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Context-Based Intelligent Scheduling and Knowledge Push Algorithms for AR-Assist Communication Network Maintenance

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Computer Modeling in Engineering & Sciences 2019年第2期118卷 291-315页

作者： lanlan rui Yabin Qin Biyao Li Zhipeng Gao State Key Laboratory of Networking and Switching Technology Beijing University of Posts and TelecommunicationsBeijing100876China

Maintenance is an important aspect in the lifecycle of communication network *** problems in the maintenance of communication networks include inconvenient data carrying and sub-optimal scheduling of work orders,which significantly restrict the efficiency of maintenance ***,most maintenance systems are still based on cloud architectures that slow down data *** a focus on the completion time,quality,and load balancing of maintenance work,we propose in this paper a learning-based virus evolutionary genetic algorithm with multiple quality-ofservice(QoS)constraints to implement intelligent scheduling in an edge *** algorithm maintains the diversity of the population and improves the speed of convergence using a fitness function and a learning-based population generation *** test results demonstrate that the algorithm delivers good performance in terms of load balancing and QoS *** also propose a knowledge push algorithm based on a context model for intelligently pushing relevant knowledge according to the given *** simulation results demonstrate that our scheme can improve the efficiency of on-site maintenance.

关键词： Internet of things(IoT) edge computing Augmented Reality(AR) maintenance communication network.

Theil Utility Based Multi-Device Cooperation Mechanism for Service Quality Equilibrium in Ubiquitous Stub Environments

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China Communications 2014年第6期11卷 140-150页

作者： rui lanlan MU Nan GUO Shaoyong QIU Xuesong ZHANG Jie State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications AsiaInfo-Linkage Inc.

An approach to make heterogeneous devices cooperate with each other to provide ubiquitous service remains a longstanding challenge in ubiquitous environments. In addition, when multi-user request ubiquitous services simultaneously, the arbitrariness in resource allocation process easily leads to non-equilibrium of qualities of multiuser's services. Thus, How to design an effective heterogeneous devices cooperation mechanism that meet the QoS requirements and also equilibrate QoS among multi-user's services becomes a very tough problem. In the paper, Equilibrium Index and Theil Utility function are imported to establish a multidevice equilibrium cooperation model, which is benefit for resource allocation in ubiquitous environments. Then a Theil-Equilibrium based Cooperation Mechanism for multi-service is proposed. Firstly, in order to simplify calculating, a dimensionless processing is introduced. After that, a cooperation approach with Theil-Utility Heuristic algorithm is designed to choose the best solution for the simplified model. At last, the mechanism is simulated in a smart home scenario. The simulation results show that this mechanism outperforms non-equilibrium method by improving almost 40% in internal equilibrium with just reducing 0-2% in the total utility ofall the requested ubiquitous services. These data show that the proposed mechanism performs well in equilibrating qualities of multi-user's ubiquitous services.

关键词： ubiquitousstub environments cooperation among multi-device QoS equilibrium equilibrium index theil utility

CMLP: Exploiting Caches at Multiple Levels of Proxies to Enhance Seamless Mobility Support in Information-Centric Networks

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China Communications 2016年第10期13卷 86-107页

作者： Haoqiu Huang lanlan rui Weiwei Zheng Danmei Niu Xuesong Qiu State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications

The recent evolution of the Internet towards "Information-centric" transfer modes has renewed the interest in exploiting proxies to enhance seamless mobility. In this work, we focus on the case of multiple levels of proxies in ICN architectures, in which content requests from mobile subscribers and the corresponding items are proactively cached to these proxies at different levels. Specifically, we present a multiple-level proactive caching model that selects the appropriate subset of proxies at different levels and supports distributed online decision procedures in terms of the tradeoff between delay and cache cost. We show via extensive simulations the reduction of up to 31.63% in the total cost relative to Full Caching, in which caching in all 1-level neighbor proxies is performed, and up to 84.21% relative to No Caching, in which no caching is used. Moreover, the proposed model outperforms other approaches with a flat cache structure in terms of the total cost.

