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Quality-Aware Massive Content Delivery in Digital Twin-Enabled edge networks

China Communications 2023年第2期20卷 1-13页

作者： Yun Gao Junqi Liao Xin Wei Liang Zhou School of Communications and Information Engineering Nanjing University of Posts and TelecommunicationsNanjing 210003China Key Laboratory of Broadband Wireless Communication and Sensor Network Technology Ministry of EducationNanjing University of Posts and TelecommunicationsNanjing 210003China

Massive content delivery will become one of the most prominent tasks of future B5G/6G communication.However,various multimedia applications possess huge differences in terms of object oriented(i.e.,machine or user)and corresponding quality evaluation metric,which will significantly impact the design of encoding or decoding within content delivery strategy.To get over this dilemma,we firstly integrate the digital twin into the edge networks to accurately and timely capture Quality-of-Decision(QoD)or Quality-of-Experience(QoE)for the guidance of content delivery.Then,in terms of machinecentric communication,a QoD-driven compression mechanism is designed for video analytics via temporally lightweight frame classification and spatially uneven quality assignment,which can achieve a balance among decision-making,delivered content,and encoding latency.Finally,in terms of user-centric communication,by fully leveraging haptic physical properties and semantic correlations of heterogeneous streams,we develop a QoE-driven video enhancement scheme to supply high data fidelity.Numerical results demonstrate the remarkable performance improvement of massive content delivery.

关键词： content delivery digital twin edge networks QoD QoE

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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 management.Considering 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 necessary.Therefore,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 model.Izhikevich neurons inherit the simplicity of LIF neurons but also possess richer behavioral characteristics and flexibility to handle diverse data inputs.Inspired by Fast Spiking Interneurons(FSIs)with a high-frequency firing pattern,we use the parameters of FSIs.Secondly,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 consumption.Furthermore,we propose a multiple iterative Dynamic Spike Timing Dependent Plasticity(DSTDP)algorithm to enhance the accuracy of the LSNN model.Experiments on two server fault datasets demonstrate significant precision,recall,and F1 improvements across three diagnosis dimensions.Simultaneously,lightweight indicators such as Params and FLOPs significantly reduced,showcasing the LSNN’s advanced performance and model efficiency.To 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

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