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AQROM:A quality of service aware routing optimization mechanism based on asynchronous advantage actor-critic in software-defined networks

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Digital Communications and networks 2024年第5期10卷 1405-1414页

作者： Wei Zhou Xing Jiang Qingsong Luo Bingli Guo Xiang Sun Fengyuan Sun Lingyu Meng School of Information and Communication Guilin University of Electronic TechnologyGuilin541004China The 34th Research Institute of China Electronics Technology Group Corporation Guilin541004China Guangxi Key Laboratory of Optical Network and Optical Information Security Guilin541004China State Key Laboratory of Information Photonics and Optical Communications BeijingUniversity of Posts and TelecommunicationsBeijing100876China Department of Electrical and Computer Engineering University of New MexicoAlbuquerqueNM87131USA

In Software-Defined networks(SDNs),determining how to efficiently achieve Quality of Service(QoS)-aware routing is challenging but critical for significantly improving the performance of a network,where the metrics of QoS can be defined as,for example,average latency,packet loss ratio,and *** SDN controller can use network statistics and a Deep Reinforcement Learning(DRL)method to resolve this *** this paper,we formulate dynamic routing in an SDN as a Markov decision process and propose a DRL algorithm called the Asynchronous Advantage Actor-Critic QoS-aware Routing Optimization Mechanism(AQROM)to determine routing strategies that balance the traffic loads in the *** can improve the QoS of the network and reduce the training time via dynamic routing strategy updates;that is,the reward function can be dynamically and promptly altered based on the optimization objective regardless of the network topology and traffic *** can be considered as one-step optimization and a black-box routing mechanism in high-dimensional input and output sets for both discrete and continuous states,and actions with respect to the operations in the *** simulations were conducted using OMNeT++and the results demonstrated that AQROM 1)achieved much faster and stable convergence than the Deep Deterministic Policy Gradient(DDPG)and Advantage Actor-Critic(A2C),2)incurred a lower packet loss ratio and latency than Open Shortest Path First(OSPF),DDPG,and A2C,and 3)resulted in higher and more stable throughput than OSPF,DDPG,and A2C.

关键词： Software-defined networks Asynchronous advantage actor-critic QoS-aware routing optimization mechanism

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SMGC-SBERT: A Multi-Feature Fusion Chinese Short Text Similarity Computation Model Based on Optimised SBERT

SMGC-SBERT: A Multi-Feature Fusion Chinese Short Text Simila...

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2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Zhao, Shuo Gu, Qiliang Zhang, Jianqiang Song, Gongpeng Lu, Qin Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Jinan China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China Shandong Branch of China Mobile Communication Group Design Institute Co. Jinan China

ISBN: (纸本)9781665410205

Chinese short text similarity computation stands as a pivotal task within natural language processing, garnering significant attention. However, existing models grapple with limitations in handling intricate semantic relationships, such as the challenge of discerning subtle semantic nuances in text, inadequacies in effectively integrating diverse levels of semantic information, and the struggle to capture polysemous meanings accurately. In addressing these issues, this paper introduces an innovative Chinese short text similarity computation model, SMGC-SBERT. This model addresses shortcomings of the existing models by employing a multi-module fusion strategy, thereby enabling a more precise measurement of semantic similarity between texts. Primarily, the model incorporates SAT embedding to acquire phrase-level semantic information and leverages the MS-BERT model to encode text, improving the model's comprehension of textual polysemy and obtaining richer semantic representation. Then, the fusion of module features, including multi-branch convolutional networks and mix pooling, enables the extraction of textual features from varied levels, bolstering the model's representational capacity. Additionally, to further reduce overfitting risks while improving accuracy and other performance, a multi-layer feature adjustment network is utilized for short text similarity calculation. The final resultant experimental findings showcase the superiority of the SMGC-SBERT model over other neural network models, demonstrating significant advancements across both Chinese-SNLI and CCKS2018_Task3 Chinese short text datasets. © 2024 IEEE.

关键词： Semantics

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FERI: Feature Enhancement and Relational Interaction for Image-text Matching 30

FERI: Feature Enhancement and Relational Interaction for Ima...

