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A Review of Pseudonym Change Strategies for Location Privacy Preservation Schemes in Vehicular networks

ACM Computing Surveys 2025年第8期57卷 1-37页

作者： Benarous, Leila Zeadally, Sherali Boudjit, Saadi Mellouk, Abdelhamid Department of Computer science Amar Telidji University of Laghouat Laghouat Algeria LISSI-Tinc-NET Laboratory University of Paris-Est Creteil Créteil France College of Communication and Information University of Kentucky Lexington United States Department of Electronic Engineering Kyung Hee University Seoul Korea Republic of Litis Lab Intelligent Transportation Systems Group University of Rouen Normandy Mont-Saint-Aignan France Lissi Laboratory Department of Networks and Telecom University Paris-Est Créteil Val de Marne Créteil France

Location privacy protection in vehicular networks has been a primary priority to ensure because of its direct impact on human physical safety. Leakage and violation of road users' location privacy may be perilous beyond simple curiosity as it may scale to cyberstalking and tracking. This may lead to road users being subjected to mental and physical distress, from blackmailing to targeted advertising, and trap planning. The results of tracking may be fatal to road users. Over the past 17 years, researchers have been actively investigating location privacy protection mechanisms for vehicular networks. Numerous research results have been published that examined the use of pseudonym change strategies, which have been considered as the most appropriate solution that maintains the tradeoff between ease of application, good protection level, and the correct functionality of the network. We evaluate, classify, and analyze pseudonym change strategies aimed at location privacy preserving solutions for vehicular networks and discuss their performance. We also review the simulation tools used for evaluating pseudonym change techniques used in location privacy preserving solutions for the vehicular environment. The results of this survey will help researchers in this field to develop more robust, efficient, and cost-effective location privacy protection schemes in the future. © 2025 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Differential privacy

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Efficient fragmentation-aware algorithms for multicast traffic in elastic optical networks with a novel demand size based fragmentation metric

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Optical Fiber Technology 2025年 93卷

作者： Asadzadeh, Leila Khonsari, Ahmad Ghaffarpour Rahbar, Akbar School of Electrical and Computer Engineering University of Tehran Tehran 14155-6619 Iran School of Computer Science Institute for Research in Fundamental Sciences Institute for Research in Fundamental Sciences Tehran 19538-33511 Iran Computer Networks Research Lab Faculty of Electrical Engineering Tabriz University of Technology Tabriz Iran

With flexible allocation of spectrum resources and on-demand bandwidth provisioning, Elastic Optical networks (EONs) have made it possible to meet the high volumes of traffic demands. Multicast Routing, Modulation level and Spectrum Assignment (MRMSA) algorithms are important in efficient multicast transmissions in EONs. The dynamic nature of allocating and releasing spectral resources causes the spectrum to be divided into small empty blocks that are not suitable to be allocated anymore, so the resource utilization reduces greatly. Many metrics have been introduced in the literature to measure the amount of fragmented spectrum. In this paper, we propose a novel demand size based fragmentation metric (DemFRAG) to evaluate the suitability of the optical links or paths to establish the requests. We also propose two fragmentation-aware algorithms based on this novel metric to efficiently solve MRMSA problem in EONs. The performance of the proposed fragmentation-aware MRMSA algorithms is evaluated by considering the novel DemFRAG metric and also other fragmentation metrics in terms of Blocking Probability (BP) and Bandwidth Blocking Probability (BBP) under different network topologies. Simulation results demonstrate that the proposed MRMSA algorithms with the DemFRAG metric allow for significant reduction in BP and BBP compared to other algorithms. © 2025

关键词： Elastic optical networks Fragmentation metrics Modulation level and spectrum assignment Multicast routing Multicast transmission

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Correcting Noisy Multilabel Predictions: Modeling label Noise through Latent Space Shifts

arXiv

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arXiv 2025年

作者： Huang, Weipeng Li, Qin Xiao, Yang Qiao, Cheng Cai, Tie Liang, Junwei Hurley, Neil J. Piao, Guangyuan School of Computer Science and Software Engineering Shenzhen Institute of Information Technology Guangdong China State Key Lab of Integrated Service Networks School of Cyber Engineering Xidian University Shaanxi China Cyberspace Institute of Advanced Technology Guangzhou University Guangdong China School of Computer Science University College Dublin Dublin Ireland