关键词： Information-centric networking mobility multiple levels of proxies Publish-Subscribe

An Incentive Mechanism Model for Crowdsensing with Distributed Storage in Smart Cities

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Computers, Materials & Continua 2023年第8期76卷 2355-2384页

作者： Jiaxing Wang lanlan rui Yang Yang Zhipeng Gao Xuesong Qiu State Key Laboratory of Networking and Switching Technology Beijing University of Posts and TelecommunicationsBeijingChina

Crowdsensing,as a data collection method that uses the mobile sensing ability of many users to help the public collect and extract useful information,has received extensive attention in data *** crowdsensing relies on user equipment to consume resources to obtain information,and the quality and distribution of user equipment are uneven,crowdsensing has problems such as low participation enthusiasm of participants and low quality of collected data,which affects the widespread use of *** paper proposes to apply the blockchain to crowdsensing and solve the above challenges by utilizing the characteristics of the blockchain,such as immutability and *** architecture for constructing a crowdsensing incentive mechanism under distributed incentives is proposed.A multi-attribute auction algorithm and a k-nearest neighbor-based sensing data quality determination algorithm are proposed to support the *** users upload data,determine data quality according to the algorithm,update user reputation,and realize the selection of perceived *** process of screening data and updating reputation value is realized by smart contracts,which ensures that the information cannot be tampered with,thereby encouraging more users to *** of the simulation show that using two algorithms can well reflect data quality and screen out malicious *** the help of blockchain performance,the architecture and algorithm can achieve decentralized storage and tamper-proof information,which helps to motivate more users to participate in perception tasks and improve data quality.

关键词： Crowdsensing incentive mechanism blockchain smart contract

The Lightweight Edge-Side Fault Diagnosis Approach Based on Spiking Neural Network

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Computers, Materials & Continua 2024年第6期79卷 4883-4904页

作者： Jingting Mei Yang Yang Zhipeng Gao lanlan rui Yijing Lin State Key Laboratory of Networking and Switching Technology Beijing University of Posts and TelecommunicationsBeijing100876China

Network fault diagnosis methods play a vital role in maintaining network service quality and enhancing user experience as an integral component of intelligent network *** the unique characteristics of edge networks,such as limited resources,complex network faults,and the need for high real-time performance,enhancing and optimizing existing network fault diagnosis methods is ***,this paper proposes the lightweight edge-side fault diagnosis approach based on a spiking neural network(LSNN).Firstly,we use the Izhikevich neurons model to replace the Leaky Integrate and Fire(LIF)neurons model in the LSNN *** neurons inherit the simplicity of LIF neurons but also possess richer behavioral characteristics and flexibility to handle diverse data *** by Fast Spiking Interneurons(FSIs)with a high-frequency firing pattern,we use the parameters of ***,inspired by the connection mode based on spiking dynamics in the basal ganglia(BG)area of the brain,we propose the pruning approach based on the FSIs of the BG in LSNN to improve computational efficiency and reduce the demand for computing resources and energy ***,we propose a multiple iterative Dynamic Spike Timing Dependent Plasticity(DSTDP)algorithm to enhance the accuracy of the LSNN *** on two server fault datasets demonstrate significant precision,recall,and F1 improvements across three diagnosis ***,lightweight indicators such as Params and FLOPs significantly reduced,showcasing the LSNN’s advanced performance and model *** conclude,experiment results on a pair of datasets indicate that the LSNN model surpasses traditional models and achieves cutting-edge outcomes in network fault diagnosis tasks.

关键词： Network fault diagnosis edge networks Izhikevich neurons pruning dynamic spike timing dependent plasticity learning

General, practical, and accurate models for the performance analysis of cache cascades

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Science China(Information Sciences) 2017年第4期60卷 244-246页

作者： Haoqiu HUANG lanlan rui Weiwei ZHENG Danmei NIU Xuesong QIU State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications

Dear editor,Owing to the significant benefits generated by caching in Content Delivery Networks(CDN)and Information-Centric Networking(ICN),the performance analysis of multi-cache systems has received renewed interest by the networking research community[1–5].To analyze the performance(i.e.,cache hit probability),Melazzi et al.[3]proposed an approximate approach that uses

关键词： and accurate models for the performance analysis of cache cascades practical General

Two-Stage Edge-Side Fault Diagnosis Method Based on Double Knowledge Distillation

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Computers, Materials & Continua 2023年第9期76卷 3623-3651页

