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User-Centric Multi-Static Sensing for Joint User and Target Tracking in Mobile Wireless Systems

IEEE Transactions on Wireless Communications 2025年

作者： Hu, Boyang Tian, Hui Ni, Wei Fan, Shaoshuai Hossain, Ekram Beijing University of Posts and Telecommunications State Key Laboratory of Networking and Switching Technology Beijing100876 China University of Technology Faculty of Engineering and Information Technology SydneyNSW2007 Australia University of Manitoba Department of Electrical and Computer Engineering WinnipegMBR3T 2N2 Canada

This paper presents a novel user-centric sensing framework, where a user equipment (UE) acts as the receiver of a multi-static radar sensing system and utilizes the communication signals emitted by base stations (BSs) and scattered by the targets for joint UE and target tracking. Specifically, we propose to locate the UE using the least squares (LS) estimator with a one-dimensional (1D) search and then develop a two-dimensional (2D) target identification approach using the estimated target location and motion of each path based on mean-shift clustering. After the motion parameters of the UE and targets are estimated, a joint UE and target tracking algorithm is designed based on the analysis of the localization and motion estimation errors. Extensive simulations corroborate the ability of our approach to estimate target parameters and cluster and identify targets. Specifically, the average estimation error of the target number is only 0.18. The speed and heading accuracy of the UE and targets is [0.121 m/s, 3.879°] and [0.199 m/s, 4.669°], respectively. In joint UE and target tracking, our scheme outperforms the benchmarks of the extended Kalman filter (EKF) and belief propagation (BP) by at least 33.16% and 10.29%, respectively, even though the EKF and BP require a-priori knowledge of the motion parameters and target identification. © 2002-2012 IEEE.

关键词： Aircraft detection

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GAPLG: Graph Augmented With Pseudolabels Generation for Blockchain Anomaly Transaction Detection

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IEEE Transactions on Computational Social Systems 2025年

作者： Huang, Jing Bu, Kuijian Han, Honggui Gong, Bei Xiong, Ao Wang, Wei Wu, Qihui Beijing University of Technology Faculty of Information Technology Beijing100124 China Beijing University of Posts and Telecommunications State Key Laboratory of Networking Beijing100876 China Beijing University of Posts and Telecommunications Switching Technology Beijing100876 China Nanjing University of Aeronautics and Astronautics College of Electronic and Information Engineering Nanjing211106 China

Cryptocurrencies, underpinned by blockchain technology, face persistent threats such as money laundering and extortion due to their decentralized and anonymous nature. Detecting fraudulent transactions is crucial for ensuring the security of block-chain systems. However, the existing detection methods face the following challenges: lack of labeled data, severe class imbalance in labeled data, complex network structure, numerous parameters, and long training time. To address these challenges, we propose a novel semisupervised learning framework that combines the graph augmented with pseudolabels generation (GAPLG) model and post-processing technique. Our framework employs graph learning networks to elucidate relationships between transactions and users. By utilizing pseudolabels for unlabeled transaction data and embedding them onto diverse graph nodes, we achieve precise labels, enhancing prediction accuracy. Additionally, we employ specific post-processing technique, such as correction and smoothing (C&S) technology, to rectify residuals and refine labels, ensuring our framework rivals the best parameter and baseline models. Our method boasts high scalability and flexibility, aiding in optimizing various evaluation indicators. Experimental verification through multiple real transaction datasets under varying data segmentations, demonstrated its effectiveness when compared with other representative frameworks. The analysis validates the effectiveness and benefits of our method. © 2014 IEEE.

关键词： Adversarial machine learning

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Low Latency and Accuracy-Guaranteed DNN Inference for UAV-Assisted IoT Networks

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IEEE Transactions on Cognitive Communications and networking 2025年

作者： Xu, Jiaqi Yao, Haipeng Zhang, Ru Mai, Tianle Guizani, Mohsen Beijing University of Posts and Telecommunications School of Science China Beijing University of Posts and Telecommunications State Key Laboratory of Networking and Switching Technology China Beijing Institute of Technology School of Information and Electronics China Mohamed Bin Zayed University of Artificial Intelligence Department of Machine Learning United Arab Emirates

