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When Crowdsensing Meets Smart Cities: A Comprehensive Survey and New Perspectives

IEEE Communications Surveys and Tutorials 2025年第2期27卷 1101-1151页

作者： Wang, Zhenning Cao, Yue Jiang, Kai Zhou, Huan Kang, Jiawen Zhuang, Yuan Tian, Daxin Leung, Victor C. M. Wuhan University School of Cyber Science and Engineering Wuhan430072 China Northwestern Polytechnical University College of Computer Xi’an710129 China Guangdong University of Technology School of Automation Guangzhou510006 China Wuhan University State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing Wuhan430079 China Beihang University School of Transportation Science and Engineering Beijing100191 China Shenzhen University College of Computer Science and Software Engineering Shenzhen518061 China The University of British Columbia Department of Electrical and Computer Engineering VancouverBCV6T 1Z4 Canada

Crowdsensing has received widespread attention in recent years. It is extensively employed in smart cities and intelligent transportation systems. This paper comprehensively surveys the latest research advancements in crowdsensing for smart cities from a novel perspective. Specifically, this paper is categorized according to sensing entities in smart cities, including human-oriented sensing, vehicle-oriented sensing, and infrastructure-oriented sensing. Meanwhile, the development of Unmanned Aerial Vehicle (UAV)-assisted sensing in recent years is also summarized, accompanied by a timeline of related research. To facilitate easy comprehension, we have positioned the reading flow into the corresponding architectures, resolved problems, existing technical solutions, and specific application scenarios for different sensing entities. In particular, the problems to be solved are further analyzed from four technical perspectives, namely mathematics and operational research, artificial intelligence and machine learning, incentive mechanisms, security and privacy protection. Based on the proposed taxonomy, recent studies are thoroughly investigated to illustrate the current state of research in crowdsensing. Furthermore, this paper highlights the emerging applications of human-oriented and vehicle-oriented sensing in smart cities, as well as the frameworks, platforms, simulators, and datasets involved in crowdsensing. Finally, this paper discusses research directions related to crowdsensing in smart cities, such as digital twins, metaverses, and artificial intelligence-generated content. The primary goal of this survey is to review and synthesize prior research, identify potential avenues for future research, and explore opportunities for collaboration with other relevant research domains. © 1998-2012 IEEE.

关键词： Smart city

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HybridFL: Hybrid Approach Toward Privacy-Preserving Federated Learning 6th

HybridFL: Hybrid Approach Toward Privacy-Preserving Federate...

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6th International Conference on Security and Privacy in New Computing Environments, SPNCE 2023

作者： Ali, Sheraz Mamoon, Saqib Usman, Areeba Abidin, Zain ul Zhao, Chuan School of Information Science and Engineering University of Jinan Jinan250022 China School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing210094 China University of Sargodha Sargodha Pakistan Shandong Provincial Key Laboratory of Network-Based Intelligent Computing University of Jinan Jinan250022 China Shandong Provincial Key Laboratory of Software Engineering University of Jinan Jinan250022 China

ISBN: (纸本)9783031736988

In this study, we introduce a novel Hybrid Federated Learning (HybridFL) approach aimed at enhancing privacy and accuracy in collaborative machine learning scenarios. Our methodology integrates Differential Privacy (DP) and secret sharing techniques to address inference risks during training and protect against information leakage in the output model. Drawing inspiration from recent advances, we present a HybridFL framework that combines the strengths of Homomorphic Encryption (HE) and Multi-Party Computation (MPC) to achieve secure computation without the computational overhead of pure HE methods. Our contributions include a privacy-preserving design for Federated Learning (FL) that ensures local data privacy through secret sharing while leveraging DP mechanisms for noise addition. The system offers resilience against unreliable participants and is evaluated using various machine learning models, including Convolutional Neural Networks (CNN), Multi-Layer Perceptrons (MLP), and linear regression. Furthermore, we address potential external threats by deploying predictive model outputs as robust services against inference attacks. Experimental results demonstrate improved accuracy and convergence speed, establishing the viability of HybridFL as an effective solution for collaborative machine learning with enhanced privacy guarantees. © ICST Institute for computer Sciences, Social Informatics and Telecommunications engineering 2025.

