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QoE oriented intelligent online learning evaluation technology in B5G scenario

Digital communications and networks 2024年第1期10卷 7-15页

作者： Mingzi Chen Xin Wei Peizhong Xie Zhe Zhang School of Communications and Information Engineering Nanjing University of Posts and TelecommunicationsNanjing210003China Key Lab of Broadband Wireless Communication and Sensor Network Technology(Nanjing University of Posts and Telecommunications) Ministry of EducationNanjing210003China Institute of Higher Education Nanjing University of Posts and TelecommunicationsNanjing210003China

Students'demand for online learning has exploded during the post-COvID-19 pandemic ***,due to their poor learning experience,students'dropout rate and learning performance of online learning are not always *** technical advantages of Beyond Fifth Generation(B5G)can guarantee a good multimedia Quality of Experience(QoE).As a special case of multimedia services,online learning takes into account both the usability of the service and the cognitive development of the *** that affect the Quality of Online Learning Experience(OL-QoE)become more *** get over this dilemma,we propose a systematic scheme by integrating big data,Machine Learning(ML)technologies,and educational psychology ***,we first formulate a general definition of OL-QoE by data analysis and experimental *** formula considers both the subjective and objective factors(i.e.,video watching ratio and test scores)that most affect ***,we induce an extended layer to the classic Broad Learning System(BLS)to construct an Extended Broad Learning System(EBLS)for the students'OL-QoE *** the extended layer can increase the width of the BLS model and reduce the redundant nodes of BLS,the proposed EBLS can achieve a trade-off between the prediction accuracy and computation ***,we provide a series of early intervention suggestions for different types of students according to their predicted OL-QoE *** timely interventions,their OL-QoE and learning performance can be *** results verify the effectiveness oftheproposed scheme.

关键词： B5G Online learning Quality of experience

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Offloading Model of 5G Intelligent Terminal Based on UC-MEC and Node Consensus Mechanism 15

Offloading Model of 5G Intelligent Terminal Based on UC-MEC ...

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15th IEEE International Conference on wireless communications and Signal Processing, WCSP 2023

作者： Wang, Lei Su, Yufei Dou, Haie Cui, Jingwu Zheng, Baoyu Nanjing University of Posts and Telecommunications School of Communications and Information Engineering Nanjing China Nanjing University of Posts and Telecommunications Ministry of Education Key Lab of Broadband Wireless Communication and Sensor Network Technology Nanjing China

ISBN: (纸本)9798350324662

Offloading for 5G intelligent terminals is an important requirement of mobile edge computing (MEC), which can reduce the load of terminal devices and provide more powerful computing power and services. However, due to the interference between different users and the waste of a lot of resources, the role of MEC is greatly limited. In the article, energy-saving blockchain technology is introduced into the user-centric MEC (UC-MEC) system. However, in UC-MEC, it is necessary to comprehensively consider task offloading and energy consumption optimization in the consensus process to improve overall energy efficiency. To solve these problems, a new offloading model of 5G intelligent terminal is proposed, which realizes safe and efficient resource utilization by jointly optimizing the bandwidth and computing resource allocation of access point (AP). The research also proposed a resource consensus mechanism based on Zookeeper atomic broadcast (RC-ZAB), and the parallel optimization framework of relaxed hybrid alternative direction method of multipliers (RH-ADMM) to solve the problems of resource transaction and energy consumption optimization. The experimental results show that compared with the comparison method, the total energy consumption of the proposed method is reduced by 41.39% at most, and the total delay is reduced by 29.80% at most. © 2023 IEEE.

关键词： blockchain Mobile edge computing resource consensus task offloading user-centric

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A General Framework for Intelligent IoT Data Acquisition and Sharing in an Untrusted Environment Based on Blockchain

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China communications 2024年第3期21卷 137-148页

作者： Lu Yin Xue Yongtao Li Qingyuan Wu Luocheng Li Taosen Yang Peipei Zhu Hongbo College of Internet of Things Nanjing University of Posts and TelecommunicationsNanjing 210003China Key Lab of Broadband Wireless Communication and Sensor Network Technology(Nanjing University of Posts and Telecommunications) Ministry of EducationNanjing 210003China Jiangsu Key Laboratory of Wireless Communications Nanjing University of Posts and TelecommunicationsNanjing 210003China Research Institute of Internet of Things Nanjing University of Posts and TelecommunicationsNanjing 210003China Bell Honors School Nanjing University of Posts and TelecommunicationsNanjing 210023China School of Automation Nanjing University of Posts and TelecommunicationsNanjing 210023China

Traditional Io T systems suffer from high equipment management costs and difficulty in trustworthy data sharing caused by *** provides a feasible research direction to solve these problems. The main challenge at this stage is to integrate the blockchain from the resourceconstrained Io T devices and ensure the data of Io T system is credible. We provide a general framework for intelligent Io T data acquisition and sharing in an untrusted environment based on the blockchain, where gateways become Oracles. A distributed Oracle network based on Byzantine Fault Tolerant algorithm is used to provide trusted data for the blockchain to make intelligent Io T data trustworthy. An aggregation contract is deployed to collect data from various Oracle and share the credible data to all on-chain users. We also propose a gateway data aggregation scheme based on the REST API event publishing/subscribing mechanism which uses SQL to achieve flexible data aggregation. The experimental results show that the proposed scheme can alleviate the problem of limited performance of Io T equipment, make data reliable, and meet the diverse data needs on the chain.