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30th IEEE International Conference on Parallel and Distributed Systems, ICPADS 2024

作者： Zhang, Yu Zhang, Jianqiang Song, Gongpeng Lu, Qin Zhao, Shuo Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Jinan China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Jinan China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computer Networks Jinan China Shandong Branch of China Mobile Communication Group Design Institute Co. Jinan China

ISBN: (纸本)9798331515966

Image-text matching is an important problem at the intersection of computer vision and natural language processing. It aims to establish the semantic link between image and text to achieve high-quality semantic alignment between the two modalities. However, the existing methods have the problem that the meaning expressed in the image or the complex narrative in the text cannot be fully understood due to insufficient feature extraction. Moreover, due to the essential modal differences between images and texts, how to effectively and accurately align the semantic contents in images and texts has become the key of research. In order to solve the above problems, this paper proposes a method based on feature enhancement and relationship interaction. When processing images, the proposed method fuses labeled features, region features and location features to represent images. When processing text, a combination of Bi-GRU and self-attention mechanism is used to represent the text. In order to further align the semantic content in images and texts accurately, this paper improves two relational interaction mechanisms by identifying connection relationships and learning association relationships. Thus, the relation enhanced embedding is obtained. Finally, it calculated the similarity of the enhanced embedding to judge the matching degree of the image and text. Extensive experiments on the public datasets Flickr30K and MSCOCO demonstrate the effectiveness of our method. © 2024 IEEE.

关键词： cross-modal retrieval feature enhancement image-text matching relationship interaction

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Fine-grainedly Synthesize Streaming Data Based On Large Language Models With Graph Structure Understanding For Data Sparsity

arXiv

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arXiv 2024年

作者： Zhang, Xin Zhang, Linhai Zhou, Deyu Xu, Guoqiang School of Computer Science and Engineering Key Laboratory of Computer Network and Information Integration Ministry of Education Southeast University China SANY Group Co. Ltd China

Due to the sparsity of user data, sentiment analysis on user reviews in e-commerce platforms often suffers from poor performance, especially when faced with extremely sparse user data or long-tail labels. Recently, the emergence of LLMs has introduced new solutions to such problems by leveraging graph structures to generate supplementary user profiles. However, previous approaches have not fully utilized the graph understanding capabilities of LLMs and have struggled to adapt to complex streaming data environments. In this work, we propose a fine-grained streaming data synthesis framework that categorizes sparse users into three categories: Mid-tail, Long-tail, and Extreme. Specifically, we design LLMs to comprehensively understand three key graph elements in streaming data, including Local-global Graph Understanding, Second-Order Relationship Extraction, and Product Attribute Understanding, which enables the generation of high-quality synthetic data to effectively address sparsity across different categories. Experimental results on three real datasets demonstrate significant performance improvements, with synthesized data contributing to MSE reductions of 45.85%, 3.16%, and 62.21%, respectively. Copyright © 2024, The Authors. All rights reserved.

关键词： Sentiment analysis

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Deep Reinforcement Learning-based Partial Task Offloading in High Altitude Platform-aided Vehicular networks 13

Deep Reinforcement Learning-based Partial Task Offloading in...

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13th International Conference on Information and Communication Technology Convergence, ICTC 2022

作者： Nguyen, Tri-Hai Truong, Thanh Phung Dao, Nhu-Ngoc Na, Woongsoo Park, Heejae Park, Laihyuk Seoul National University of Science and Technology Department of Computer Science and Engineering Seoul Korea Republic of Viettel Network Viettel Group Hanoi Viet Nam Sejong University Department of Computer Science and Engineering Seoul Korea Republic of Kongju National University Division of Computer Science and Engineering Cheonan Korea Republic of

ISBN: (数字)9781665499392

ISBN: (纸本)9781665499392

Compared with traditional terrestrial access networks, a mobile edge computing-enabled aerial access network is a potential paradigm for performing complicated computations by offloading tasks to edge servers. To this end, high altitude platforms employed in the stratosphere to offer extensive coverage and powerful computing capabilities are regarded as a crucial component of an aerial access network. In this work, we investigate a computation offloading issue in a high altitude platform -aided vehicle network, where a ground base station is either overload or not accessible. Non-orthogonal multiple access is employed to enhance the transmission rate. We model the problem as a Markov decision process due to the complexity of the vehicular network and computation-intensive, latency-sensitive tasks. Then, we develop a deep reinforcement learning-based intelligent offloading strategy to minimize all vehicles' total energy consumption and task execution latency. © 2022 IEEE.

