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IEEE International Conference on Communications (ICC)

作者： Haibin Song Tian Pan Guohao Ruan Yan Zheng Ying Wan Jiao Zhang Tao Huang Yunjie Liu Beijing University of Posts and Telecommunications Beijing China Purple Mountain Laboratories Nanjing China Key Laboratory of Computer Network and Information Integration Ministry of Education Southeast University China China Mobile (Suzhou) Software Technology Co. Ltd Suzhou China

ISBN: (数字)9781728190549

ISBN: (纸本)9781728190556

Due to the high costs of low Earth orbit (LEO) satellite manufacturing and launch, as well as the complexity of in-orbit network protocol debugging, simulating and verifying satellite network protocols on the ground before satellite launch holds significant importance. Compared to the expensive emulation with one-to-one replication, simulation (e.g., using ns-3) can achieve discrete event processing at a relatively lower cost by extending the wall clock time. However, very few studies have used ns-3 for LEO satellite network simulation, facing challenges such as faithfully simulating the on/off state switching of inter-satellite links (ISLs) and achieving simulation performance scalability for high-density satellite constellations. In this work, we propose a system to accelerate mega-scale LEO satellite network simulation in ns-3 via MPI-based parallelization. Specifically, we simulate ISLs based on ns-3's P2P channels/P2P remote channels and achieve runtime link connection/disconnection by implementing stateful traffic dropping inside the network interface. Then, we conduct concurrent simulation with ns-3's parallel and distributed simulation capability and partition the satellite constellation into multiple simulation processes through a hierarchical clustering algorithm and automated scripts, considering satellite locality and inter-process workload balance. Our evaluation shows significant speed improvements via parallelization, e.g., a 373% speedup with 12 processes for LEO-192, and a 156% speedup with 3 processes for LEO-3072.

关键词： Satellite constellations Satellites Runtime Protocols Costs Low earth orbit satellites Clustering algorithms

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Adaptive VDI Session Placement Via User Logoff Prediction

SSRN

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SSRN 2022年

作者： Fan, Wenping Zhang, Min-Ling Meng, Puhui Tian, Yu Zhang, Yao School of Computer Science and Engineering Southeast University Nanjing210096 China Ltd Beijing100190 China Key Laboratory of Computer Network and Information Integration Southeast University Ministry of Education China

The global pandemic has fundamentally changed our ways of work and life where remote working is becoming the new normal of many people. In this circumstance, DaaS(Desktop as a Service) has become the first choice of companies’ remote working solution. Compared with the on-premise VDI(Virtual Desktop Infrastructure) solution, DaaS customers are more cost-sensitive which boosts the requirement of resource optimization. Power management plays a key role here as it can drive saving running cost by maintaining minimum powered-on virtual machines without impacting user experience. Prior researches in this area focus on VDI session logon behavior modeling which can help DaaS system to power on only necessary virtual machines. But for the RDSH(Remote Desktop Service Host)-shared desktop pools, powering off RDSH servers is still a challenge. In current system, sessions are placed statically by roundrobin or in a pre-defined order without considering the effect of their logoff time. As a RDSH server can’t be powered off even there is only one session left, the existing approaches usually suffer from the situation that very few sessions wide spread on different servers. In this paper, we propose SODA, an innovative compound model towards proactive RDSH session placement. Specifically, an ensemble machine learning model that can predict session logoff time is combined with a statistical session placement bucket model to place RDSH sessions with similar logoff time in a more centralized manner on RDSH hosts. Consequently, the infrastructure cost-saving can be improved by reducing the resource waste introduced by those RDSH hosts with very few hanging sessions left for a long time. Experiments on real RDSH pool data demonstrate the effectiveness of the proposed proactive session placement approach against existing static placement techniques. © 2022, The Authors. All rights reserved.

