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A Hybrid-Feature-Based Autoencoder Model for Predicting Traffic Flow to Accelerate Ambulance Medical Response Time in Internet of Vehicles

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IEEE Internet of Things Journal 2025年第9期12卷 11397-11407页

作者： Li, Yongmeng Tian, Yu Liu, Wenjian Gao, Longxiang Tian, Hui Xing, Lumin The First Affiliated Hospital of Shandong First Medical University Shandong Provincial Qianfoshan Hospital Shandong Key Laboratory of Digital Diagnosis and Treatment of Thoracic Oncology Shandong Engineering Research Center of Intelligent Surgery Shandong Jinan250014 China Qilu Hospital of Shandong University Department of Thoracic Surgery Shandong Jinan250012 China City University of Macau Faculty of Data Science China Qilu University of Technology Shandong Academy of Sciences Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Jinan250014 China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computer Networks Jinan250000 China The First Affiliated Hospital of Shandong First Medical University Shandong Provincial Qianfoshan Hospital Shandong Key Laboratory of Digital Diagnosis and Treatment of Thoracic Oncology Shandong Jinan250014 China

Traffic congestion during ambulance travel can delay medical response times. With the growing availability of traffic data, computational methods for traffic flow prediction are attracting significant attention. However, current computational prediction models have the following shortcomings. Fully connected networks require extensive feature engineering yet struggle to capture local traffic flow features. Their high parameter count increases training time and overfitting risk, while varying traffic conditions hinder model generalization. Therefore, in this work, we propose a hybrid-feature-based autoencoder (HFAE) model to predict traffic flow and accelerate ambulance medical response time in Internet of Vehicles. Specifically, the HFAE model simultaneously considers local features from the convolutional neural network and global features from the fully connected neural network. Additionally, the HFAE model uses an autoencoder to reconstruct the original input, capturing the intrinsic structure of the traffic flow data. Specifically, the root mean-squared error and mean absolute error scores of the HFAE model are better than those of the three mainstream models. © 2014 IEEE.

关键词： Ambulances

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A Survey on Intelligent Solutions for Increased Video Delivery Quality in Cloud-Edge-End networks

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IEEE Communications Surveys and Tutorials 2025年第2期27卷 1363-1394页

作者： Shi, Wanxin Li, Qing Yu, Qian Wang, Fulin Shen, Gengbiao Jiang, Yong Xu, Yang Ma, Lianbo Muntean, Gabriel-Miro Tsinghua University Tsinghua Shenzhen International Graduate School Shenzhen518055 China Fudan University School of Computer Science Shanghai200433 China Peng Cheng Laboratory Department of Strategic and Advanced Interdisciplinary Research Shenzhen518066 China Institute of Future Networks Southern University of Science and Technology Shenzhen518055 China Tecorigin Software Department Shanghai200120 China Peng Cheng Laboratory Department of Strategic and Advanced Interdisciplinary Research Shenzhen518055 China Northeastern University College of Software Shenyang110819 China Dublin City University Performance Engineering Laboratory School of Electronic Engineering DublinD09 DD7R Ireland

The digital age has brought a significant increase in video traffic. This traffic growth, driven by rapid Internet advancements and a surge in multimedia applications, presents both challenges and opportunities to video transmissions. Users seek high-quality video content, prompting service providers to offer high-definition options to improve user experience and increase profits. However, traditional end-to-end best-effort networks struggle to meet the demands of extensive video streaming and ensure good user Quality of Experience (QoE), especially in high user mobility scenarios or fluctuating network conditions. Addressing some of these challenges, content delivery networks (CDN) are instrumental in delivering video content, but they are under increased pressure to support high quality and reduce their deployment and maintenance costs. Currently, cloud-edge-end fusion technologies have become one of the optimization directions for network services due to their flexibility and scalability. At the same time, in the context of the recent advancements in computing-focused network paradigms, intelligent enhancement techniques (e.g., super-resolution), commonly utilized in image optimization, have been adopted as a pivotal solution for increasing video delivery quality. To illustrate the essence and employment of the intelligent enhancement solutions for video streaming, this paper first outlines the video streaming process, discusses relevant evaluation metrics, and examines aspects related to the intelligent solutions. Then the paper presents the intelligent enhancement process of video streaming, analyzes various typical intelligent models for content enhancement and highlights their distinct characteristics. This exploration delves deeper into various intelligent quality-improved solutions, scrutinizing their applicability across different transmission scenarios like Video on Demand (VoD) and live streaming, and shedding light on their strengths and weaknesses from

