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User-Oriented Edge Node Grouping in Mobile Edge Computing

IEEE TRANSACTIONS ON MOBILE COMPUTING 2023年第6期22卷 3691-3705页

作者： Li, Qing Ma, Xiao Zhou, Ao Luo, Xiapu Yang, Fangchun Wang, Shangguang Beijing Univ Posts & Telecommun State Key Lab Networking & Switching Technol Beijing 100876 Peoples R China Hong Kong Polytech Univ Dept Comp Hong Kong Peoples R China

In mobile edge computing networks, densely deployed access points are empowered with computation and storage capacities. This brings benefits of enlarged edge capacity, ultra-low latency, and reduced backhaul congestion. This paper concerns edge node grouping in mobile edge computing, where multiple edge nodes serve one end user cooperatively to enhance user experience. Most existing studies focus on centralized schemes that have to collect global information and thus induce high overhead. Although some recent studies propose efficient decentralized schemes, most of them did not consider the system uncertainty from both the wireless environment and other users. To tackle the aforementioned problems, we first formulate the edge node grouping problem as a game that is proved to be an exact potential game with a unique Nash equilibrium. Then, we propose a novel decentralized learning-based edge node grouping algorithm, which guides users to make decisions by learning from historical feedback. Furthermore, we investigate two extended scenarios by generalizing our computation model and communication model, respectively. We further prove that our algorithms converge to the Nash equilibrium with upper-bounded learning loss. Simulation results show that our mechanisms can achieve up to 96.99% of the oracle benchmark.

关键词： Peer-to-peer computing Wireless communication Task analysis Games Reinforcement learning Multi-access edge computing Heuristic algorithms Mobile edge computing edge node grouping game theory reinforcement learning

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VideoQA-TA: Temporal-Aware Multi-Modal Video Question Answering 31

VideoQA-TA: Temporal-Aware Multi-Modal Video Question Answer...

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31st International Conference on Computational Linguistics, COLING 2025

作者： Wu, Zhixuan Cheng, Bo Han, Jiale Ma, Jiabao Zhang, Shuhao Chen, Yuli Li, Changbao State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications China Hong Kong University of Science and Technology Hong Kong North China Institute of Computing Technology China

ISBN: (纸本)9798891761964

Video question answering (VideoQA) has recently gained considerable attention in the field of computer vision, aiming to generate answers rely on both linguistic and visual reasoning. However, existing methods often align visual or textual features directly with large language models, which limits the deep semantic association between modalities and hinders a comprehensive understanding of the interactions within spatial and temporal contexts, ultimately leading to sub-optimal reasoning performance. To address this issue, we propose a novel temporal-aware framework for multi-modal video question answering, dubbed VideoQA-TA, which enhances reasoning ability and accuracy of VideoQA by aligning videos and questions at fine-grained levels. Specifically, an effective Spatial-Temporal Attention mechanism (STA) is designed for video aggregation, transforming video features into spatial and temporal representations while attending to information at different levels. Furthermore, a Temporal Object Injection strategy (TOI) is proposed to align object-level and frame-level information within videos, which further improves the accuracy by injecting explicit temporal information. Experimental results on MSVD-QA, MSRVTT-QA, and ActivityNet-QA datasets demonstrate the superior performance of our proposed method compared with the current SOTAs, meanwhile, visualization analysis further verifies the effectiveness of incorporating temporal information to videos. © 2025 Association for Computational Linguistics.

关键词： Question answering

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Adaptive Hybrid Beamforming for UAV mmWave Communications Against Asymmetric Jitter

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IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS 2024年第8期23卷 9432-9445页

作者： Chen, Wenyun Liu, Chenxi Wang, Wei Peng, Mugen Zhang, Wei Beijing Univ Posts & Telecommun State Key Lab Networking & Switching Technol Beijing 100876 Peoples R China Peng Cheng Lab Shenzhen 518000 Peoples R China Univ New South Wales Sch Elect Engn & Telecommun Sydney NSW 2052 Australia