关键词： Forecasting

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EXPLAIN, EDIT, GENERATE: Rationale-Sensitive Counterfactual Data Augmentation for Multi-hop Fact Verification

arXiv

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arXiv 2023年

作者： Zhu, Yingjie Si, Jiasheng Zhao, Yibo Zhu, Haiyang Zhou, Deyu He, Yulan School of Computer Science and Engineering Key Laboratory of Computer Network and Information Integration Ministry of Education Southeast University China Department of Informatics King’s College London United Kingdom The Alan Turing Institute United Kingdom

Automatic multi-hop fact verification task has gained significant attention in recent years. Despite impressive results, these well-designed models perform poorly on out-of-domain data. One possible solution is to augment the training data with counterfactuals, which are generated by minimally altering the causal features of the original data. However, current counterfactual data augmentation techniques fail to handle multi-hop fact verification due to their incapability to preserve the complex logical relationships within multiple correlated texts. In this paper, we overcome this limitation by developing a rationale-sensitive method to generate linguistically diverse and label-flipping counterfactuals while preserving logical relationships. In specific, the diverse and fluent counterfactuals are generated via an Explain-Edit-Generate architecture. Moreover, the checking and filtering modules are proposed to regularize the counterfactual data with logical relations and flipped labels. Experimental results show that the proposed approach outperforms the SOTA baselines and can generate linguistically diverse counterfactual data without disrupting their logical relationships1 Copyright © 2023, The Authors. All rights reserved.

关键词： Machine learning

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TPMARL-MSS Thought-Perception Multi-Agent Reinforcement Learning Meeting Scheduling System

TPMARL-MSS Thought-Perception Multi-Agent Reinforcement Lear...

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IEEE International Conference on High Performance Computing and Communications (HPCC)

作者： Jinsong Zhang Xiaoxuan Mao Lu Li Hanqian Wu Joint graduate school Southeast University Nanjing China College of Software Engineering Southeast University Nanjing China School of Cyber Science and Engineering Southeast University Nanjing China Key Laboratory of Computer Network and Information Integration of Ministry of Education School of Cyber Science and Engineering Southeast University Nanjing China

In today's evolving society, the increasing complexity and frequency of meetings necessitate advanced scheduling systems. Traditional methods are constrained by rigid prede-fined strategies, lack intelligent negotiation mechanisms, and often compromise user privacy. Addressing these challenges, we introduce the Thought-Perception Multi-Agent Reinforcement Learning Meeting Scheduling System (TPMARL-MSS). Unlike conventional systems, TPMARL-MSS autonomously learns and refines its strategies through continuous feedback. It features automated negotiation and adaptive decision-making, offering a more nuanced scheduling approach. Importantly, our Thought-Perception module protect privacy, allowing the agent to deduce preferences from participant behavior without revealing personal data. Evaluations on the real-world dataset shows that TPMARL-MSS surpasses traditional methods in efficiency and schedule quality, highlighting its practical applicability.

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Private Protocol Traffic Identification Based on Sequence Statistical Fingerprint

Private Protocol Traffic Identification Based on Sequence St...

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GLOBECOM 2022 - 2022 IEEE Global Communications Conference

作者： Junchen Li Guang Cheng Zekun Jing Haiyang Wei School of Cyber Science and Engineering Southeast University Key Laboratory of Computer Network and Information Integration(Southeast University) Ministry of Education Jiangsu Province Engineering Research Center of Security for Ubiquitous Network (Southeast University) Nanjing China

ISBN: (纸本)9781665435413

With more attention paid to communication security and user privacy, there has been a dramatic rise in private protocols, which brings great challenges to traditional protocol identification. The increasingly complex design of private pro-tocols has led to the loss of effectiveness of features used for traditional identification. Besides, some features are redundant and also lose sight of the particularity of private protocols, which could not be guaranteed to be the optimal identification solution. In this paper, we propose a method to obtain the Sequence Sta-tistical Fingerprint(Seq-SF) which is proprietary for each private protocol and can be used for identifying private protocols from promiscuous traffic. We extend statistical features associated with protocol transmission to make the sequence feature be integrated into it. Then, we adopt a feature selection algorithm based on mutual information ranking, which can evaluate the pertinence between features and protocols to guarantee the effectiveness and optimality of Seq-SF. The results of experiments on the real-world dataset covering 13 private protocols indicate that Seq-SF achieves an ideal performance(99.41% accuracy, 97.56% precision, 95.18% recall, and 0.20% FPR on average) and has good robustness.