关键词： Video streaming

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

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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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A High Performance Region Recognition Network-Enhanced Deep CNN for Image Content Perceptual Hashing

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IEEE Internet of Things Journal 2025年

作者： Yang, Meihong Qi, Baolin Ma, Bin Xu, Jian Xian, Yongjin Li, Xiaolong Jinan China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computer Networks Jinan China School of Computer and Information Engineering Shandong University of Finance and Economics Jinan China Beijing Jiaotong University School of Computer and Information Technology Beijing100044 China

Perceptual image hashing has emerged as a crucial forensic tool within the Internet of Things (IoT) ecosystem. Traditional perceptual hashing algorithms predominantly rely on global image features to generate hash codes, which limit their ability to represent key features of images effectively. This paper introduces a Perceptual Region Recognition Network (PRRN) to accurately identify key feature regions in images based on their texture distribution characteristics, thereby generating image perceptual hashing codes that reflect the key content of the images. At the same time, a perceptual hashing feature extraction module, which integrates a Residual Network (ResNet) and a Weighted Feature Fusion Network (WFFN), is built to extract deep semantic features of the object image. Where, ResNet is leveraged to extract high-level semantic features, while WFFN ensures the preservation of low-level local features. Furthermore, skip connections are employed to achieve content enhancements for intricate details of critical image regions. Additionally, the Mean Squared Error (MSE) loss is incorporated to enhance the accuracy of key region localization, further improving the sensitivity of image perceptual hash codes and accelerating the network’s convergence speed. Extensive experimental evaluations demonstrate that the proposed PRRN-based perceptual image hashing scheme significantly outperforms other state-of-the-art methods in terms of image feature representation capability. Specifically, it achieves an average improvement of over 1.2% in attack-resistant capability for images compared with other counterparts, making it a promising candidate for practical applications in the IoT environment. © 2014 IEEE.

关键词： Mean square error

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Improving Adversarial Training From the Perspective of Class-Flipping Distribution

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IEEE Transactions on Pattern Analysis and Machine Intelligence 2025年第6期47卷 4330-4342页

作者： Dawei Zhou Nannan Wang Tongliang Liu Xinbo Gao State Key Laboratory of Integrated Services Networks Xidian University Xi’an China School of Computer Science Facult of Engineering The University of Sydney Camperdown NSW Australia Chongqing Key Laboratory of Image Cognition Chongqing University of Posts and Telecommunications Chongqing China

Adversarial training has been proposed and widely recognized as a very effective method to defend against adversarial noise. However, the label flipping pattern on different classes still need deeper exploration to identify potential problems and assist in further enhancing robustness. In this work, we model the class-flipping distribution via statistical investigations and find this distribution reveals two shortcomings: the highly misleading category is present in the model's predictions for data in each class, and the trend in class flipping are significantly different across classes. Based on these observations, we propose a Class-Flipping-aware Adversarial Training (CFAT) method. On the one hand, we obtain the most misleading categories for the data in each class by counting the samples flipped to different wrong categories, and utilize them as the target to construct corresponding targeted adversarial samples, respectively. On the other hand, we take the proportions of samples flipped to the most misleading category as factors to scale the perturbation budgets of adversarial training samples for the data with corresponding classes. Experimental results on datasets with different class number validate the effectiveness of the proposed method.

关键词： Training Noise Robustness Accuracy Training data Predictive models Data models Airplanes Perturbation methods Hands

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Mission-Driven Resource Scheduling in Satellite-Terrestrial networks: From Perspective of Collaboration and Reconfiguration

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IEEE Transactions on Communications 2025年

作者： Zhou, Di Sheng, Min Bao, Chenxi Wang, Yixin Li, Jiandong Han, Zhu Xidian University State Key Laboratory of Integrated Service Networks Shaanxi Xi'an710071 China University of Houston Department of Electrical and Computer Engineering HoustonTX77004 United States Kyung Hee University Department of Computer Science and Engineering Seoul446-701 Korea Republic of