Noise in data appears to be inevitable in most realworld machine learning applications and would cause severe overfitting problems. Not only can data features contain noise, but labels are also prone to be noisy due to human input. In this paper, rather than noisy label learning in multiclass classifications, we instead focus on the less explored area of noisy label learning for multilabel classifications. Specifically, we investigate the post-correction of predictions generated from classifiers learned with noisy labels. The reasons are two-fold. Firstly, this approach can directly work with the trained models to save computational resources. Secondly, it could be applied on top of other noisy label correction techniques to achieve further improvements. To handle this problem, we appeal to deep generative approaches that are possible for uncertainty estimation. Our model posits that label noise arises from a stochastic shift in the latent variable, providing a more robust and beneficial means for noisy learning. We develop both unsupervised and semi-supervised learning methods for our model. The extensive empirical study presents solid evidence to that our approach is able to consistently improve the independent models and performs better than a number of existing methods across various noisy label settings. Moreover, a comprehensive empirical analysis of the proposed method is carried out to validate its robustness, including sensitivity analysis and an ablation study, among other elements. © 2025, CC BY.

关键词： Contrastive Learning

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Flexible Routing Algorithm for Digital Intermediate Frequency Interoperability Satellite Systems Using Fair Buffer Queuing

Flexible Routing Algorithm for Digital Intermediate Frequenc...

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International Conference on Computing, Networking and Communications (ICNC)

作者： Walter Roensch Hyuck M. Kwon Amitav Mukherjee Khanh Pham Electrical and Computer Engineering Wichita State University Wichita KS United States Tiami Networks Elk Grove CA United States Air Force Research Lab Kirtland Air Force Base NM United States

ISBN: (数字)9798331520960

ISBN: (纸本)9798331520977

With changes in the Digital Intermediate Frequency Interoperability (DIFI) standard, there is now a standardized way for DIFI systems to communicate various flow control details. These details can be used to increase the reliability of satellite systems in terms of packet loss and delay. We will examine a deterministic routing scenario in which a satellite is used as a relay between two earth stations. To accomplish this goal, we propose a new queuing algorithm for network scheduling called "fair buffer queuing." We will also briefly discuss situations that would allow us to further leverage the changes from the updated flow control mechanisms included in the DIFI 1.2.0.

关键词： Satellites Flow production systems Packet loss Routing Scheduling Reliability Relays Standards Interoperability Frequency control

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Metaverse meets distributed machine learning: A contemporary review on the development with privacy-preserving concerns

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ICT Express 2025年

作者： Gardner, Samuel Harry Hoang, Trong-Minh Na, Woongsoo Dao, Nhu-Ngoc Cho, Sungrae Department of Computer Science and Engineering Sejong University Seoul 05006 South Korea Intelligently Connected Networks Lab Posts and Telecommunications Institute of Technology Hanoi 100000 Viet Nam Division of Computer Science and Engineering Kongju National University Cheonan 31080 South Korea School of Computer Science and Engineering Chung-Ang University Seoul 05006 South Korea

Distributed machine learning utilization in the metaverse exposes many potential benefits. However, the combination of these advanced technologies raises significant privacy concerns due to the potential exploitation of sensitive user and system data. This paper provides a systematic investigation of over 100 recent studies across key academic databases obtained by initial keyword-filter screening followed by a thorough full-text review. Particularly, metaverse evolution and enabling infrastructure technologies are briefly summarized. Subsequently, the distributed learning architectures and their features are analyzed as well as possibly associated vulnerability discussions. Then, envisioned metaverse applications and future research challenges are highlighted before concluding remarks. © 2025 The Authors

关键词： Distributed machine learning Mobile metaverse Privacy preservation Virtual reality

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Meta-Reinforcement Learning for Timely and Energy-efficient Data Collection in Solar-powered UAV-assisted IoT networks

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IEEE Transactions on Communications 2025年

作者： Yi, Mengjie Wang, Xijun Liu, Juan Zhang, Yan Hou, Ronghui School of Cyber Engineering Xidian University Xi’an710071 China School of Electronics and Information Technology Sun Yat-sen University Guangzhou510006 China School of Electrical Engineering and Computer Science Ningbo University Zhejiang315211 China State Key Lab of Integrated Service Networks Information Science Institute Xidian University Shaanxi Xi’an710071 China