关键词： Privacy Protocols Heuristic algorithms Telecommunication traffic Fingerprint recognition Robustness Classification algorithms

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The Distributed Intelligent Collaboration to UAV-Assisted VEC: Joint Position Optimization and Task Scheduling

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IEEE Internet of Things Journal 2025年

作者： Yi, Meng Lee, Vincent C.S. Yang, Peng Li, Peisong Zhang, Yifan Wei, Wei Gao, Honghao Southeast University School of Computer Science and Engineering Nanjing211189 China Monash University Faculty of IT Department of DS and AI ClaytonVIC3800 Australia Southeast University School of Computer Science and Engineering the Key Laboratory of Computer Network and Information Integration Ministry of Education Nanjing211189 China Xidian University Hangzhou Institute of Technology Hangzhou311200 China Southeast University-Monash University Joint Graduate School Suzhou215123 China Xi'an University of Technology School of Computer Science and Engineering Xi'an710048 China Shanghai University School of Computer Engineering and Science Shanghai200444 China

Deploying Unmanned Aerial Vehicles (UAVs) as aerial base stations enhances the coverage and performance of communication networks in Vehicular Edge Computing (VEC) scenarios. However, due to the limited communication range and energy capacity of UAVs, they cannot continuously cover entire areas or sustain long flights. Therefore, achieving full communication coverage of a target area with a minimal number of UAVs and efficient task offloading remains a significant challenge. To address this problem, the UAV-assisted Two-stage Intelligent Collaboration (UTIC) method is proposed in this paper to tackle the joint position optimization and task scheduling issue. Firstly, a UAV-assisted Two-stage Task Scheduling (UTTS) system model is designed to optimize the allocation process. Secondly, an Enhanced Particle Swarm Optimization (E-PSO) algorithm is designed to determine the optimal positions of UAVs, ensuring complete coverage of all mobile vehicles (MVs) with the minimum number of UAVs. Thirdly, Deep Deterministic Policy Gradient (DDPG) method is employed to find the optimal scheduling decisions for MVs, considering energy consumption, delay, and task priorities. Simulation results demonstrate that the proposed UTIC method can achieve nearly 20% reduction in UAV deployment and outperform three other classical reinforcement learning (RL) algorithms in terms of reducing system cost. © 2025 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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Research on Lightweight Sensing Technology Based on Single-Antenna Multicarrier

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IEEE Internet of Things Journal 2025年

作者： Jiang, Yu Zhu, Di Wang, Jiadong Hu, Aiqun Southeast University School of Cyber Scienc and Engineering Purple Mountain Laboratories Nanjing210096 China Key Laboratory of Computer Network Technology of Jiangsu Province Nanjing210096 China Southeast University Frontiers Science Center for Mobile Information Communication and Security Nanjing210096 China Southeast University School of Cyber Science and Engineering Nanjing210096 China Southeast University School of Cyber Science and Engineering School of Information Science and Engineering Nanjing210096 China Southeast University State Key Laboratory of Mobile Communication Nanjing210096 China

Channel State information (CSI) serves as a critical indicator of wireless signal conditions and is widely regarded by researchers for its sensitivity in detecting changes within the channel. However, traditional sensing technologies often require substantial data and intricate learning algorithms, highlighting an urgent need for advancements in lightweight sensing technologies. These technologies should leverage simpler terminal devices, reduced data volumes, and more straightforward classification algorithms to achieve sensing capability that are comparable to those offered by more complex and established methods. This paper concentrates on the lightweight application of wireless sensing and encompasses the following key contributions: 1) The development of a lightweight sensing model utilizing a single-antenna multicarrier system, which introduces a CSI ratio model that adapts multi-antenna techniques for single-antenna settings;2) The enhancement of feature stability through the introduction of a complex-plane fitting method using artificial vector, alongside a dual receiver based method for cross-scene feature generation aimed at producing stable and high-quality auxiliary features. Experimental results show that the feature extraction capability of the single-antenna multicarrier CSI ratio model is close to that of traditional multi-antenna scheme. On the gait dataset, when the enhanced CSI ratio is used as a feature, the accuracy is nearly 100%, surpassing the 93% accuracy of the original amplitude feature. On the gesture dataset, the combination of the enhanced CSI ratio and PEI features achieves an accuracy of 96%, which is superior to using the original CSI amplitude feature alone. An analysis of resource consumption shows that the lightweight SVM model incurs very low computational overhead during decision-making, validating the potential of this scheme in terms of efficiency and practical application. © 2014 IEEE.