Satellite-terrestrial networks (STNs) are emerging as a promising solution for provisioning comprehensive services, such as the Internet of Remote Things (IoRT) and remote sensing, within the realm of 6G wireless networks. Nonetheless, resource failures and the exigencies of diverse mission urgencies exacerbate the intricacies of resource scheduling in STNs, thus impeding the effective alignment of distinct mission requirements with dynamic resources. In light of these challenges, we first mathematically formulate the complex resource scheduling problem in STNs as a stochastic optimization paradigm, endeavoring to maximize the number of successfully accomplished missions. Subsequently, we conceptualize the resource evolution to delineate scheduling dynamics, encompassing potential contingencies of resource discontinuities. Next, we propose an innovative hierarchical deep learning-based mission-driven resource scheduling (HDL-MDRS) algorithm, aimed at optimizing resource collaboration and reconfiguration to amplify network performance within the dynamic ambits characterized by resource disruptions. The HDL-MDRS algorithm achieves a coarse-grained alignment of diverse mission requirements with multidimensional resources. It enhances overall mission fulfillment and network resource utilization efficiency through fine-grained collaboration and reconfiguration among satellites, both within and across different clusters. Notably, the simulation findings substantiate the effectiveness of the HDL-MDRS algorithm, effectively ensuring the requirements of different types of missions in case of the unforeseen resource failures, orchestrated through efficient resource collaboration and on-demand reconfiguration. © 2025 IEEE.

关键词： Stochastic systems

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Advancing low-resource dialect identification: A hybrid cross-lingual model leveraging CAMeLBERT and FastText for Algerian Arabic

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Expert Systems with Applications 2025年 284卷

作者： Chabane, Mafaza Harrag, Fouzi Shaalan, Khaled Departement of Computer Science University of Ferhat Abbas Setif Algeria Laboratory of Networks and Distributed Systems University of Ferhat Abbas Setif Algeria Faculty of Engineering and IT The British University in Dubai Dubai United Arab Emirates

This study addresses the issue of dialect identification in low-resource contexts, specifically Algerian Arabic. To mitigate the lack of labeled data for these dialects, we propose an innovative hybrid model that utilizes cross-lingual transfer learning from Modern Standard Arabic (MSA), a high-resource language. Our hybrid model integrates pre-trained CAMeLBERT and FastText embeddings, both trained on MSA, to handle dialectal input via two parallel routes. The CAMeLBERT pathway generates profound contextual embeddings, whereas the FastText pathway utilizes a convolutional layer, a Bidirectional GRU, and an attention mechanism. The combined results are input into a multi-layer perceptron (MLP) for precise dialect classification. The novelty of our work lies in the strategic leveraging of MSA as a bridge language, enabling the transfer of its rich linguistic knowledge to low-resource dialects. The efficacy of our approach is confirmed through meticulous experimentation plus a comprehensive ablation study. The model's versatility is further evidenced by its testing across various datasets that reflect distinct dialects. Significantly, our model surpassed multiple current methodologies in these assessments. These findings highlight the capacity of cross-lingual transfer learning to improve Natural Language Processing (NLP) tasks for under-resourced languages by utilizing the established knowledge of their high-resource equivalents. © 2025 Elsevier Ltd

关键词： Sentiment analysis

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SAFLA: Semantic-Aware Full Lifecycle Assurance for Intent-Driven networks

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IEEE Transactions on Cognitive Communications and Networking 2025年

作者： Kou, Shiwen Yang, Chungang Gurusamy, Mohan Xidian University The State Key Laboratory on Integrated Services Networks Xi’an710071 China Communication & Networks Lab Department of Electrical and Computer Engineering National University of Singapore SG117583 Singapore

Intent-Driven networks (IDNs) are designed to improve network management efficiency by transforming high-level intents into actionable configurations. Due to evolving user requirements and network dynamics, a semantic gap has emerged between declarative intents and underlying configurations, posing significant challenges to the assurance and reliability of IDNs. Despite promising preliminaries, existing intent assurance approaches, which mainly focus on monitoring intents’ Key Performance Indicators (KPIs), fail to bridge the semantic gap. This gap can result in configurations that technically meet KPIs from a top-down perspective but fail to accurately reflect the original network intent from a bottom-up view, leading to serious security risks and undermining overall objectives. To address this issue, we introduce SAFLA, a semantic-aware framework designed to ensure the intent’s full lifecycle. By seamlessly integrating top-down and bottom-up approaches, SAFLA not only enhances intent assurance by monitoring intent performance but also bridges the semantic gap between high-level intents and low-level configurations. This integration facilitates a self-healing mechanism for IDNs, reducing the need for manual intervention even under evolving requirements and network reconfigurations. Experimental results on software-defined Networking demonstrate the SAFLA’s feasibility and efficiency in intent assurance. © 2015 IEEE.