关键词： Federated learning

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A Spatio-Temporal Transformer for Enhancing the Coherence on 3D Human Motion Prediction 18th

A Spatio-Temporal Transformer for Enhancing the Coherence ...

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18th Chinese Conference on Biometric Recognition, CCBR 2024

作者： Xie, Miner Wang, Lei Liu, Feng College of Computer Science and Software Engineering Shenzhen University Shenzhen China Guangdong Provincial Key Laboratory of Intelligent Information Processing Shenzhen University Shenzhen China Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China

ISBN: (纸本)9789819610709

Human motion prediction is of great importance for artificial intelligence systems, particularly in fields like autonomous driving and human-computer interaction. Existing methods have achieved good results in simple motions with small changes but still struggle with coherence in predicting complex motions with large changes in a short time. Regarding the issue in current methods, this paper proposes a novel network composed of three modules: spatial encoders that captures correlations between joints, temporal encoders that models the movement patterns of joints across frames, and a multi-frame temporal encoder that learns high-level temporal correlations to enhance prediction coherence. In addition, we propose to use Weighted Mean Per-Joint Position Error (WMPJPE) as the loss function by considering that different joints of the human skeleton have different effects on the entire action. We evaluate our method on the Human3.6M, AMASS, and 3DPW datasets. The quantitative results indicate that our method notably outperforms other methods in the prediction range of 160ms to 560ms. The qualitative results show that our method can improve the coherence of prediction. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Prediction models

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An efficient and balanced feature pyramid network: shared-FPN

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Optoelectronics Letters 2025年

作者： Tian Sulong Qin Longfei Pang Wenchao Gao Ying Zhao Dexin School of Computer Science and Engineering， Tianjin University of Technology Tianjin Key Laboratory of Intelligence Computing and Novel Software Technology， Tianjin University of Technology

In the field of object detection， it is challenging to achieve a balance between neck complexity and accuracy. To address this issue， we propose an efficient and decoupled neck module called Shared-FPN(Shared Feature Pyramid Network). Our design aims to effectively address the trade-off between these two factors. Neck modules are crucial for effectively fusing multi-scale information in neural network models. However， traditional neck designs， with their single structure， have struggled to significantly improve detection performance. In recent years， the incorporation of neural architecture search techniques in neck design has led to an increase in model complexity. This trade-off between model complexity and detection performance highlights the need for more efficient neck design approaches. Not only does Shared-FPN not increase the number of model parameters and FLOPs， but it also can be easily ported to any of the detection models. It improves on PAFPN by using transposed convolution with a large convolution kernel as the upsampling module， designing spatial pyramidal pooling-fast downsampling(SPPFD) based on shared pooling， and designing shared convolution as the right part module. To evaluate the performance of Shared-FPN in object detection tasks， we conducted experiments on object detection datasets. The results show that Shared-FPN achieved excellent performance across all sizes. In particular， on the VOC 2012 dataset， which contains mostly large objects， Shared-FPN's mAP was improved by 11.2% compared to FPN with YOLOX-s as the detector. On the COCO dataset， which contains mostly small objects， Shared-FPN's mAP was improved by 7.8% compared to FPN and 7.1% compared to PAFPN with Faster RCNN as the detector. Shared-FPN can be easily inserted into any of the detectors for better performance in various scenarios， such as small， medium or large objects.

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Guest Editorial: Special Issue on Human-Machine Fusion Decision-Making for Emergency Handling

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IEEE Transactions on Automation Science and engineering 2025年 22卷 4427-4433页

作者： Wu, Edmond Q. Li, Jianqiang Chen, Guimin Yuce, Mehmet R. Islam, Shafiqul Ser, Javier Del Yu, Hui Liu, Peter Xiaoping Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai200240 China College of Computer and Software Engineering Shenzhen University Shenzhen518060 China State Key Laboratory for Manufacturing Xi'an Jiaotong University Xi'an710049 China Department of Electrical and Computer Systems Engineering Monash University MelbourneVIC3800 Australia Xavier University of Louisiana New OrleansLA70125 United States Tecnalia Institute Bizkaia48160 Spain School of Creative Technologies University of Portsmouth PortsmouthPO1 2UP United Kingdom Department of Systems and Computer Engineering Carleton University OttawaONKIS 5B6 Canada