Unmanned aerial vehicles (UAVs) have the potential to greatly aid Internet of Things (IoT) networks in mission-critical data collection, thanks to their flexibility and cost-effectiveness. However, challenges arise due to the UAV’s limited onboard energy and the unpredictable status updates from sensor nodes (SNs), which impact the freshness of collected data. In this paper, we investigate the energy-efficient and timely data collection in IoT networks through the use of a solar-powered UAV. Each SN generates status updates at stochastic intervals, while the UAV collects and subsequently transmits these status updates to a central data center. Furthermore, the UAV harnesses solar energy from the environment to maintain its energy level above a predetermined threshold. To minimize both the average age of information (AoI) for SNs and the energy consumption of the UAV, we jointly optimize the UAV trajectory, SN scheduling, and offloading strategy. Then, we formulate this problem as a Markov decision process (MDP) and propose a meta-reinforcement learning algorithm to enhance the generalization capability. Specifically, the compound-action deep reinforcement learning (CADRL) algorithm is proposed to handle the discrete decisions related to SN scheduling and the UAV’s offloading policy, as well as the continuous control of UAV flight. Moreover, we incorporate meta-learning into CADRL to improve the adaptability of the learned policy to new tasks. To validate the effectiveness of our proposed algorithms, we conduct extensive simulations and demonstrate their superiority over other baseline algorithms. ©1972-2012 IEEE.

关键词： Antennas

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Occlusion-Aware Secure Device-to-Device Communication in Social Internet of Things networks

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IEEE Transactions on Consumer Electronics 2025年

作者： Chandra, Saurabh Prateek Arya, Rajeev Sharma, Rohit Almansour, Shahad Gandor, Michal National Institute of Technology Patna Wireless Sensor Networks Lab Department of Electronics and Communication Engineering Bihar Patna 800005 India ABES Engineering College Department of Electronics and Communication Engineering UP Ghaziabad India University of Hail Applied College Saudi Arabia Cracow University of Technology Faculty of Computer Science and Telecommunications Department of Computer Science Warszawska Krakow Poland

The Social Internet of Things (SIoT) framework facilitates the development of various consumer electronic devices such as smartphones, smart wearables, and smart wireless devices, enabling social relationships between electronic devices and humans. Human-centric intelligent Device-to-Device (D2D)-enabled SIoT networks provide essential services like traffic offloading and secure data rate, but face challenges from urban interference caused by obstacles;to mitigate this, we propose a user-driven approach that prioritizes social relationships to enhance network performance. We address occlusion challenges in urban areas by formulating a problem aimed at maximizing throughput and secrecy rate while minimizing power consumption, employing an intelligent resource allocation and spline-based power transmission scheme for efficient utilization. Field tests using Universal Software Radio Peripheral (USRP) and labVIEW have been performed to validate the proposed method. The results show a throughput improvement of 10.93%, and a 27.86% enhancement in secrecy rate compared to other benchmark schemes. The comprehensive adoption of this algorithm can enable secure D2D communication for consumer electronics products driven by social IoT networks. © 1975-2011 IEEE.

关键词： Device-to-Device Communication Quality of Service Resource Allocation Secrecy Rate Social Internet of Things Universal Software Radio Peripheral (USRP)

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Characterizing the thermo-optic coefficient of gallium phosphide-on-insulator platform using high-quality ring resonators

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Applied Physics Letters 2025年第16期126卷

作者： Cheng, Weiren Ding, Ning Zhang, Xucheng Liu, Zhenyu Tang, Xingyu Lin, Wenfu Wang, Yifan Pan, Ziyu Bu, Naiqin You, Mingjian Ji, Xingchen Li, Yi Zhao, Qiancheng School of Microelectronics MOE Engineering Research Center of Integrated Circuits for Next Generation Communications Southern University of Science and Technology Guangdong Shenzhen518000 China John Hopcroft Center for Computer Science School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Shanghai200240 China State Key Lab of Advanced Optical Communication Systems and Networks Department of Electronic Engineering Shanghai Jiao Tong University Shanghai200240 China

Characterizing a material's thermo-optic coefficient lays the foundation for optimizing thermal tuning of photonic integrated devices, a key feature for applications in optical communication, sensing, and signal processing. Unlike traditional bulk measurements, determining the thermo-optic coefficient (TOC) in microscale photonic devices offers significant advantages in data processing and provides more direct relevance to real-world device performance. In this work, we characterize the TOC of gallium phosphide (GaP) films using an air-cladded ring resonator, built on a GaP-on-insulator (GaP-OI) architecture. The resonator is fabricated via an optimized "etch-n-transfer" process, which incorporates silicon dioxide hard masks to enhance the precision of pattern transfer and improve the waveguide surface cleanliness, reducing defects and ensuring better device performance. The fabricated resonator exhibits a loaded quality factor of (2.18 ± 0.1)×104 at 1550 nm by using contact lithography, with a waveguide propagation loss of 23.8 ± 0.3 dB/cm. At 780 nm, the propagation loss decreases to 16.7 dB/cm. The resonator also shows a temperature-dependent wavelength shift of 65.8 pm/K, allowing us to extract a TOC of 1.19 × 10−4/K for GaP. This high temperature sensitivity empowers the GaP-OI platform particularly well-suited for rapid thermal turning, which is beneficial for a range of applications including optical sensing, optical signal processing, and highly efficient nonlinear conversion. © 2025 Author(s).