关键词： Network management

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Resource Allocation for Heterogeneous Statistical QoS Provisioning Over Internet-of-Vehicles via Hierarchical Deep Reinforcement Learning

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IEEE Transactions on Cognitive Communications and Networking 2025年

作者： Zhou, Yuchen Yu, Fei Richard Ren, Mengmeng Yang, Long Chen, Jian Xidian University State Key Laboratory of Integrated Service Networks Xi'an710071 China Carleton University Department of Systems and Computer Engineering OttawaONK1S 5B6 Canada

The high-dynamic characteristics of vehicular networks make it difficult to satisfy strict deterministic quality-of-service (QoS) conditions. Statistical QoS provisioning instead of deterministic QoS provisioning can provide an efficient way to satisfy delay-bounded QoS requirements of vehicular services. Different from the existing work, this paper considers the heterogeneity of statistical QoS provisioning for delay-sensitive services in Internet-of-Vehicles (IoV), where the overall delay violation probability is limited in each time slot for mission-critical applications, and the queue length bound along with its higher-order statistics is limited from a long-term perspective for infotainment services. A long-term resource optimization problem is formulated to minimize the average energy consumption with heterogeneous statistical latency guarantees. To provide a stable and fast solution, Lyapunov optimization method is first leveraged to transform the formulated stochastic problem to a series of short-term deterministic optimization sub-problems. Afterwards, a novel two-level hierarchical deep reinforcement learning (H-DRL) framework is presented, where the knowledge sharing is further introduced to realize the fast model training and resource decision-making with QoS performance guarantees. Simulation results verify the convergence speed and the training accuracy of the presented H-DRL framework, and it also shows the superiority of the proposal in the perspective of the end-to-end delay and the queue stability for heterogeneous delay-bounded services under high-dynamic environments of IoV. © 2025 IEEE.

关键词： Deep reinforcement learning

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FDC-Swap: An efficient face swapping framework based on feature disentangling consistency

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Knowledge-Based Systems 2025年 318卷

作者： Tian, Jue Zhao, Chunya Liu, Yang Chen, Yanping School of Computer Xi'an University of Posts and Telecommunications Xi'an710121 China Ministry of Education Key Laboratory for Intelligent Networks and Network Security School of Cyber Science and Engineering Xi'an Jiaotong University Xi'an710049 China

Face swapping images with high quality are critical for video production, privacy protection, and forgery detection, etc. In face swapping technology, one kernel element is the disentanglement of identity and attribute information. However, due to their deep entanglement in the latent space, effectively enhancing disentangling ability remains challenging. To tackle this challenge, this paper proposes the feature disentangling consistency: by executing the face swapping process (i.e., disentangling and recombining the identity and attribute information of two face images) in a pipeline twice, the generated twice-swapped images should be consistent with the original images. First, a new evaluation method (named FDC-Evaluation) is proposed to assess model performance according to disentangling consistency, which can be reflected in identity-attribute or image-level consistency. Meanwhile, to handle the limitation of missing ground truth, we introduce the FDC-Evaluation into original face swapping network, and propose a highly scalable and lightweight swapping framework (named FDC-Swap). Experiments demonstrate that the FDC-Evaluation overcomes traditional evaluation limitations, such as cognitive differences in identity-attribute features, identity assessment unfairness due to choices of identity encoders in training and evaluation, and operational complexity due to attribute feature diversity. Meanwhile, the FDC-Swap framework effectively enhances the performance of various existing face swapping networks. Code available at https://***/tzjoyzx/FDC_Swap. © 2025 Elsevier B.V.

关键词： Evaluation method Face swapping Feature disentangling consistency Generative adversarial networks Symmetric network structure

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