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19th IEEE International Wireless Communications and Mobile Computing (IEEE IWCMC)

作者： Liu, Jingtian Wu, Zhonghui Li, Zhuoyuan Li, Menglin Yang, Shujie Xiao, Han Xu, Changqiao Beijing Univ Posts & Telecommun State Key Lab Networking & Switching Technol Beijing 100876 Peoples R China

ISBN: (纸本)9798350333398

Recently, blockchain-based covert communication has gained momentum, for the decentralization, anonymity, and immutability feature of blockchain. Nevertheless, some challenges impair its security and efficiency. Most schemes have a weak generalization ability, and can merely be applied to a specific blockchain platform. Storage-based covert transmission schemes usually have limited space for data embedding, affecting their Information delivery efficiency. Besides, static data sifting rules raise the risk of information leakage. In this paper, we design DLCCB(Dynamic labeling based Covert Communication on Blockchain). We first split the information to be delivered into several pieces and utilize the destination address of each transaction to embed them. Then a dynamic labeling method is proposed for updating sifting rules without extra negotiation between sender and receiver. Besides, we design two kinds of sifting algorithms, namely online and offline sifting algorithm. We perform our solution on Ropsten, a test net of Ethereum. The experiment result verifies the feasibility of our scheme.

关键词： Covert communication Blockchain Sifting algorithm

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Secure Blockchain-Enabled Internet of Vehicles Scheme with Privacy Protection

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Computers, Materials & Continua 2023年第6期75卷 6185-6199页

作者： Jiansheng Zhang Yang Xin Yuyan Wang Xiaohui Lei Yixian Yang Information Security Center State Key Laboratory of Networking and Switching TechnologyBeijing University of Posts and TelecommunicationsBeijing100876China Beijing Everyone Crowdsourcing Technology Company Ltd. Beijing100018China

The car-hailing platform based on Internet of Vehicles(IoV)tech-nology greatly facilitates passengers’daily car-hailing,enabling drivers to obtain orders more efficiently and obtain more significant ***,to match the driver closest to the passenger,it is often necessary to process the location information of the passenger and driver,which poses a considerable threat to privacy disclosure to the passenger and *** these issues,in this paper,by combining blockchain and Paillier homomorphic encryption algorithm,we design a secure blockchain-enabled IoV scheme with privacy protection for online *** this scheme,firstly,we propose an encryp-tion scheme based on the ***,the location information of passengers and drivers is encrypted in this ***,by introducing Paillier homomorphic encryption algorithm,the location matching of passengers and drivers is carried out in the ciphertext state to protect their location *** last,blockchain technology is used to record the transactions in online car-hailing,which can provide a security guarantee for passengers and *** we further analyze the security and performance of this *** with other schemes,the experimental results show that the proposed scheme can protect the user’s location privacy and have a better performance.

关键词： Blockchain IoV privacy protection anti-quantum

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Efficient VNF-FG Scaling Algorithm for 5G Network Slices

Efficient VNF-FG Scaling Algorithm for 5G Network Slices

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2023 IEEE International Symposium on Broadband Multimedia Systems and Broadcasting, BMSB 2023

作者： Chai, Xuqing Wang, Ying Zhang, Manjun Cong, Lin Beijing University of Posts and Telecommunications State Key Laboratory of Networking and Switching Technology Beijing China

ISBN: (纸本)9798350321524

While meeting the needs of network traffic, dynamic deployment of VNF is the key to cost optimization of NFV system. In addition to considering server resource capacity and dynamic user traffic, the dynamic expansion strategy also needs to strike a balance between the operating costs of VNF, the cost of maintaining VNF instances, the cost of VNF deployment, and the additional cost of servers placing new VNF instances. In this paper, the problem is modeled as a 0-1 integer linear programming model with quadratic terms, and a new linear programming method is used to solve the problem. The algorithm reduces the complexity of the algorithm to a great extent by reducing the number of variables in the constraint. Through the experimental simulation, we prove the effectiveness of the algorithm and reduce the cost of VNF-FG scaling. © 2023 IEEE.