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RGB-Based Category-Level Object Pose Estimation via Depth Recovery and Adaptive Refinement

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IEEE Robotics and Automation Letters 2025年第6期10卷 5377-5384页

作者： Yang, Hui Sun, Wei Liu, Jian Zheng, Jin Zeng, Zhiwen Mian, Ajmal Hunan University National Engineering Research Center for Robot Visual Perception and Control Technology College of Electrical and Information Engineering Changsha410082 China Hunan University State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body Changsha410082 China Central South University School of Architecture and Art Changsha410082 China University of Western Australia Department of Computer Science and Software Engineering CrawleyWA6009 Australia

Category-level pose estimation methods have received widespread attention as they can be generalized to intra-class unseen objects. Although RGB-D-based category-level methods have made significant progress, reliance on depth image limits practical application. RGB-based methods offer a more practical and cost-effective solution. However, current RGB-based methods struggle with object geometry perception, leading to inaccurate pose estimation. We propose depth recovery and adaptive refinement for category-level object pose estimation from a single RGB image. We leverage DINOv2 to reconstruct the coarse scene-level depth from the input RGB image and propose an adaptive refinement network based on an encoder-decoder architecture to dynamically improve the predicted coarse depth and reduce its gap from the ground truth. We introduce a 2D–3D consistency loss to ensure correspondence between the point cloud obtained from depth projection and the objects in the 2D image. This consistency supervision enables the model to maintain alignment between the depth image and the point cloud. Finally, we extract features from the refined point cloud and feed them into two confidence-aware rotation regression branches and a translation and size prediction residual branch for end-to-end training. Decoupling the rotation matrix provides a more direct representation, which facilitates parameter optimization and gradient propagation. Extensive experiments on the REAL275 and CAMERA25 datasets demonstrate the superior performance of our method. Real-world estimation and robotic grasping experiments demonstrate our model robustness to occlusion, clutter environments, and low-textured objects. Our code and robotic grasping video are available at DA-Pose. © 2016 IEEE.

关键词： Depth perception

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FreqSense: Universal and Low-Latency Adversarial Example Detection for Speaker Recognition with Interpretability in Frequency Domain

FreqSense: Universal and Low-Latency Adversarial Example Det...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Huang, Yihuan Li, Yuanzhe Ren, Yanzhen Tu, Weiping Yang, Yuhong Key Laboratory of Aerospace Information Security and Trusted Computing Ministry of Education China School of Cyber Science and Engineering Wuhan University China National Engineering Research Center for Multimedia Software School of Computer Science Wuhan University China

ISBN: (纸本)9798350368741

Speaker recognition (SR) systems are particularly vulnerable to adversarial example (AE) attacks. To mitigate these attacks, AE detection systems are typically integrated into SR systems. To overcome the limitations of low detection accuracy, poor generalization, and high latency in existing schemes, this paper proposes FreqSense, an AE detection scheme based on frequency distribution features. FreqSense detects a variety of unknown AE attacks with low latency, and provides interpretability in its detection process. The basic idea of FreqSense is that AE typically introduce carefully designed noise in specific frequency bands that are associated with highly distinctive speaker identities. Therefore, leveraging the distributional variations in these frequency bands can effectively distinguish between AE and benign audio. FreqSense models frequency distribution features by integrating time-frequency transformation technology with a self-attention mechanism and employs a neural network-based classifier to distinguish between AE and benign audio. Experimental results show that FreqSense achieves an overall detection accuracy of 99.2%, surpassing state-of-the-art (SOTA) schemes by 27.2%. When confronting unknown AE attacks, FreqSense achieves a detection accuracy of 98.3% with a latency of just 0.0014 seconds. © 2025 IEEE.