关键词： blockchain data sharing Internet of Things Oracle

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Deep Learning Empowered Real-Time Haptic communications for IoT

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IEEE Transactions on Consumer Electronics 2024年

作者： Zhang, Xuguang Chen, Mingkai Nanjing University of Posts and Telecommunications School of Communications and Information Engineering Key Lab of Broadband Wireless Communication and Sensor Network Technology Ministry of Education Nanjing210003 China

The advancement of the Internet of Things (IoT) has enabled a variety of innovative consumer electronics applications. In particular, multi-modal applications integrating audio, video, and haptic interactions have emerged, offering users more immersive experiences and enhanced remote interaction capabilities. However, the distributed and fragmented nature of IoT network resources presents significant challenges for ensuring haptic communications under strict constraints. To address these challenges, this paper proposes a joint coding and network resource scheduling solution for haptic communications in IoT environments. First, we design a scalable and flexible haptic coding scheme that supports efficient and seamless haptic streaming across heterogeneous IoT networks. Second, we introduce a deep learning-based imperceptible Quality of Experience (QoE) recognition method, which uses emotional immersion as an evaluation metric to achieve unobtrusive QoE monitoring. Finally, based on user QoE feedback, we develop an on-demand cooperative network resource scheduling strategy to ensure high QoE, reduce network resource wastage, and optimize the utilization of fragmented IoT resources. Simulation results validate the effectiveness of the proposed architecture and strategies. © 1975-2011 IEEE.

关键词： network coding

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The Proof is in the Pudding: Decision-Oriented Machine-Type wireless Video Transmission

The Proof is in the Pudding: Decision-Oriented Machine-Type ...

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2024 IEEE Global communications Conference, GLOBECOM 2024

作者： Song, Jie Gao, Yun Wu, Dan Zhou, Liang Qian, Yi Nanjing University of Posts and Telecommunications Ministry of Education Key Lab of Broadband Wireless Communication and Sensor Network Technology Nanjing China Army Engineering University of Pla Institute of Communications Engineering Nanjing China University of Nebraska-Lincoln Department of Electrical and Computer Engineering Lincoln United States

ISBN: (纸本)9798350351255

With the rise of the Internet of Everything, machine-type wireless video applications like intelligent surveillance are increasingly becoming mainstream network services. However, the massive transmission of video streams heavily burdens wireless networks. Existing methods typically establish a correlation between the transmitted content and the quality of experience or service, making it challenging to balance trade-offs among decision quality, bandwidth utilization, and latency requirements. To address this issue fundamentally, this paper proposes a novel perspective by designing wireless video transmission strategies from the angle of decision quality, where only the content that impacts the decision outcomes is transmitted. The highlight of this paper is designing a lightweight yet efficient binary classifier that predicts whether the current content will change decision outcomes based on content discrepancies, measured only through pixel-level differences and macroblock-level similarity. Furthermore, these discrepancies can be used to distinguish the background, thereby further saving bandwidth by reusing the background information. Additionally, a fine-tuning module is incorporated to flexibly update the classifier model, ensuring adaptability across various scenarios. Extensive results demonstrate that the proposed strategy achieves over 81% redundant bandwidth savings while maintaining decision quality and catering to strict latency requirements on computation-constrained devices. © 2024 IEEE.

关键词： Television transmission

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Knowledge Tracing Based Wheel Spinning Detection Model in Intelligent Tutoring Systems

Knowledge Tracing Based Wheel Spinning Detection Model in In...

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International Conference on Educational and Information technology (ICEIT)

作者： Mingzi Chen Yanan Chen Shouxiang Ni Peilin Li Xuguang Zhang School of Communications and Information Engineering Nanjing University of Posts and Telecommunications Nanjing China Key Lab of Broadband Wireless Communication and Sensor Network Technology Ministry of Education (NJUPT) Nanjing China

ISBN: (数字)9798350372663

ISBN: (纸本)9798350372670

The recent spread of Intelligent Tutoring Systems (ITS) provides students with access to adaptive learning. However, existing ITS systems face significant challenges in student supervision, primarily in designing robust detection models to characterize the students' learning state and to prevent students from getting stuck in wheel spinning. To this end, this paper proposes a wheel spinning detection model based on Mediating Deep Knowledge Tracing (MDKT). First, the detection model applies “student question mastery” as a mediator variable to improve its interpretability. Specifically, we utilize a Classification and Regression Tree (CART) in the detection model, which enhances the model compatibility with a variety of data by incorporating explicit features (e.g., hint usage). In addition, we employ deep knowledge tracing to merge implicit time-dependent features (e.g., students' knowledge proficiency and forgetting) so as to improve the detection accuracy. Finally, an interpretable neural network based on logistic regression is applied to determine the probability of a student experiencing wheel spinning behavior. Experimental results validate the effectiveness and interpretability of our wheel spinning detection model.