Jittering effect is a critical issue in the unmanned aerial vehicle (UAV) millimeter wave (mmWave) communications. In this paper, we identify and characterize the asymmetric impact of jitter on the angular domain information in the UAV mmWave channels, showing how it can lead to significant performance degradation if not properly handled. To address this issue, we propose an adaptive hybrid beamforming for the UAV mmWave communications to maximize the average transmission rate against the asymmetric jitter. The proposed adaptive hybrid beamforming consists of an optimal beam angular range design and an adaptive beamforming vector design. Specifically, we first analytically derive a compact expression of the average transmission rate of our systems. Based on the derived expression, we optimize the beam angular range to maximize the average transmission rate under arbitrary asymmetric jitter. Moreover, we derive the asymptotic expression of the optimal beam angular range in the high signal-to-noise ratio regime. We further develop a simple-yet-efficient algorithm to obtain an adaptive beamforming vector that delivers the optimal beam angular range. Through numerical results, we verify the destructive impacts of the asymmetric jitter, and demonstrate how our proposed scheme can be robust to it, compared to the existing methods without considering the asymmetric jitter.

关键词： UAV mmWave communications jittering effects hybrid beamforming asymmetric beam angular range UAV mmWave communications jittering effects hybrid beamforming asymmetric beam angular range

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Joint Optimization of Energy Consumption and Network Latency in Blockchain-Enabled Fog Computing Networks

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China Communications 2024年第4期21卷 104-119页

作者： Huang Xiaoge Yin Hongbo Cao Bin Wang Yongsheng Chen Qianbin Zhang Jie School of Communications and Information Engineering Chongqing University of Posts and TelecommunicationsChongqing 400065China State Key Laboratory of Networking and Switching Technology Beijing University of Posts and TelecommunicationsBeijing 100876China School of Communication and Information Engineering University of SheffieldUnited Kingdom

Fog computing is considered as a solution to accommodate the emergence of booming requirements from a large variety of resource-limited Internet of Things(IoT)*** ensure the security of private data,in this paper,we introduce a blockchain-enabled three-layer device-fog-cloud heterogeneous network.A reputation model is proposed to update the credibility of the fog nodes(FN),which is used to select blockchain nodes(BN)from FNs to participate in the consensus *** to the Rivest-Shamir-Adleman(RSA)encryption algorithm applied to the blockchain system,FNs could verify the identity of the node through its public key to avoid malicious ***,to reduce the computation complexity of the consensus algorithms and the network overhead,we propose a dynamic offloading and resource allocation(DORA)algorithm and a reputation-based democratic byzantine fault tolerant(R-DBFT)algorithm to optimize the offloading decisions and decrease the number of BNs in the consensus algorithm while ensuring the network *** results demonstrate that the proposed algorithm could efficiently reduce the network overhead,and obtain a considerable performance improvement compared to the related algorithms in the previous literature.

关键词： blockchain energy consumption fog computing network Internet of Things latency

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Toward Efficient Satellite Computing Through Adaptive Compression

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IEEE TRANSACTIONS ON SERVICES COMPUTING 2024年第6期17卷 4411-4422页

作者： Yang, Chen Sun, Qibo Zhang, Qiyang Lu, Hao Ardagna, Claudio A. Wang, Shangguang Xu, Mengwei Beijing Univ Posts & Telecommun State Key Lab Networking & Switching Technol Beijing 100876 Peoples R China Peking Univ Sch Comp Sci Minist Educ Key Lab High Confidence Software Technol Beijing 100871 Peoples R China Univ Milan Dipartimento Informat I-20122 Milan Italy

The rapid development of Low Earth Orbit (LEO) satellite constellations offers significant potential for in-orbit services, particularly in mitigating the impact of sudden natural disasters. However, the massive data collected by these satellites are often large and severely constrained by limited transmission capabilities when sending data to the ground. Satellite computing, which utilizes onboard computational capacity to process data before transmission, presents a promising solution to alleviate the downlink burden. Nonetheless, this paradigm introduces another bottleneck: limited onboard computing capacity, resulting in slow in-orbit processing and poor results. Current satellite computing systems struggle to efficiently address both data transmission and computing bottlenecks, particularly for urgent disaster services that demand accurate and timely results. Thus, we introduce an efficient satellite computing system designed to jointly mitigate these bottlenecks, thereby providing better service. The core idea is to utilize onboard computing capacity for swift in-orbit annotation of image regions, enabling adaptive compression and download based on annotation confidence and perceived downlink availability. Once the data is downloaded, image restoration and re-inference are performed on the ground to enhance accuracy. Compared to satellite-only inference, our system demonstrates an average improvement in inference accuracy of 3.8%. Furthermore, compared to ground-only inference, with only a 2.8% accuracy loss, our system achieves a 38.4% reduction in response time and saves 71.6% of downlink volume on average.

