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3rd International Conference on Algorithms, Microchips, and Network Applications, AMNA 2024

作者： Zhang, Mengyang Yuan, Kaiguo Li, Xiaoyong Key Laboratory of Trustworthy Distributed Computing and Service Beijing University of Posts and Telecommunications Beijing China

ISBN: (纸本)9781510680098

Most current network devices have multiple network interfaces, and multipath transport protocols can utilize multiple network paths (e.g., WiFi and cellular) to improve the performance and reliability of network transmission. The scheduler of the multipath transmission protocol determines the path to which each data packet should be transmitted, and is a key module that affects multipath transmission. However, current multipath schedulers cannot adapt well to various user usage scenarios. In this paper, we propose DRLMS, a deep reinforcement learning based multipath scheduler. DRLMS uses deep reinforcement learning to train neural networks to generate packet scheduling policies. It optimizes the scheduling strategy through feedback to the neural network through the reward function based on the current user usage scenario and QoS. We implement DRLMS in the MPQUIC protocol and compared it with current multipath schedulers. The results show that DRLMS's adaptability to user usage scenarios is significantly outperforms other schedulers. © 2024 SPIE.

关键词： Reinforcement learning

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Robust Spatio-Temporal Trajectory Modeling Based on Auto-Gated Recurrent Unit 19

Robust Spatio-Temporal Trajectory Modeling Based on Auto-Gat...

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2022 IEEE SmartWorld, 19th IEEE International Conference on Ubiquitous Intelligence and computing, 2022 IEEE International Conference on Autonomous and Trusted Vehicles Conference, 22nd IEEE International Conference on Scalable computing and Communications, 2022 IEEE International Conference on Digital Twin, 8th IEEE International Conference on Privacy computing and 2022 IEEE International Conference on Metaverse, SmartWorld/UIC/ATC/ScalCom/DigitalTwin/PriComp/Metaverse 2022

作者： Jia, Jia Li, Xiaoyong Li, Ximing Li, Linghui Yuan, Jie Wang, Hongmiao Gao, Yali Qiu, Pengfei Tang, Jialu Beijing University of Posts and Telecommunications Key Laboratory of Trustworthy Distributed Computing and Service Beijing China

ISBN: (纸本)9798350346558

With the huge amount of crowd mobility data generated by the explosion of mobile devices, deep neural networks (DNNs) are applied to trajectory data mining and modeling, which make great progresses in those scenarios. However, recent studies have demonstrated that DNNs are highly vulnerable to adversarial examples which are crafted by adding subtle, imperceptible noise to normal examples, and leading to the wrong prediction with high confidence. To improve the robustness of modeling spatiotemporal trajectories via DNNs, we propose a collaborative learning model named 'Auto-GRU', which consists of an autoencoder-based self-representation network (SRN) for robust trajectory feature learning and gated recurrent unit (GRU)-based classification network which shares information with SRN for collaborative learning and strictly defending adversarial examples. Our proposed method performs well in defending both white and black box attacks, especially in black-box attacks, where the performance outperforms state-of-the-art methods. Moreover, extensive experiments on Geolife and Beijing taxi traces datasets demonstrate that the proposed model can improve the robustness against adversarial examples without a significant performance penalty on clean examples. © 2022 IEEE.

关键词： Deep neural networks

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Towards Energy-Efficiency: Integrating MATD3 Reinforcement Learning Method for Computational Offloading in RIS-Aided UAV-MEC Environments

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IEEE Internet of Things Journal 2025年

作者： Wu, Liangshun Zhang, Cong Zhang, Bin Du, Jianbo Qu, Junsuo Key Laboratory of Embedded System and Service Computing Tongji University Ministry of Education Shanghai201804 China Shanghai Key Laboratory of Trustworthy Computing East China Normal University Shanghai201210 China School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Shanghai200240 China Xi'an Key Laboratory of Advanced Control and Intelligent Process School of Automation Xi'an University of Posts & Telecommunications Xi'an710061 China Xinjiang University of Political Science and Law Tumushuke832003 China School of Remote Sensing and Information Engineering Wuhan University Wuhan430079 China Department of Computer City University of Hong Kong 999077 Hong Kong School of Communications and Information Engineering Xi'an University of Posts & Telecommunications Xi'an710061 China

With the proliferation of IoT devices, there is an escalating demand for enhanced computing and communication capabilities. Mobile Edge computing (MEC) addresses this need by relocating computing resources to the network edge, thereby delivering swifter and more efficient services. This paper introduces a computation offloading and energy consumption optimization framework that leverages Reconfigurable Intelligent Surfaces (RIS), Unmanned Aerial Vehicles (UAVs), and MEC. The scheme aims to maximize energy efficiency through the optimization of task allocation, RIS phase shifts, and UAV trajectories. By employing the Multi-Agent Twin Delayed Deep Deterministic Policy Gradient (MATD3) reinforcement learning algorithm, the paper further refines UAV trajectories and RIS configurations. The simulation results indicate that the proposed method surpasses the traditional Concave-Convex Procedure (CCCP) algorithm in both UAV trajectory control and RIS configuration, demonstrating quicker convergence and enhanced stability. The method proves to be adaptable to diverse environments and tasks, showcasing notable benefits in RIS-assisted interference suppression, particularly with large RIS, thereby enhancing UAV data reception rates. Additionally, MATD3 exhibits faster and smoother convergence for extended task durations and smaller RIS scenarios. Simulation results reveal that UAVs tend to move closer to RIS, with energy efficiency falling as IoT tasks increase, affirming the proposed algorithm's high energy efficiency and effectiveness. © 2025 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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A Neural Network and Bloom Filter-Based Name Search Method in Named Data Networking 4

A Neural Network and Bloom Filter-Based Name Search Method i...