关键词： Adaptation models Logistic regression Neural networks Wheels Feature extraction Data models Spinning

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Cross-Modal Haptic Generation for Emergency Rescue in Internet of Robotic Things

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IEEE Internet of Things Journal 2025年

作者： Liu, Hengfa Wei, Xin Gao, Yun Zhou, Liang Qian, Yi Nanjing University of Posts and Telecommunications School of Communications and Information Engineering Key Lab of Broadband Wireless Communication and Sensor Network Technology Ministry of Education Nanjing210003 China University of Nebraska-Lincoln Department of Electrical and Computer Engineering OmahaNE68588 United States

Robots equipped with multi-modal sensing capabilities play an important role in the Internet of Robotic Things (IoRT), especially in emergency rescue. However, existing rescue robots pose challenges for human operators in achieving precise manipulation due to the absence of haptic signals. Additionally, limited and fluctuating bandwidth in emergency rescue renders current cross-modal haptic generation schemes ineffective. To overcome this dilemma, we propose a novel cross-modal haptic generation scheme that enhances scalability across diverse network conditions by leveraging correlations among audio-visual-haptic modalities. Specifically, we first propose an edge-device collaboration-based architecture that dynamically extracts semantics from audio-visual signals, tailored to the current network conditions, for haptic generation. This is achieved by predicting the network state at the edge and performing multi-modal encoding and fusion at the device. Next, we design a scalable cross-modal haptic generation scheme that implements the optimal generation strategy based on received audio-visual semantics of different granularities, ensuring real-time acquisition of coarse-grained or fine-grained haptic feedback. Finally, numerical experimental results conducted on a multi-modal dataset and a simulated emergency environment indicate that the proposed scheme reliably generates haptic signals in emergency rescue. © 2025 IEEE.

关键词： Robots

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Outage Probability and Power Allocation Analysis of AmBC-Enabled NOMA for Future IoT 23

Outage Probability and Power Allocation Analysis of AmBC-Ena...

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23rd IEEE International Conference on Communication technology, ICCT 2023

作者： Wang, Chongye Liu, Yang Wang, Cheng-Xiang School of Internet of Things Engineering Jiangnan University Wuxi214122 China Nanjing University of Posts and Telecommunications Ministry of Education Key Lab of Broadband Wireless Communication and Sensor Network Technology Nanjing210023 China School of Information Science and Engineering Southeast University National Mobile Communications Research Laboratory Nanjing210096 China Purple Mountain Laboratories Nanjing211111 China

ISBN: (纸本)9798350325959

Ambient backscatter communication (AmBC) combined with non-orthogonal multiple access (NOMA) presents superior energy reusability and higher sum rate. However, existing studies failed to utilize the information of backscatter links. Therefore, to explore the impact of backscatter link, this paper employs three-user NOMA to improve the spectral efficiency. We propose a simple distance power allocation method to accomplish successive interference cancellation. Based on the Monte Carlo method, the outage probability of the proposal is analyzed. Our work provides a scheme for coexistence between AmBC and NOMA systems in the future Internet of Things. © 2023 IEEE.

关键词： Ambient backscatter communication non-orthogonal multiple access outage probability power allocation

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Offloading Model of 5G Intelligent Terminal Based on UC-MEC and Node Consensus Mechanism

Offloading Model of 5G Intelligent Terminal Based on UC-MEC ...

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International Conference on wireless communications and Signal Processing (WCSP)

作者： Lei Wang Yufei Su Haie Dou Jingwu Cui Baoyu Zheng School of Communications and Information Engineering Nanjing University of Posts and Telecommunications Nanjing China Key Lab of Broadband Wireless Communication and Sensor Network Technology (Nanjing University of Posts and Telecommunications) Ministry of Education Nanjing China

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The Proof is in the Pudding: Decision-Oriented Machine-Type wireless Video Transmission

The Proof is in the Pudding: Decision-Oriented Machine-Type ...

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IEEE Conference on Global communications (GLOBECOM)

作者： Jie Song Yun Gao Dan Wu Liang Zhou Yi Qian Key Lab of Broadband Wireless Communication and Sensor Network Technology Nanjing University of Posts and Telecommunications Ministry of Education Nanjing China Institute of Communications Engineering Army Engineering University of PLA Nanjing China Department of Electrical and Computer Engineering University of Nebraska-Lincoln Lincoln USA

ISBN: (数字)9798350351255

ISBN: (纸本)9798350351262

关键词： Correlation wireless networks Surveillance Current measurement Bandwidth Streaming media Quality of experience Internet of Things Global communication Streams

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