关键词： Quadratic programming

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REWAFL: Residual Energy and Wireless Aware Participant Selection for Efficient Federated Learning Over Mobile Devices

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IEEE TRANSACTIONS ON MOBILE COMPUTING 2024年第10期23卷 9487-9501页

作者： Li, Yixuan Qin, Xiaoqi Geng, Jiaxiang Chen, Rui Hou, Yanzhao Gong, Yanmin Pan, Miao Zhang, Ping Beijing Univ Posts & Telecommun State Key Lab Networking & Switching Technol Beijing 100876 Peoples R China Univ Houston Dept Elect & Comp Engn Houston TX 77204 USA Univ Texas San Antonio Dept Elect & Comp Engn San Antonio TX 78249 USA Beijing Univ Posts & Telecommun State Key Lab Networking & Switching Technol Beijing 100876 Peoples R China Peng Cheng Lab Dept Broadband Commun Shenzhen 518066 Peoples R China

Participant selection (PS) helps to accelerate federated learning (FL) convergence, which is essential for the practical deployment of FL over mobile devices. While most existing PS approaches focus on improving training accuracy and efficiency rather than residual energy of mobile devices, which fundamentally determines whether the selected devices can participate. Meanwhile, the impacts of mobile devices heterogeneous wireless transmission rates on PS and FL training efficiency are largely ignored. Moreover, PS causes the staleness issue. Prior research exploits isolated functions to force long-neglected devices to participate, which is decoupled from original PS designs. In this paper, we propose a residual energy and wireless aware PS design for efficient FL training over mobile devices (REWAFL). REWAFL introduces a novel PS utility function that jointly considers global FL training utilities and local energy utility, which integrates energy consumption and residual battery energy of candidate mobile devices. Under the proposed PS utility function framework, REWAFL further presents a residual energy and wireless aware local computing policy. Besides, REWAFL buries the staleness solution into its utility function and local computing policy. The experimental results show that REWAFL is effective in improving training accuracy and efficiency, while avoiding flat battery of mobile devices.

关键词： Training Mobile handsets Wireless communication Computational modeling Servers Energy consumption Batteries Federated learning mobile devices participant selection residual energy awareness

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TRCT: A Traceable Anonymous Transaction Protocol for Blockchain

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IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY 2023年 18卷 4391-4405页

作者： Duan, Junke Wang, Licheng Wang, Wei Gu, Lize Beijing Univ Posts & Telecommun State Key Lab Networking & Switching Technol Beijing 100876 Peoples R China Beijing Inst Technol Beijing 100081 Peoples R China

Anonymous cryptocurrencies are distributed applications based on blockchain technology that enhance the anonymity of user transactions. However, they also provide cover for many criminal activities. Existing traceable modifications of anonymous transaction protocols do not support public verification of the traceability of transactions, meaning dishonest users can still evade tracing by forging partial proofs. In this paper, we first propose EPoK, which is a partially extractable zero-knowledge proof scheme designed to address this issue. We provide the instantiation of EPoK, as well as its formal security proofs. Combined with EPoK and the classic anonymous transaction protocol RingCT, we propose a traceable anonymous transaction protocol TRCT for tracing participants' addresses and transaction amounts in anonymous transactions. Compared to previous works, TRCT is the first to implement public verification of the transaction's traceability while still maintaining anonymity. This ensures that users cannot forge relevant proofs to evade tracing. We present and prove the security properties of TRCT, and experimentally compare its efficiency with the original RingCT protocol. The result shows that the extra overhead generated by TRCT is small.