In recent years, unmanned aerial vehicles (UAVs) have shown great potential in various Internet of Things (IoT) applications, such as monitoring, inspection, and data collection. Especially, by integrating deep neural network (DNN) technology, UAV networks can provide real-time data insights, enhancing decision-making processes and ultimately boosting industrial productivity while saving costs. In this context, the environmental monitoring data collected by task nodes requires further inference, thereby generating a continuous stream of inference tasks. These tasks should be scheduled and offloaded to UAVs equipped with DNN models, i.e., inference nodes, for further real-time inference. However, the deployment of DNN models requires substantial computing resources, posing challenges for resource-constrained UAV networks. Fortunately, the knowledge distillation (KD) technique offers an effective solution by deriving lightweight models that maintain high accuracy. In this way, the UAV network can request and deploy the proper lightweight model for DNN inference. To attain efficient DNN inference, the inference task assignment, DNN model selection, and bandwidth allocation are coordinately considered to minimize the average inference delay while satisfying the strict accuracy requirements of inference tasks. In this paper, we formulate this problem as a Constrained Markov Decision Process (CMDP) and propose a Constrained Deep Reinforcement Learning based Task Allocation and Resource Management (CDRL-TR) algorithm to dynamically obtain solutions. Furthermore, a resource allocation optimization subroutine is embedded in the proposed algorithm to accelerate the training process. Extensive simulations demonstrate that the CDRL-TR algorithm has satisfactory performance in terms of task accuracy guarantee and inference delay optimization. © 2015 IEEE.

关键词： Decision making

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Semantics-Empowered Non-orthogonal Multiple Access for Downlink Transmission of Correlated Information Sources

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IEEE Transactions on Wireless Communications 2025年

作者： Li, Weizhi Liu, Yucheng Dong, Chen Xu, Xiaodong Zhang, Ping Li, Lin Beijing University of Posts and Telecommunications State Key Laboratory of Networking and Switching Technology Beijing100876 China Peng Cheng Laboratory Department of Broadband Communication Shenzhen China MIGU Co. Ltd Beijing China

In this paper, we introduce an end-to-end non-orthogonal multiple access (NOMA) framework for the downlink transmission of correlated information sources in the multi-user scenario, in which the data required or transmitted by multiple users share similar content. To enhance the end-to-end transmission performance, we resort to the semantic communication paradigm and build our system based on the deep joint source-channel coding (D-JSCC) scheme. Inspired by Wyner's common information, an information theoretical concept, the common information (CI) extraction is proposed to capture the correlation between multiple users effectively. By relaxing the constraint of the object function, equivalency can be established between common information extraction and mutual information maximization. Thereby, the Jenson-Shannon divergence (JSD) is adopted in the loss function for learning the common information representation (CIR). In order to categorize the theoretical performance limit of the proposed system, semantic synonymous mapping (SSM) based information theory is applied for analyzing the effect of correlation level and different decoding schemes on the achievable channel capacity. Specifically, the analytical expression of channel capacity under additive white Gaussian noise (AWGN) and Rayleigh channel is derived and verified by Monte-Carlo experiments. By conducting simulations on three different image datasets, it is verified that our proposed scheme can outperform a series of other state-of-the-art (SoTA) multiple access or distributed source coding (DSC) schemes under up to seven user scenarios. Besides, the visualization and ablation study results validate the effectiveness of the common information extraction. © 2002-2012 IEEE.

关键词： Decoding

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A Survey of Zero-Knowledge Proof Based Verifiable Machine Learning

arXiv

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arXiv 2025年

作者： Peng, Zhizhi Wang, Taotao Zhao, Chonghe Liao, Guofu Lin, Zibin Liu, Yifeng Cao, Bin Shi, Long Yang, Qing Zhang, Shengli College of Electronics and Information Engineering Shenzhen University Guangdong Province Shenzhen China State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications China School of Electronic and Optical Engineering Nanjing University of Science and Technology Nanjing210094 China