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4th International Conference on Neural Networks, Information and Communication Engineering, NNICE 2024

作者： Liu, Yue Li, Xiaoyong Si, Xiaotian Zhang, Mengyang Yuan, Kaiguo Li, Pengcheng Beijing University of Posts and Telecommunications Key Laboratory of Trustworthy Distributed Computing and Service Beijing China

ISBN: (纸本)9798350394375

In a new network architecture like NDN, designing an efficient name lookup method has always been a valuable challenge. The paper studies the name lookup technology based on the Bloom filter and BPNN neural network mapping model in named data networking. In the part of the hybrid model based on the Bloom filter and BPNN, the paper proposes the fusion idea of the Bloom filter and BPNN, and provides a detailed introduction to the construction process of the hybrid model. At the same time, in order to improve the memory utilization of traditional Bloom filters, this article proposes a way to use neural networks to enhance it, and proved its effectiveness through experiments. © 2024 IEEE.

关键词： Network architecture

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Encrypted Traffic Classification Model Based on SwinT-CNN 4

Encrypted Traffic Classification Model Based on SwinT-CNN

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4th International Conference on Computer Engineering and Application, ICCEA 2023

作者： Wang, Yueyang Gao, Yali Li, Xiaoyong Yuan, Jie Beijing University of Posts and Telecommunications Key Laboratory of Trustworthy Distributed Computing and Service Beijing China

ISBN: (纸本)9798350347548

With the rapid development of the network, the proportion of encrypted traffic in the network is increasing, which brings great challenges to traffic classification. The traditional encrypted traffic classification algorithm extracts the port number of the traffic or the load information of the traffic packet for feature matching, which has no effect in the current encrypted traffic. The encrypted traffic classification algorithm based on machine learning especially relies on the features extracted by professionals for encrypted traffic, which is inefficient and has low accuracy. And, The accuracy and overall performance of existing deep learning methods also need to be improved. Therefore, in order to improve the accuracy of encrypted traffic classification, this paper proposes an encrypted traffic classification method based on deep learning, which tries to combine the core modules of CNN and Swin Transformer with the encrypted traffic classification model to realize the identification of the application type of encrypted traffic. Among them, the CNN module is able to capture the local spatial information characteristics of encrypted traffic data. The multi-head attention mechanism of Swin Transformer can capture global information about the association between attributes of the data. The model is tested on the public ISCX VPN-nonVPN encryption dataset to evaluate the performance of the proposed model. Experimental results show that the accuracy of our proposed model reaches 96.7%. Compared with the other best-performing deep learning models, the proposed model improves the accuracy by 3.1% and the F1 value by 2.7%. © 2023 IEEE.

关键词： Cryptography

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An Intrusion Detection Method Based on Transformer-LSTM Model 3

An Intrusion Detection Method Based on Transformer-LSTM Mode...

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3rd International Conference on Neural Networks, Information and Communication Engineering, NNICE 2023

作者： Zhang, Zhipeng Si, Xiaotian Li, Linghui Gao, Yali Li, Xiaoyong Yuan, Jie Xing, Guoqiang Beijing University of Posts and Telecommunications Key Laboratory of Trustworthy Distributed Computing and Service Beijing China

ISBN: (纸本)9798350335972

With the development of network technologies, network intrusion has become increasing complex which makes the intrusion detection challenging. Traditional intrusion detection algorithms detect intrusion traffic through intrusion traffic characteristics or machine learning. These methods are inefficient due to the dependence of manual work. Therefore, in order to improve the efficiency and the accuracy, we propose an intrusion detection method based on deep learning. We integrate the Transformer and LSTM module with intrusion detection model to automatically detect network intrusion. The Transformer and LSTM can capture the temporal information of the traffic data which benefits to distinguish the abnormal data from normal data. We conduct experiments on the publicly available NSL-KDD dataset to evaluate the performance of our proposed model. The experimental results show that the proposed model outperforms other deep learning based models. © 2023 IEEE.

关键词： Intrusion detection

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Real Time Attrition Prediction Mechanism Based on Deep Learning 2

Real Time Attrition Prediction Mechanism Based on Deep Learn...