关键词： Blockchain cryptocurrency anonymity RingCT traceability

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Channel Estimation for Intelligent Reflecting Surface Assisted Massive MIMO Systems-A Deep Learning Approach

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IEEE COMMUNICATIONS LETTERS 2022年第4期26卷 798-802页

作者： Mao, Zhendong Liu, Xiqing Peng, Mugen Beijing Univ Posts & Telecommun State Key Lab Networking & Switching Technol Beijing 100876 Peoples R China

The benefits of intelligent reflecting surfaces (IRS) assisted massive multiple-input multiple-output systems are based on the accurate acquisition of channel state information but at the cost of the high pilot overhead. In this work, the sparse structure in angular domain is revealed, and then the uplink cascaded channel estimation is converted into a compressive sensing (CS) problem. However, the angle of arrival and angle of departure are fundamentally continuous values, and thus they usually do not fall precisely on the discrete grids, resulting in grid mismatch. In this case, the typical CS solution usually yields the compromised reconstruction performance. To address this issue, we proposed a deep learning-based approach with the traditional orthogonal matching pursuit followed by the residual network to improve the performance. Furthermore, a straightforward network structure is proposed to reduce computational complexity. Simulation results demonstrate that the proposed solutions achieve a better estimation performance and require lower pilot overhead compared with the state-of-the-art ones.

关键词： Channel estimation Matching pursuit algorithms Antennas Uplink Massive MIMO Manganese Estimation Intelligent reflecting surface channel estimation deep learning compressive sensing

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Dynamic and Static Representation Learning Network for Recommendation

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IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2024年第1期35卷 831-841页

作者： Liu, Tongcun Lou, Siyuan Liao, Jianxin Feng, Hailin Zhejiang A&F Univ Sch Math & Comp Sci Hangzhou 311300 Peoples R China Beijing Univ Posts & Telecommun State Key Lab Networking & Switching Technol Beijing 100876 Peoples R China

Existing review-based recommendation methods learn a latent representation of user and item from user-generated reviews by a static strategy, which are unable to capture the dynamic evolution of users' interests and the dynamic attraction of items. Here, we propose a dynamic and static representation learning network (DSRLN) to improve the rating prediction accuracy by exploring fine-grained representations of users and items. Specifically, we built DSRLN with a dynamic representation extractor to model the dynamic evolution of users' interests by exploring the inner relations of an interaction sequence, and with a static representation extractor to model the users' intrinsic preferences by learning the semantic coherence and feature strength information from reviews. To identify the different influences of dynamic and static features for different users, a personalized adaptive fusion module was designed using a weighted attention mechanism. Extensive experiments on five real-world datasets from Amazon demonstrated the superiority of the proposed model, and the additional ablation studies verified the effectiveness of the components designed in the DSRLN model.

关键词： Adaptation models Semantics Representation learning Recommender systems Feature extraction Data models Data mining Adaptive fusion strategy attention mechanism recommender system representation learning

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Efficient Adaptive Federated Learning in Resource-Constrained IoT Environments

Efficient Adaptive Federated Learning in Resource-Constraine...

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IEEE Conference on Global Communications (IEEE GLOBECOM) - Intelligent Communications for Shared Prosperity

作者： Chen, Zunming Cui, Hongyan Luan, Qiuji Xi, Yu Beijing Univ Posts & Telecommun State Key Lab Networking & Switching Technol Beijing 100876 Peoples R China

ISBN: (纸本)9798350310900

Federated Learning (FL) has emerged as a privacy-preserving distributed learning framework which enables IoT devices to collaboratively train machine learning models via sharing model parameters. However, inefficiency due to frequent parameters transmissions significantly reduces FL performance. Existing acceleration algorithms for speeding up FL training consist of two main types including local update and parameter compression which consider the trade-offs between communication and computation/precision respectively. Jointly considering these two trade-offs and adaptively balancing their impacts on convergence have remained unresolved. To solve the problem, we propose an efficient adaptive federated optimization (EAFO) algorithm to improve the efficiency of FL in resource-constrained IoT environments, which minimizes the learning error by the joint consideration of two variables consisting of the local update and parameter compression. The EAFO enables FL to adaptively adjust two variables and balance trade-offs among computation, communication, and precision. The experiment results illustrate the high effectiveness of the proposed EAFO algorithm, which can achieve higher accuracies faster compared with the state-of-the-art algorithms.