As machine learning technologies advance rapidly across various domains, concerns over data privacy and model security have grown significantly. These challenges are particularly pronounced when models are trained and deployed on cloud platforms or third-party servers due to the computational resource limitations of users’ end devices. In response, zero-knowledge proof (ZKP) technology has emerged as a promising solution, enabling effective validation of model performance and authenticity in both training and inference processes without disclosing sensitive data. Thus, ZKP ensures the verifiability and security of machine learning models, making it a valuable tool for privacy-preserving AI. Although some research has explored the verifiable machine learning solutions that exploit ZKP, a comprehensive survey and summary of these efforts remain absent. This survey paper aims to bridge this gap by reviewing and analyzing all the existing Zero-Knowledge Machine Learning (ZKML) research from June 2017 to December 2024. We begin by introducing the concept of ZKML and outlining its ZKP algorithmic setups under three key categories: verifiable training, verifiable inference, and verifiable testing. Next, we provide a comprehensive categorization of existing ZKML research within these categories and analyze the works in detail. Furthermore, we explore the implementation challenges faced in this field and discuss the improvement works to address these obstacles. Additionally, we highlight several commercial applications of ZKML technology. Finally, we propose promising directions for future advancements in this domain. Copyright © 2025, The Authors. All rights reserved.

关键词： Adversarial machine learning

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MAPSM: Mobility Aware Proactive Service Migration Framework for Mobile Edge Computing in Consumer Internet of Vehicles

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IEEE Transactions on Consumer Electronics 2025年

作者： Zhao, Xuhui Shi, Yan Chen, Shanzhi Liu, Jianghui Ji, Baofeng Mumtaz, Shahid Henan University of Science and Technology Luoyang471023 China Beijing University of Posts and Telecommunications Beijing China Beijing University of Posts and Telecommunications State Key Laboratory of Networking and Switching Technology Beijing China China Academy of Telecommunication Technology National Engineering Research Center of Mobile Communications and Vehicular Networks State Key Laboratory of Wireless Mobile Communications Beijing China Department of Applied Informatics Silesian University of Technology Akademicka16 Gliwice44-100 Poland Department of Engineering Nottingham Trent University United Kingdom

Mobile edge computing is considered as a key technology for consumer Internet of Vehicles networks, which provides low-latency, high-reliability network services for end-users. Service migration need to address where to migrate and how to implement service migration procedure based on user mobility. The existing reactive migrating solutions lead to overlong service migration time and end to end latency. The proactive service migration method obtains the target server in advance through mobility prediction, and the service migration procedure starts before communication handover. Based on the above observation, a Mobility Aware Proactive edge Service Migration framework (MAPSM) is proposed in this paper. MAPSM includes the key aspects: 1) predicting the next location of an end user based on ensemble learning method combining recurrent neural networks and geographical embedding markov chain predictors;2) using the mobility prediction result to determine the target edge server of service migration, a proactive migration-handover coordinated method is proposed by performing container pre-migration, memory state migration and communication handover. The time planning scheme in the procedure is also designed. Experimental results demonstrate that MAPSM can greatly improve migration performance, effectively reduce end-to-end latency and significantly reduce service migration time. MAPSM outperforms other baseline service migration approaches. © 1975-2011 IEEE.

关键词： Mobile edge computing

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An improved certificateless signcryption in the standard model

引用

International Journal of Network Security 2015年第5期17卷 597-606页

作者： Cheng, Lin Wen, Qiaoyan State Key Laboratory of Networking and Switch Technology Beijing University of Posts and Telecommunications Beijing100876 China

Signcryption is a cryptographic primitive which can offer simultaneously security requirements of confidentiality and authentication, and is more efficient than the traditional sign-then-encrypt way. Recently, Liu et al. proposed the first certificateless signcryption scheme in the standard model. However, their scheme is proved to have some security weaknesses. In this paper, we propose a corrected version of Liu et al.'s scheme and prove our scheme is indistinguishable against adaptive chosen ciphertext attacks and is existentially unforgeable against chosen message attacks in the standard model. Performance analysis shows the new scheme has smaller public parameter size than the previous certificateless signcryption schemes without using the random oracles.

关键词： Cryptography

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Towards Agentic AI networking in 6G: A Generative Foundation Model-as-Agent Approach

arXiv

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arXiv 2025年

作者： Xiao, Yong Shi, Guangming Zhang, Ping School of Electronic Information and Communications The Huazhong University of Science and Technology Wuhan430074 China Peng Cheng Laboratory Shenzhen China Guangzhou China Peng Cheng Laboratory Shenzhen518055 China School of Artificial Intelligence The Xidian University China State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing100876 China

