Research Xian China College of Computer Science and Technology Xidian University Xian China

ISBN: (纸本)9798350330014

With the widespread deployment of smart sensors and edge computing, Internet of Things (IoT) has become an im-portant enabler to facilitate many conventional systems, such as e-learning, e-health, and assisted driving. Among these systems, one of the most impactful, IoT-enabled practices is e-learning systems, which aim to capture real-time behaviors of students via a multitude of sensors in accordance with course materials, and to further provide adaptive teaching effectiveness. However, e-learning system, specifically an IoT-enabled classroom, faces three important challenges: (1) how to capture the real-time sensing information;(2) how to collect and differentiate jobs with delay constraints, such as face recognition, multi-modal attention detection, etc;and (3) how to allocate computing resources of edge nodes to satisfy the delay requirements of jobs that consist of multiple tasks. To tackle the first two challenges, we design and implement a novel IoT-enabled, e-learning system, named Yo-Yo, with edge computing to collect real-time jobs and tasks for further data analysis. In Yo-Yo system, both job completion rate and energy consumption are considered as quality-of-service (QoS) metrics. Therefore, for the third challenge, we propose a new multi-objective, multi-agent reinforcement learning method to jointly optimize these two objectives. experiments results indicate that for each device, around 4,200 jobs are generated, with varying tasks associated with each job. The average job completion rate can achieve up to 94% over 10 devices;for dealing with complex task dependency, our approach can improve by 24 %, while reducing the ineffective energy consumption by 70 % against to single-agent based methods. © 2023 ieee.

关键词： edge computing

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energy-saving Processors Two-phases Frequency Reduction Algorithm on Heterogeneous embedded Systems 10

Energy-saving Processors Two-phases Frequency Reduction Algo...

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10th ieee international conference on Cyber Security and Cloud computing and 9th ieee international conference on edge computing and Scalable Cloud, CSCloud-edgeCom 2023

作者： Huang, Weihong Jiang, Kuan Huang, Jing Lisi, Li Xiao, Yufeng Deng, Zihao Hunan University of Science and Technology Hunan Key Laboratory for Service Computing and Novel Software Technology School of Computer Science and Engineering Xiangtan411201 China The Bilingual Experimental School HNSDFZ Changsha410006 China

ISBN: (纸本)9798350312461

energy saving has become a key issue for heterogeneous embedded systems. Previous energy-saving methods attempt to minimize the energy consumption of applications in heterogeneous embedded systems subject to deadline constraints by reducing the processor frequency. However, good scheduling strategy can also minimize energy consumption to some extent. This paper proposes a novel energy-saving scheduling algorithm, called the energy-saving Processors Two-phases Frequency Reduction (ePTFR) Algorithm. In the first stage, within the deadline constraints, the maximum working frequency of each processor is reasonably and synchronously reduced;in the second phase, when running sub-applications on the processor, under the constraints of the earliest start time and latest end time of sub-applications, the actual operating frequency of the processor is reasonably reduced. Finally, the effectiveness of the ePTFR algorithm is verified through numerical experiments, and the results show that the proposed ePTFR algorithm can achieve a significant energy-saving effect. © 2023 ieee.

关键词： energy conservation

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Multi-Objective Optimization on Stealthy False Data Injection Attack in Smart Power Transmission grid

Multi-Objective Optimization on Stealthy False Data Injectio...

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2023 international conference on Networks, Communications and Intelligent computing, NCIC 2023

作者： Ye, Tong Zhou, Boyang Nie, Yinghui Zhu, Zhen Zhejiang Lab China Zhejiang Lab UCAS HIAS China

ISBN: (纸本)9798350371949

In smart power transmission grids, the existing energy management systems (eMSes) unprecedentedly faces the stealthy false data injection attack (SFDI), where an adversary aims to compromise the optimal power flow analysis (OPF) to generate incorrect power dispatches and affect the power grid security and economy, via injecting subtle differences to the power consumption data measured by the wide-area remote terminal units (RTUs) in the supervisory control and data acquisition system (SCADA). To study the above OPF-targeted SFDI (OT-SFDI), we investigate a novel mechanism OT-SAG based on a bilevel genetic algorithm to efficiently optimize the attack vectors with multiple objectives, and accurately simulate the impact on the power grid. We evaluate OT-SAG under the OT-SFDI scenario in real power transmission grid constructed based on the ieee 30-bus datasets. The numerical results show that OT-SAG can efficiently optimize a diversity of OT-SFDI attack vectors, and accurately evaluate their effectiveness, concerning the adversaries' different objectives in the attack cost, stealthiness and attack impact. © 2023 ieee.

关键词： Multiobjective optimization

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enhancing grid Reliability in the Baltic Region: Integrating Renewable energy and eNTSO-e Synchronization 13

Enhancing Grid Reliability in the Baltic Region: Integrating...