关键词： IoT Federated Learning Distributed Artificial Intelligence Communication Trade-offs

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Stochastic programming based multi-arm bandit offloading strategy for internet of things

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Digital Communications and Networks 2023年第5期9卷 1200-1211页

作者： Bin Cao Tingyong Wu Xiang Bai State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing100876China National Key Laboratory of Science and Technology on Communications University of Electronic Science and Technology of ChinaChengdu611731China China Electrics Technology Cyber Security Co..Ldt Chengdu611731China

In order to solve the high latency of traditional cloud computing and the processing capacity limitation of Internet of Things(IoT)users,Multi-access Edge Computing(MEC)migrates computing and storage capabilities from the remote data center to the edge of network,providing users with computation services quickly and *** this paper,we investigate the impact of the randomness caused by the movement of the IoT user on decision-making for offloading,where the connection between the IoT user and the MEC servers is *** uncertainty would be the main obstacle to assign the task ***,if the assigned task cannot match well with the real connection time,a migration(connection time is not enough to process)would be *** order to address the impact of this uncertainty,we formulate the offloading decision as an optimization problem considering the transmission,computation and *** the help of Stochastic Programming(SP),we use the posteriori recourse to compensate for inaccurate ***,in heterogeneous networks,considering multiple candidate MEC servers could be selected simultaneously due to overlapping,we also introduce the Multi-Arm Bandit(MAB)theory for MEC *** extensive simulations validate the improvement and effectiveness of the proposed SP-based Multi-arm bandit Method(SMM)for offloading in terms of reward,cost,energy consumption and *** results showthat SMMcan achieve about 20%improvement compared with the traditional offloading method that does not consider the randomness,and it also outperforms the existing SP/MAB based method for offloading.

关键词： Multi-access computing Internet of things Offloading Stochastic programming Multi-arm bandit

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Modeling and Performance Analysis of Multiserver Cloud Database Over Quasi-Static Rayleigh Fading Channel

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IEEE INTERNET OF THINGS JOURNAL 2024年第11期11卷 20314-20326页

作者： Chen, Mengying Liu, Yang Dong, Chen An, Wannian Xu, Xiaodong Zhang, Ping Beijing Univ Posts & Telecommun Sch Informat & Commun Engn Beijing 100876 Peoples R China Beijing Univ Posts & Telecommun State Key Lab Networking & Switching Technol Beijing 100876 Peoples R China

With the development of communication in the post-5G era, the combination of communication and cloud computing becomes closer. In order to promote the further development of cloud-network, this article will study the performance of Multiserver cloud Database under the Communication quality of quasi-static Rayleigh fading channel with multiple antennas (MC-MD). The CLIENTS with unlimited customers, a COMMUNICATION SYSTEM subject to quasi-static Rayleigh fading, and CLOUD DATABASE with two-phase locking protocol are the three components of the MC-MD model. Transactions are 1)initiated by the CLIENTS, 2)transmitted to the CLOUD DATABASE through the COMMUNICATION SYSTEM for processing, 3)then returned to the CLIENTS. The indicators of the model is mathematically derived by using queuing theory. These include client's indicators [average concurrent quantity of the system in steady state (CQ), average transactions stay time of the system in steady state (ST), average queue length of the waiting area in steady state (QL), and average transactions wait time of the waiting area in steady state (WT)] and server's indicator [average number of service desks in the busy period at steady state (DN)]. Under the appropriate conditions, the results indicate that the theoretical value of service performance is basically consistent with the simulation value. Clearly, the high speed improves the service performance of the system and decreases the service pressure. On the basis of this, the optimization strategy is proposed and the simulation indicators Jitter of transactions sojourn time of the system in steady state (STJ) is added. The results show that the transaction scheduling optimization strategy effectively reduce the delay and its jitter.

关键词： Databases Cloud computing Protocols Distributed databases Analytical models Computational modeling Servers Exclusive lock multiserver cloud database quasi-static Rayleigh fading channel with multiple antennas queue system shared lock

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