关键词： Communication channels (information theory)

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Image-Text Matching with Intra-Modal Knowledge

SSRN

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SSRN 2022年

作者： Liu, Haoliang Zhang, Yu School of Computer Science and Engineering The Key Lab of Computer Network and Information Integration Ministry of Education Southeast University Nanjing211189 China

In this paper, we study the problem of image-text matching. Image-text matching aims to train an model that learns to identify whether or not an (image, caption) input pair constitutes an aligned pair. To boost image-text matching, the soft label is introduced by knowledge distillation. Its benefit is generally attributed to the privileged information on similarities among image-text pairs provided by the teacher model. However, training a stronger teacher is computationally heavy and inefficient. In this paper, we propose a novel parameter-free Soft Labels Generation(SLG) method for image-text matching which can produce soft labels with privileged information without the need of training a heavy teacher model. We propose to use intra-modal knowledge to generate the soft label for each image-text pair, which provides an alternative to knowledge distillation. Our method is especially applicable to cases where a stronger teacher model is not available, or only limited computation resources are provided. Systematic experiments conducted on public benchmarks demonstrate that our SLG is empirically effective. Our SLG achieves about 2%-3% recall@1 boosting than the baseline. © 2022, The Authors. All rights reserved.

关键词： Personnel training

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Image-Text Retrieval with Intra-Modal Soft Labels

SSRN

引用

SSRN 2022年

作者： Liu, Haoliang Zhang, Yu School of Computer Science and Engineering The Key Lab of Computer Network and Information Integration Ministry of Education Southeast University Nanjing211189 China

Image-text retrieval seeks to find relevant images from a huge image corpus given a textual description, and vice versa. The critical challenge of this task is to well learn image-text alignments to measure the image-text similarity. Existing works normally label the negative/positive image-text pair with a binary label. They assume that a text is only relevant to the paired images and irrelevant to other images in the corpus. However, using the binary labels during training may be sub-optimal for several reasons. First, a binary label can not reveal the precise similarity between an image and a text. Second, the annotated labels may contain noise. In this paper, we study the idea of training the model with the soft label information in which each image-text pair is associated with a specific soft label. Intuitively, if the textual description of an image shares a high textual similarity with a text, the image should also be highly relevant to the text. Therefore, we determine the soft label between the cross-modal (image, text) pair by the similarity between the intra-modal (image caption, text) pair, which is termed as intra-modal soft label (ISL). In the experiment, we evaluated our method on benchmark datasets, which demonstrate the effectiveness of the proposed method. © 2022, The Authors. All rights reserved.

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Graph-Based Proximal Policy Optimization Empowered Adaptive Task Scheduling Leveraging Cloud-Edge Collaboration for Consumer Electronics

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IEEE Transactions on Consumer Electronics 2024年

作者： Li, Peisong Yi, Meng Iqbal, Muddesar Wang, Xinheng Xiao, Ziren Xi'an Jiaotong-Liverpool University School of Advanced Technology Suzhou215123 China Southeast University School of Computer Science and Engineering Key Laboratory of Computer Network and Information Integration Ministry of Education Nanjing211189 China Prince Sultan University Renewable Energy Laboratory Communications and Networks Engineering Department College of Engineering Riyadh11586 Saudi Arabia CIP Technology ltd United Kingdom

The rapid advancement of IoT, AI, and edge computing has led to a significant increase in consumer devices and computation tasks, with new electronics incorporating these technologies to enhance services like VR/AR and autonomous driving, requiring real-time processing for safety and efficiency. However, recent research has focused on optimizing IoT task scheduling and resource allocation through various methods, yet overlooks the dynamic nature of IoT environments, fails to adapt to changing device counts and movements, and often ignores task completion rate in favor of minimizing latency and energy cost. In this context, an adaptive task scheduling and resource allocation strategy is proposed for Edge IoT systems, based on the designed Graph-based Proximal Policy Optimization (GPPO) algorithm. Firstly, the GPPO algorithm enhances PPO for adaptive task scheduling in the fluctuating MEC scenarios, adjusting for the varying number of nearby edge servers. Secondly, it accounts for consumer mobility by opting for local task execution if the consumer risks moving outside the edge server's range, ensuring result reception. Thirdly, it prioritizes task completion rate to increase the number of tasks finished within their acceptable duration. Experimental results demonstrated that the proposed method outperforms traditional methods. © 1975-2011 IEEE.

关键词： Scheduling algorithms

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