关键词： Satellites Accuracy Image coding Downlink Image restoration Computational modeling Disasters Orbits Hardware Time factors Satellite computing in-orbit service inference adaptive compression

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Joint Beamforming Design for Multi-Functional RIS-Aided Uplink Communications

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IEEE COMMUNICATIONS LETTERS 2023年第10期27卷 2697-2701页

作者： Yan, Yingjie Wang, Ying Ni, Wanli Niyato, Dusit Beijing Univ Posts & Telecommun State Key Lab Networking & Switching Technol Beijing 100876 Peoples R China Nanyang Technol Univ Sch Comp Sci & Engn Singapore 117583 Singapore

This letter investigates the joint beamforming design problem in a multi-functional reconfigurable intelligence surface (MF-RIS)-aided uplink communication system. Specifically, each element of the MF-RIS is able to switch between the reflection and refraction modes, while achieving power amplification of incident signals. By jointly optimizing the transmit power of users, MF-RIS coefficients, and the receive beamforming of the base station, we formulate a mixed-integer non-linear programming (MINLP) problem to maximize the uplink sum-rate. To solve this challenging MINLP problem, we propose an iterative algorithm by using the successive convex approximation and penalty function methods, where the optimal transmit power is derived. Numerical results show the effectiveness of our proposed algorithm and the superiority of the MF-RIS compared to other RIS counterparts.

关键词： Reconfigurable intelligent surface uplink communication sum-rate maximization beamforming design

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SBFT:A BFT Consensus Mechanism Based on DQN Algorithm for Industrial Internet of Thing

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China Communications 2023年第10期20卷 185-199页

作者： Ningjie Gao Ru Huo Shuo Wang Jiang Liu Tao Huang Yunjie Liu State Key Laboratory of Networking and Switching Technology Beijing University of Posts and TelecommunicationsBeijing 100876China Faculty of Information Technology Beijing University of TechnologyBeijing 100124China Future Network Research Center Purple Mountain LaboratoriesNanjing 211111China

With the development and widespread use of blockchain in recent years,many projects have introduced blockchain technology to solve the growing security issues of the Industrial Internet of Things(IIoT).However,due to the conflict between the operational performance and security of the blockchain system and the compatibility issues with a large number of IIoT devices running together,the mainstream blockchain system cannot be applied to IIoT *** order to solve these problems,this paper proposes SBFT(Speculative Byzantine Consensus Protocol),a flexible and scalable blockchain consensus mechanism for the Industrial Internet of *** has a consensus process based on speculation,improving the throughput and consensus speed of blockchain systems and reducing communication *** order to improve the compatibility and scalability of the blockchain system,we select some nodes to participate in the consensus,and these nodes have better performance in the *** multiple properties determine node performance,we abstract the node selection problem as a joint optimization problem and use Dueling Deep Q Learning(DQL)to solve ***,we evaluate the performance of the scheme through simulation,and the simulation results prove the superiority of our scheme.

关键词： Industrial Internet of Things Byzantine fault tolerance speculative consensus mechanism Markov decision process deep reinforcement learning

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Dynamic Beam Hopping and Resource Allocation for Non-Uniform Traffic Demand in NGSO Satellite Communication Systems

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IEEE TRANSACTIONS ON VEHICULAR technology 2025年第1期74卷 816-830页

作者： Jia, Huaiqi Wang, Ying Peng, Haixia Li, Wei Beijing Univ Posts & Telecommun State Key Lab Networking & Switching Technol Beijing 100876 Peoples R China Xi An Jiao Tong Univ Sch Informat & Commun Engn Xian 710049 Peoples R China State Radio Monitoring Ctr Beijing 100037 Peoples R China