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13th ieee international conference and expositions on electric and Power engineering, ePei 2024

作者： Sahnovskis, Aleksandrs Zicmane, Inga Kovalenko, Sergejs Riga Technical University Faculty of Computer Science Information Technology and Energy Riga Latvia

ISBN: (纸本)9798350356199

The integration of renewable energy sources within the Baltic energy system, which includes Latvia, Lithuania, and estonia, presents both challenges and opportunities. As the Baltic countries strive to increase the share of renewable energy to 42.5% by 2030, driven by the eU's 'Fit for 55' package, they must address the inherent variability of renewable sources like wind and solar, which can complicate grid *** to synchronize the Baltic energy system with the eNTSO-e (european Network of Transmission System Operators for electricity) further emphasize the need for a resilient grid. A simplified model of the Baltic energy system is used to explore four scenarios-Optimistic, Realistic+, Realistic-, and Pessimistic-focusing on electrical connections in base load, export, and import modes. The analysis highlights the critical role of BeSS systems (Battery energy Storage Systems) in stabilizing the grid and managing power deficits. Despite ongoing development of interregional transmission lines and BeSS installations, enhancing energy storage and diversifying energy sources remain crucial for ensuring a stable and sustainable energy *** investments in alternative energy sources, such as small hydropower or nuclear energy, alongside advanced storage solutions, are essential to support the region's ambitious renewable energy goals. By boosting energy system resilience and reducing dependence on variable renewable sources, the Baltic region can secure a reliable and environmentally friendly energy supply, significantly contributing to global efforts to combat climate change. © 2024 ieee.

关键词： Clean energy

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Cross-System Data Integration Based on Rule-Based NLP and Node2Vec 8

Cross-System Data Integration Based on Rule-Based NLP and No...

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8th international conference on Data science in Cyberspace, DSC 2023

作者： Xiao, Jiakai Dut, Wei Xu, Zhengxiang Qian, Yang State Grid Anhui Electric Power Company Department of Internet China School of Management Hefei University of Technology China

ISBN: (纸本)9798350331035

In the era of Big data, a growing number of enterprises are paying attention to data governance and data services. Large enterprises (e.g., state grid corporations), have numerous information systems, which have caused issues such as inconsistent data standards, and different data storage methods. This makes it very difficult to share data information among different enterprises and departments. In this situation, enterprises have begin to use various methods to integrate data across different systems. In this study, we select the power industry and design a cross-system data fusion framework. Specifically, there are many types of power grid equipment, and the topology data, equipment data, and operation data associated with them are stored in different ways. For cross-system data, we first use equipment name information and design a ruled-based Natural Language Processing (NLP) technique to match the same data between different systems. However, the equipment names in different systems do not follow a strict and unified naming standard, which leads to a set of equipment nodes that cannot be accurately matched. To solve this issue, we use network structure information about equipment and design a node representation technique based on Node2vec. Based on the node representation, we further optimize the results of equipment matching. For evaluation, we use a cross-system dataset from a state grid corporation in China. Through experiments, we show that the proposed framework performs very well in cross-system data integration. © 2023 ieee.

关键词： Artificial intelligence computing methodologies computing methodologies Data Integration electricity Industry Graph embedding Information extraction Learning latent representations Machine learning Machine learning approaches Naturallanguage processing NLP

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Research on Intelligent Scheduling Method of Multi Cloud Collaborative computing Network Fusion Resources 4

Research on Intelligent Scheduling Method of Multi Cloud Col...

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4th international conference on Mechatronics Technology and Intelligent Manufacturing, ICMTIM 2023

作者： Yang, Mingjie Zhang, Xiaodong Guo, Rui Zhao, Shuhan Wang, Wei State Grid Gansu Electric Power Gansu Lanzhou730050 China

ISBN: (纸本)9798350314434

Conventional scheduling methods mainly focus on shortening the scheduling time, and the network resources cannot be balanced, resulting in poor scheduling results. Therefore, an intelligent scheduling method for multi cloud collaborative computing network fusion resources is designed. Construct a global Nash equilibrium of computing network integration resources, balance the resources after computing network integration, and avoid the problem of local optimal scheduling. Based on multi cloud collaboration, the resource scheduling model of computing network cloud computing is constructed to find the optimal scheduling value of resource data after computing network integration. Verify the resource scheduling consistency of the multi cloud platform, the hash function of the fixed scheduling model, and analyze the integrity of the resource scheduling data. By means of simulation experiments, it is verified that the intelligent scheduling effect of this scheduling method is more stable and can be applied to real life. © 2023 ieee.

关键词： Hash functions

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A review for soil nutrient identification using image processing 1

A review for soil nutrient identification using image proces...

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1st ieee international conference on Advances in computing, Communication and Networking, ICAC2N 2024

作者： Sisodia, Ms. Shivani Dhyani, Saurabh Uttaranchal University Uttaranchal School of Computing Sciences Uttarakhand Dehradun India

ISBN: (纸本)9798350356816

In agriculture, environmental science, and land resource management, soil analysis is crucial. The ability to extract detailed data from soil images has recently become possible thanks to the integration of image processing methods with soil analysis. We emphasize on the precision and effectiveness of soil analysis using machine learning. To tackle the issues of sustainable agriculture, soil conservation, and land-use planning, the combination of soil science and image processing provides promising solutions. This work offers a comprehensive examination of the integration of image processing into soil analysis. We look at the entire soil analysis process, and we have also subcategorized the work depending on how images of soil were collected for processing, i.e., remotely, using satellites, or using a digital camera. This study is a useful tool for academicians, engineers, and decision-makers who want to use image processing to its full potential for soil nutrient identification. © 2024 ieee.

关键词： image processing machine learning nutrients soil

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