关键词： Surface cleaning

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Uplink Control Information Based Clustered Access and Discontinuous Reception Management for mMTC

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IEEE Communications Letters 2025年

作者： Wang, Shao-Di Wang, Chang-Long Wang, Hui-Ming Zhou, Feng Leung, Victor C. M. Xidian University Key Laboratory of Electronic Information Countermeasure and Simulation Technology of Ministry of Education Xi’an710071 China Xi’an Jiaotong University School of Information and Communication Engineering Ministry of Education Key Lab for Intelligent Networks and Network Security Xi’an710049 China Shenzhen MSU BIT University Artificial Intelligence Research Institute Shenzhen518172 China Shenzhen University College of Computer Science and Software Engineering Shenzhen518060 China The University of British Columbia Department of Electrical and Computer Engineering VancouverBCV6T 1Z4 Canada

Uplink control information (UCI) and discontinuous reception (DRX) play important roles for massive machine type communication (mMTC). Despite their standalone significance, a conspicuous gap exists in comprehensively exploring the synergy between UCI and DRX concerning both energy conservation and latency reduction for mMTC. In this letter, we propose a UCI-based clustered access and DRX management (UCADM) scheme that strikes a balance between these two critical performance metrics, reducing energy usage while preserving low latency in uplink-downlink interaction for mMTC. Specifically, we present a strictly feasible solution for the proposed UCADM scheme by using the spectral radius and the constraint projection, which can effectively solve DRX switching operations as indicated by scheduling information from UCI. Simulation results confirm the effectiveness of our scheme. © 1997-2012 IEEE.

关键词： Information management

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Neural Network-Assisted Hybrid Model Based Message Passing for Parametric Holographic MIMO Near Field Channel Estimation

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IEEE Transactions on Wireless Communications 2025年

作者： Yuan, Zhengdao Guo, Yabo Gao, Dawei Guo, Qinghua Wang, Zhongyong Huang, Chongwen Jin, Ming Wong, Kai-Kit Henan Open University Artificial Intelligence Technology Engineering Research Center Zhengzhou450002 China University of Wollongong School of Electrical Computer and Telecommunications Engineering WollongongNSW2522 Australia Zhengzhou University School of Information Engineering Zhengzhou450002 China Xidian University Hangzhou Institute of Technology Hangzhou311200 China University of Wollongong School of Electrical Computer and Telecommunications Engineering NSW2522 Australia Zhejiang University College of Information Science and Electronic Engineering Hangzhou310027 China Zhejiang Provincial Key Laboratory of Multi-Modal Communication Networks and Intelligent Information Processing Hangzhou310027 China Ningbo University Faculty of Electrical Engineering and Computer Science Ningbo315211 China University College London Department of Electronic and Electrical Engineering Torrington Place WC1E 7JE United Kingdom Yonsei University Yonsei Frontier Lab Seoul Korea Republic of

Holographic multiple-input and multiple-output (HMIMO) is a promising technology with the potential to achieve high energy and spectral efficiencies, enhance system capacity and diversity, etc. In this work, we address the challenge of HMIMO near field (NF) channel estimation, which is complicated by the intricate model introduced by the dyadic Green's function. Despite its complexity, the channel model is governed by a limited set of parameters. This makes parametric channel estimation highly attractive, offering substantial performance enhancements and enabling the extraction of valuable sensing parameters, such as user locations, which are particularly beneficial in mobile networks. However, the relationship between these parameters and channel gains is nonlinear and compounded by integration, making the estimation a formidable task. To tackle this problem, we propose a novel neural network (NN) assisted hybrid method. With the assistance of NNs, we first develop a novel hybrid channel model with a significantly simplified expression compared to the original one, thereby enabling parametric channel estimation. Using the readily available training data derived from the original channel model, the NNs in the hybrid channel model can be effectively trained offline. Then, building upon this hybrid channel model, we formulate the parametric channel estimation problem with a probabilistic framework and design a factor graph representation for Bayesian estimation. Leveraging the factor graph representation and unitary approximate message passing (UAMP), we develop an effective message passing-based Bayesian channel estimation algorithm. Extensive simulations demonstrate the superior performance of the proposed method. © 2025 IEEE.

关键词： Green's function

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