关键词： Reinforcement learning

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ARTIFICIAL INTELLIGENCE ASSISTED SERVICE MARKETING USING DEEP ASSISTED NEURAL network

ARTIFICIAL INTELLIGENCE ASSISTED SERVICE MARKETING USING DEE...

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2023 IEEE International Conference on Research Methodologies in Knowledge Management, Artificial Intelligence and Telecommunication engineering, RMKMATE 2023

作者： Shanthana, S. Gopal, Vidhya Navulla, Durgaprasad Seal, Sudeshna Sohail, Mohammed Ali Paul, Gunti Anil Rajalakshmi Institute of Technology Tamilnadu Chennai India Department of Computer Science and Engineering Sona College of Technology Tamilnadu Salem India Lakshmaiah Education Foundation Vaddeswaram Andhra Pradesh Guntur India OmDayal Group of Institutions West Bengal Uluberia India Department of Computer and Network Engineering College of Computer Science and Information Technology Jazan University Jazan Saudi Arabia Department of Mechanical Engineering Karunya Institute of Technology and Sciences Tamilnadu Coimbatore India

ISBN: (纸本)9798350305708

Improving marketing abilities, the quality of one product, and one connections with one target audience are at the service marketing approach. The marketing of goods and services can both be considered to come within the scope of this category. This data is stored in a centralized location in these databases for ease of access and analysis. Based on the findings of this study, AI has played a crucial role in big data analytics, making it possible to foresee and provide guided experiences that are individualized based on the needs of customers. In this paper, we develop a AI assisted customer support system to deliver experiences that result in advocates and consumers that remain loyal over time, the usage of AI and predictive analytics is crucial. © 2023 IEEE.

关键词： artificial intelligence neural network Service marketing validation

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Improved Trajectory-based LiDAR-INS Hand-Eye Calibration Method 6

Improved Trajectory-based LiDAR-INS Hand-Eye Calibration Met...

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6th International Conference on Robotics, Intelligent Control and Artificial Intelligence, RICAI 2024

作者： Wang, Jianzhong Liu, Fucong Cao, Cheng Sun, Haiyue Zhang, Tongzhou Hu, Lina College of Information Science and Technology Northeast Normal University Changchun China China Unicom Group Mobile Network Co. LTD. Jilin Branch Changchun China College of Computer Science and Technology Jilin University Changchun China School of Information Engineering Changchun College of Electronic Technology Changchun China

ISBN: (纸本)9798331541699

Calibration between different sensors is crucial for improving mapping accuracy. Some sensors, such as inertial navigation systems (INS), cannot extract features and rely on motion-based hand-eye calibration to determine their transformation with other sensors. However, existing methods neglect cumulative errors in light detection and ranging (LiDAR) or camera-based mapping and inertial measurement unit (IMU) drift in rotation, degrading calibration accuracy. In this paper, an improved trajectory-based LiDAR-INS hand-eye calibration method is proposed. Firstly, a novel residual that accounts for IMU rotational error is designed, distributing the error across each trajectory vertex. Then, a weight matrix related to the distances between vertices in the trajectory graph is introduced, assigning higher weights to vertices with loop closure relationships. Finally, the proposed calibration method is validated on our self-collected data sequences. Experiments show that the proposed method outperforms the baseline, with an average loss of 0.0112 per vertex across four sequences. In addition, the method converges in an average of 22 iterations across these sequences, enabling real-time computation. © 2024 IEEE.

关键词： Trajectories

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A Blockchain Method to IoT Sensors for Data Security in Aviation Surveillance 11

A Blockchain Method to IoT Sensors for Data Security in Avia...