The promising potential of AI and network convergence in improving networking performance and enabling new service capabilities has recently attracted significant interest. Existing network AI solutions, while powerful, are mainly built based on the close-loop and passive learning framework, resulting in major limitations in autonomous solution finding and dynamic environmental adaptation. Agentic AI has recently been introduced as a promising solution to address the above limitations and pave the way for true generally intelligent and beneficial AI systems. The key idea is to create a networking ecosystem to support a diverse range of autonomous and embodied AI agents in fulfilling their goals. In this paper, we focus on the novel challenges and requirements of agentic AI networking. We propose AgentNet, a novel framework for supporting interaction, collaborative learning, and knowledge transfer among AI agents. We introduce a general architectural framework of AgentNet and then propose a generative foundation model (GFM)-based implementation in which multiple GFM-as-agents have been created as an interactive knowledge-base to bootstrap the development of embodied AI agents according to different task requirements and environmental features. We consider two application scenarios, digital-twin-based industrial automation and metaverse-based infotainment system, to describe how to apply AgentNet for supporting efficient task-driven collaboration and interaction among AI agents. Copyright © 2025, The Authors. All rights reserved.

关键词： Generative adversarial networks

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Secure and trusted sharing mechanism of private data for Internet of Things

引用

High-Confidence Computing 2025年第2期5卷

作者： Li, Mengyuan Guo, Shaoyong Li, Wenjing Xiong, Ao Zhou, Xiaoming Qi, Jun Qi, Feng Wang, Dong Li, Da State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing100876 China State Grid Liaoning Electric Power Supply Company Limited Shenyang110004 China State Grid Liaoning Electric Power Supply Co. LTD. Information & Telecommunication Branch Shenyang110004 China State Grid Digital Technology Holding Co. LTD Beijing100876 China

In recent years, the rapid development of Internet of Things (IoT) technology has led to a significant increase in the amount of data stored in the cloud. However, traditional IoT systems rely primarily on cloud data centers for information storage and user access control services. This practice creates the risk of privacy breaches on IoT data sharing platforms, including issues such as data tampering and data breaches. To address these concerns, blockchain technology, with its inherent properties such as tamper-proof and decentralization, has emerged as a promising solution that enables trusted sharing of IoT data. Still, there are challenges to implementing encrypted data search in this context. This paper proposes a novel searchable attribute cryptographic access control mechanism that facilitates trusted cloud data sharing. Users can use keywords To efficiently search for specific data and decrypt content keys when their properties are consistent with access policies. In this way, cloud service providers will not be able to access any data privacy-related information, ensuring the security and trustworthiness of data sharing, as well as the protection of user data privacy. Our simulation results show that our approach outperforms existing studies in terms of time overhead. Compared to traditional access control schemes,our approach reduces data encryption time by 33%, decryption time by 5%, and search time by 75%. © 2024 The Author(s)

关键词： Authentication

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Multistatic Cooperative Sensing Assisted Secure Transmission via IRS

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IEEE Transactions on Wireless Communications 2025年

作者： Yu, Xianglin Xu, Jinlei Qin, Xiaoqi Tang, Jie Zhao, Nan Niyato, Dusit Dalian University of Technology School of Information and Communication Engineering Dalian116024 China Beijing University of Posts and Telecommunications State Key Laboratory of Networking and Switching Technology Beijing100876 China South China University of Technology School of Electronic and Information Engineering Guangzhou510641 China Nanyang Technological University College of Computing and Data Science 639798 Singapore

Benefiting from the performance enhancement brought by multistatic cooperative sensing, integrated sensing and communication (ISAC) can capture more environmental information to address the security risk of information leakage caused by the openness of wireless channels. In this paper, we study the multistatic cooperative sensing assisted secure transmission via intelligent reflecting surface (IRS). In particular, we propose a multistatic cooperative sensing scheme to obtain the angle of arrival and the localization of the eavesdropping target to achieve the beam alignment accurately. The goal is to maximize the sum secrecy rate by jointly optimizing the association variables of base station (BS) and users, the BS beamforming and the IRS phase shifts, subject to the requirement of target sensing. Due to the coupling of variables and non-convex objective function, the formulated problem is intractable to solve directly. As such, we decompose it into three subproblems and develop an alternative optimization algorithm to solve them iteratively. The association variables are first optimized by successive convex approximation. Then, the BS transmit beamforming can be derived via the semidefinite relaxation. Finally, we adopt an alternating direction method of multiplier for the IRS phase-shift design. Simulation results indicate the feasibility of the proposed scheme, and the multistatic cooperative sensing via IRS can enhance the sensing performance and guarantee the secure transmission. © 2002-2012 IEEE.

关键词： Beamforming

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