In this paper, we investigate a dynamic beam hopping and resource allocation problem in non-geostationary orbit (NGSO) satellite communication systems for terrestrial cells with non-uniform traffic demands. To obtain an efficient beam hopping and resource allocation policy for accommodating the stochastic traffic data arrival and the dynamic topology of NGSO satellites, we first formulate a stochastic optimization problem with the objective of minimizing the long-term system cost given the queue stability constraints. Due to the complicated coupling among decisions and the lack of future network state information, the formulated stochastic optimization problem cannot be directly solved. Therefore, we leverage Lyapunov optimization to transform the stochastic optimization problem into a deterministic one to minimize the system cost and the capacity-demand gap. Moreover, an online beam hopping and resource allocation algorithm is proposed to solve the deterministic problem, and thereby dynamically determining the beam hopping, bandwidth allocation, and power control solutions. The theoretical analysis shows that there is an [O(1/V), O (V)] trade-off between the system cost and the system data backlog with control parameter V . The simulation results demonstrate that the proposed algorithm can effectively match the non-uniform demands. Compared with the benchmarks, the proposed algorithm can effectively reduce the backlog of data queues and the energy consumption of satellites.

关键词： Resource management Optimization Satellites Heuristic algorithms Satellite communications Vehicle dynamics Satellite broadcasting Beam hopping resource allocation NGSO satellite communications Lyapunov optimization

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Secure incentive mechanism for energy trading in computing force networks enabled internet of vehicles: a contract theory approach

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JOURNAL OF SUPERCOMPUTING 2024年第18期80卷 26061-26087页

作者： Wen, Wen Lu, Lu Xie, Renchao Tang, Qinqin Fu, Yuexia Huang, Tao Beijing Univ Posts & Telecommun State Key Lab Networking & Switching Technol Beijing 100876 Peoples R China China Mobile Res Inst Beijing 100053 Peoples R China Purple Mt Labs Nanjing 211111 Peoples R China

The integration of generative artificial intelligence (GAI) and internet of vehicles (IoV) will transform vehicular intelligence from conventional analytical intelligence to service-specific generative intelligence, enhancing vehicular services. In this context, computing force networks (CFNs), capable of flexibly scheduling widespread, multi-domain, multi-layer, and distributed resources, can cater to the demands of the IoV for ultra-high-density computing power and ultra-low latency. In CFNs, the integration of GAI and IoV consumes enormous energy, and GAI servers need to purchase energy from energy suppliers (ESs). However, the information asymmetry between GAI servers and ESs makes it difficult to price energy fairly and distributed ESs and GAI servers constitute a complex trading environment where malicious ESs may intentionally provide low-quality services. In this paper, to facilitate efficient and secure energy trading, and supply for ubiquitous AIGC services, we initially introduce an innovative CFNs-based GAI energy trading system architecture;present an energy consumption model for AIGC services, cost model of ESs, and reputation evaluation model of ESs;and obtain utility functions of GAI servers and ESs based on contract theory. Then, we propose a secure incentive mechanism in IoV, including designing an optimal contract scheme based on contract feasibility conditions and a safety guarantee mechanism based on blockchain. Simulation results demonstrate the feasibility and superiority of our energy trading mechanism.

关键词： Computing force networks Internet of vehicles Generative artificial intelligence Contract theory Energy trading Reputation evaluation

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Quantum algorithms for anomaly detection using amplitude estimation

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PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS 2022年第0期604卷

作者： Guo, Mingchao Liu, Hailing Li, Yongmei Li, Wenmin Gao, Fei Qin, Sujuan Wen, Qiaoyan Beijing Univ Posts & Telecommun State Key Lab Networking & Switching Technol Beijing 100876 Peoples R China State Key Lab Cryptol POB 5159 Beijing 100878 Peoples R China

Anomaly detection, as an important branch of machine learning, plays a critical role in fraud detection, health care, intrusion detection, military surveillance, etc. An anomaly detection algorithm based on density estimation (called ADDE algorithm) is one of the widely used algorithms. However, the ADDE algorithm is computationally expensive when processing big data sets. To solve this problem, in this paper, we propose an efficient quantum ADDE algorithm based on amplitude estimation. It is shown that our algorithm achieves exponential speedup on the number of training data points M over its classical counterpart. Besides, the idea of our algorithm can be applied to accelerate the anomaly detection algorithm based on kernel principal component analysis (called ADKPCA algorithm), which also has a wide range of applications. Our algorithm shows exponential speedup on M compared with its classical counterpart. (C) 2022 Elsevier B.V. All rights reserved.

关键词： Anomaly detection Machine learning Quantum algorithm Exponential speedup

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