作者： Yang Yang Yuhan Long Yijing Lin Zhipeng Gao lanlan rui Peng Yu State Key Laboratory ofNetworking and Switching Technology Beijing University of Posts and TelecommunicationsBeijing100876China School of Computer Science and Engineering Nanyang Technological University639798Singapore Science and Technology on Communication Networks Laboratory The 54th Research Institute of China Electronics Technology Group CorporationShijiazhuang050051China

With the rapid development of the Internet of Things(IoT),the automation of edge-side equipment has emerged as a significant *** existing fault diagnosismethods have the characteristics of heavy computing and storage load,and most of them have computational redundancy,which is not suitable for deployment on edge devices with limited resources and *** paper proposes a novel two-stage edge-side fault diagnosis method based on double knowledge ***,we offer a clustering-based self-knowledge distillation approach(Cluster KD),which takes the mean value of the sample diagnosis results,clusters them,and takes the clustering results as the terms of the loss *** utilizes the correlations between faults of the same type to improve the accuracy of the teacher model,especially for fault categories with high ***,the double knowledge distillation framework uses ordinary knowledge distillation to build a lightweightmodel for edge-side *** propose a two-stage edge-side fault diagnosismethod(TSM)that separates fault detection and fault diagnosis into different stages:in the first stage,a fault detection model based on a denoising auto-encoder(DAE)is adopted to achieve fast fault responses;in the second stage,a diverse convolutionmodel with variance weighting(DCMVW)is used to diagnose faults in detail,extracting features frommicro andmacro *** comparison experiments conducted on two fault datasets,it is proven that the proposed method has high accuracy,low delays,and small computation,which is suitable for intelligent edge-side fault *** addition,experiments show that our approach has a smooth training process and good balance.

关键词： Fault diagnosis knowledge distillation edge-side lightweight model high similarity

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基于组合模型的短时交通流量预测算法

电子与信息学报 2016年第5期38卷 1227-1233页

作者：芮兰兰李钦铭北京邮电大学网络与交换技术国家重点实验室北京100876

交通流量预测是实现智能交通技术的核心问题,及时准确地预测道路交通流量是实现动态交通管理的前提,短时交通流量的预测是交通流量预测的重要组成部分。该文针对十字路口的短时交通流量预测问题设计了基于交通流量序列分割和极限学习机(... 详细信息

交通流量预测是实现智能交通技术的核心问题,及时准确地预测道路交通流量是实现动态交通管理的前提,短时交通流量的预测是交通流量预测的重要组成部分。该文针对十字路口的短时交通流量预测问题设计了基于交通流量序列分割和极限学习机(Extreme Learning Machine,ELM)组合模型的交通流量预测算法(Traffic Flow Prediction Based on Combined Model,TFPBCM)。该算法首先采用K-means对交通流量数据在时间上进行序列分割,然后采用ELM对各个序列进行建模和预测。仿真实验证明,与单一的BP(Back Propagation)神经网络和ELM相比,该组合模型算法建模时间为BP的1/10,ELM建模时间的4倍,均方误差为BP的1/50,ELM的1/20,该组合模型算法决定系数R2更接近于1,模型可信度更高。

关键词：短时交通流量 K均值算法极限学习机组合模型算法

车辆边缘网络中基于多参数MDP模型的动态服务迁移策略

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通信学报 2020年第1期41卷 1-14页

作者：郭辉芮兰兰高志鹏北京邮电大学网络与交换国家重点实验室

为解决车辆移动及边缘服务器有限服务范围造成的服务中断问题,为车辆边缘网络提出一种基于多参数马尔可夫决策过程的动态服务迁移算法。通过构造包含时延、带宽、服务器处理能力及车辆运动信息的多参数MDP收益函数,弥补了单纯基于距离... 详细信息

为解决车辆移动及边缘服务器有限服务范围造成的服务中断问题,为车辆边缘网络提出一种基于多参数马尔可夫决策过程的动态服务迁移算法。通过构造包含时延、带宽、服务器处理能力及车辆运动信息的多参数MDP收益函数,弥补了单纯基于距离进行服务迁移方案的不足;不再使用单一迁移目标服务器,结合车辆运动及时延限制构造候选服务器集合,基于Bellman方程表示的长期收益值进行迁移决策;利用历史数据进行权重计算及数据更新,提高了算法对动态环境的适应能力。仿真结果表明,所提算法降低了服务时延、数据分组丢失率及服务迁移次数。

关键词：边缘计算车辆边缘网络动态服务迁移 MDP