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2023 IEEE 11th International Conference on Information, Communication and networks, ICICN 2023

作者： Zhao, Erfan Bai, Jian Shen, Yi Jia, Yu Zhang, Xiaosong Kang, Rongbao University of Electronic Science and Technology of China School of Computer Science and Engineering Chengdu China The 30th Research Institute of China Electronics Science and Technology Group Corporation Chengdu China ShenZhen CyberArray Network Technology Co. Ltd Shenzhen China

ISBN: (纸本)9798350314014

Open source IoT sensors, where distributed, could submit aviation surveillance data from around the world to third-party platforms in a centralized manner. This has become the mainstream method for aviation data fusion and collaborative prediction. However, there are many network security problems in the current aviation surveillance system, particularly the issue of fake data submitted by 'unreliable' IoT sensors. This paper proposes a consensus mechanism based on the consortium chain in which ICAO member states can join to share aviation surveillance data. Additionally, using a blockchain smart contract-based solution can solve the network security problem of air surveillance IoT volunteer sensors around the world. Through the analysis and testing of nearly 100,000 pieces of aviation surveillance data, the results demonstrate that the proposed scheme has the characteristics of easy error correction for noise, updateable strategy, and high storage efficiency, and has practicality in the field of air traffic control. © 2023 IEEE.

关键词： Blockchain

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Adaptive Random Access Satellite Communication in the Presence of Interference

Adaptive Random Access Satellite Communication in the Presen...

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MILCOM, Military Communications Conference

作者： Jonathon H. Copley Vincent W.S. Chan Department of Electrical Engineering and Computer Science Research Laboratory of Electronics Claude E. Shannon Communication and Network Group Massachusetts Institute of Technology Cambridge USA

ISBN: (数字)9798350374230

ISBN: (纸本)9798350374247

This paper explores a random access service for satellites capable of adapting to unintentional or intentional interference. Previous work does not address large bandwidth-delay product systems. This paper combines the challenges of each, developing a construct for stabilizing the protocol, accommodating long delays, mitigating interference and maximizing throughput.

关键词： Military communication Satellites Protocols Interference Throughput Delays Satellite communications

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An Exemplification of Decisions of Machine Learning Classifiers to Predict Handover in a 5G/4G/3G Cellular Communications network

An Exemplification of Decisions of Machine Learning Classifi...

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International Workshop on Design of Reliable Communication networks (DRCN)

作者： Ashraf Tahat Farah Wahhab Talal A. Edwan Department of Communications Engineering Princess Sumaya University for Technology Amman Jordan Mobile Access Network Engineering Orange Jordan Telecom Group Amman Jordan Department of Computer Engineering University of Jordan Amman Jordan

ISBN: (数字)9798350348972

ISBN: (纸本)9798350348989

Mobile applications and high-end Internet-of-Things (loT) devices are progressively becoming reliant on high-data rates and high endurance content delivery, while the provided data rates on cellular communications connectivity links are intrinsically time-varying. Handover (HO) is a primary element of cellular communication networks that requires to be appropriately managed considering that HOs among base transceiver stations and between access modes as a result of user mobility pose difficulties in delivering a desired rich user Quality-of-Experience (QoE). That is because there are multiple impediments to quality-of-service (QoS) such as reduced data rate and interruptions of service. The ability to make accurate decisions in predicting the upcoming HOs, and consequently the anticipated supported data rate, will give applications valuable latitude to make perceptive adaptations to evade substantial degradation in QoE/QoS. In this paper, we provide a proof-of-concept, and investigation of the decision accuracy of Machine Learning (ML) based prediction of mobile HOs in real-time in co-existing 3G/UMTS, 4 GIL TE, and 5G (NSA) networks. We render the results of our measurement campaigns, and relevant feature values capturing the influences of the environment in the form of GPS location coordinates, received signal strength indicator (RSSI), and associated connectivity modes. To that end, we conduct a countertype analysis to compare the performance of a distinct collection of ML classification algorithms to assess their decision accuracy in predicting HOs. Evaluation metrics revealed an optimum ML algorithm for HO prediction tasks, which should be useful to employ in the context of heterogeneous cellular communications networks.

关键词： Cellular networks Machine learning algorithms Quality of service Machine learning Handover Prediction algorithms Classification algorithms

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