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2nd International Signal Processing, Communications and Engineering Management Conference, ISPCEM 2022

作者： Wu, Chen Beijing University of Posts and Telecommunications Key Laboratory of Trustworthy Distributed Computing and Service Beijing China

ISBN: (纸本)9781665492713

Modern companies mostly have their own employee management systems, which retain a large amount of employee behavior data. These data can be used to mine valuable information, such as whether employees have attrition intentions. This paper proposes a mechanism of attrition prediction based on deep learning, which mainly includes three modules: (1) Using LSTM to build a performance-attrition pre-trained model;(2)Using Transformer to build a sentiment tendency model based on employee weekly reports;(3) Based on (1), (2), establishing Real Time Attrition Prediction Mechanism in the company's employee management system to predict employees' willingness to leave at any time. The purpose of this mechanism is to help companies manage better and reduce losses caused by employee attrition. © 2022 IEEE.

关键词： Forecasting

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XThe Application of Robot Localization and Navigation Based on WSN in the Disaster Relief

XThe Application of Robot Localization and Navigation Based ...

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作者： Cui, Xiaoyan Chen, Xiaoshuang Shi, Wei Zhang, Yi Key Laboratory of Trustworthy Distributed Computing and Service Automation School BUPT China

ISBN: (纸本)9783642357947

Disasters always disturb our normal life, and the loss is immeasurable. In this paper, we intend to find a new way to reduce the harm. We select the wireless sensor network to control the robot localization and navigation in the disaster relief. This paper means to design a pre-disaster monitoring and reconstruct network with new nodes added in the network, and robot position & navigation, and also build a post-disaster history disaster mode. This paper also takes consider of the actual result of the node energy consumption. It has practical significance. © Springer-Verlag Berlin Heidelberg 2013.

关键词： Robots

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An Effective Cloud Workflow Scheduling Approach Combining PSO and Idle Time Slot-Aware Rules

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IEEE/CAA Journal of Automatica Sinica 2021年第5期8卷 1079-1094页

作者： Yun Wang Xingquan Zuo the School of Computing Science Beijing University of Posts and TelecommunicationsBeijing 100876China the School of Information Engineering Nanchang Institute of TechnologyNanchang 330099China the Key Laboratory of Trustworthy Distributed Computing and Service(BUPT) Ministry of EducationBeijing 100876China

Workflow scheduling is a key issue and remains a challenging problem in cloud *** with the large number of virtual machine(VM)types offered by cloud providers,cloud users need to choose the most appropriate VM type for each *** task scheduling sequences exist in a workflow *** task scheduling sequences have a significant impact on the scheduling *** is not easy to determine the most appropriate set of VM types for tasks and the best task scheduling ***,the idle time slots on VM instances should be used fully to increase resources'utilization and save the execution cost of a *** paper considers these three aspects simultaneously and proposes a cloud workflow scheduling approach which combines particle swarm optimization(PSO)and idle time slot-aware rules,to minimize the execution cost of a workflow application under a deadline constraint.A new particle encoding is devised to represent the VM type required by each task and the scheduling sequence of *** idle time slot-aware decoding procedure is proposed to decode a particle into a scheduling *** handle tasks'invalid priorities caused by the randomness of PSO,a repair method is used to repair those priorities to produce valid task scheduling *** proposed approach is compared with state-of-the-art cloud workflow scheduling *** show that the proposed approach outperforms the comparative algorithms in terms of both of the execution cost and the success rate in meeting the deadline.

关键词： Cloud computing idle time slot particle swarm optimization task scheduling sequence workflow scheduling

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A recommendation algorithm based on adaptive privacy protection 9

A recommendation algorithm based on adaptive privacy protect...

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9th International Symposium on Advances in Electrical, Electronics, and Computer Engineering, ISAEECE 2024

作者： Song, Yiyang Li, Xiaoyong Yuan, Kaiguo Kong, Weiyu Key Laboratory of Trustworthy Distributed Computing and Service Beijing University of Posts and Telecommunications Beijing100876 China

ISBN: (纸本)9781510683303

Differential privacy, as a provable privacy protection framework, has been widely applied in recommendation systems. However, the integration of existing collaborative filtering algorithms with differential privacy typically encounters two issues. Firstly, algorithms often use the same privacy budget for data from different time periods, introducing unnecessary noise and reducing performance. Secondly, when using gradient descent to solve collaborative filtering models, the allocation of privacy budgets is relatively simple, usually evenly distributed across each iteration. This allocation requires a predefined number of iterations. If the number is too small, the algorithm may terminate before reaching the optimal solution;if too large, each budget per iteration may be too small, leading to excessive noise. To address these issues, an adaptive privacy budget recommendation algorithm is proposed. Firstly, a dynamic time window-based privacy budget calculation algorithm is utilized to provide varying levels of privacy protection for rating data from different time periods, thus avoiding unnecessary noise. Then, an adaptive privacy budget gradient descent strategy is employed to solve the collaborative filtering model, which dynamically allocates privacy budgets based on the noise gradient of each round. This algorithm is compared with existing differential privacy-based recommendation algorithms on the Movielens-1M dataset, demonstrating superior predictive accuracy and lower sensitivity to privacy protection. © 2024 SPIE.

关键词： Differential privacy

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