关键词： Adversarial Attack Adversarial Example Detection Speaker Recognition

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Visual Language Model based Cross-modal Semantic Communication Systems

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IEEE Transactions on Wireless Communications 2025年第5期24卷 3937-3948页

作者： Jiang, Feibo Tang, Chuanguo Dong, Li Wang, Kezhi Yang, Kun Pan, Cunhua Hunan Normal University Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing Changsha410081 China Hunan Normal University School of Information Science and Engineering Changsha410081 China Hunan University of Technology and Business School of Computer Science Changsha410205 China Xiangjiang Laboratory Changsha410205 China Brunel University London Department of Computer Science UxbridgeUB8 3PH United Kingdom Nanjing University School of Intelligent Software and Engineering Suzhou215163 China Southeast University National Mobile Communications Research Laboratory Nanjing210096 China

Semantic Communication (SC) has emerged as a novel communication paradigm in recent years. Nevertheless, extant Image Semantic Communication (ISC) systems face several challenges in dynamic environments, including low information density, catastrophic forgetting, and uncertain Signal-to-Noise Ratio (SNR). To address these challenges, we propose a novel Vision-Language Model-based Cross-modal Semantic Communication (VLM-CSC) system. The VLM-CSC comprises three novel components: (1) Cross-modal Knowledge Base (CKB) is used to extract high-density textual semantics from the semantically sparse image at the transmitter and reconstruct the original image based on textual semantics at the receiver. The transmission of high-density semantics contributes to alleviating bandwidth pressure. (2) Memory-assisted Encoder and Decoder (MED) employ a hybrid long/short-term memory mechanism, enabling the semantic encoder and decoder to overcome catastrophic forgetting in dynamic environments when there is a drift in the distribution of semantic features. (3) Noise Attention Module (NAM) employs attention mechanisms to adaptively adjust the semantic coding and the channel coding based on SNR, ensuring the robustness of the CSC system. The experimental simulations validate the effectiveness, adaptability, and robustness of the CSC system. © 2002-2012 IEEE.

关键词： Decoding

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Industry Upgrading and Public Opinion Prevention Using BERTopic 1

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2nd International Conference on Artificial Intelligence Security and Privacy, AIS and P 2024

作者： Wang, Xinhua Yu, Xiaomei Jiang, Yunmeng Zheng, Xiangwei Li, Wei School of Information Science and Engineering Shandong Normal University Jinan China Shandong Provincial Key Laboratory for Distributed Computer Software Novel Technology Jinan250358 China Shandong Normal University Library Shandong Normal University Jinan China

ISBN: (数字)9789819611485

ISBN: (纸本)9789819611478

The development status of the hotel industry in a city is also an essential factor influencing local tourism revenue. In the tourism industry, besides dining and attractions, which are hotspots for public opinion, the hotel industry is also a key area. The development status of the hotel industry in a city is also an essential factor influencing local tourism revenue. In this context, it is crucial for hotels to address how to prevent such negative public opinion crises. In this paper, we use hotel review texts as an example to discuss how to use BERTopic and GRU networks to extract topics and analyze sentiment from guest reviews. This analysis helps understand guests’ focus and sentiment trends regarding hotels, enabling better grasp public opinion direction and user needs. Through topic extraction and sentiment analysis, we can extract crucial information from large scale hotel review texts. This enables monitor public opinion to help prevent large scale public opinion crises, allows for targeted improvements in the hotel industry, and supports decision-making for hotels and the tourism industry. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Hotels

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Deep Learning Algorithm for Person Re-Identification Based on Dual Network Architecture

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computers, Materials & Continua 2025年第5期83卷 2889-2905页

作者： Meng Zhu Xingyue Wang Honge Ren Abeer Hakeem Linda Mohaisen College of Information Engineering Harbin UniversityHarbin150086China Heilongjiang Provincial Key Laboratory of the Intelligent Perception and Intelligent Software Harbin UniversityHarbin150086China College of Computer and Control Engineering Northeast Forestry UniversityHarbin150040China Heilongjiang Forestry Intelligent Equipment Engineering Research Center Northeast Forestry UniversityHarbin150040China Faculty of Computing and Information Technology King Abdulaziz UniversityJeddah21589Saudi Arabia

Changing a person’s posture and low resolution are the key challenges for person re-identification(ReID)in various deep learning *** this paper,we introduce an innovative architecture using a dual attention network that includes an attentionmodule and a joint measurement module of spatial-temporal *** proposed approach can be classified into two main ***,the spatial attention feature map is formed by aggregating features in the spatial ***,the same operation is carried out on the channel dimension to formchannel attention ***,the receptive field size is adjusted adaptively tomitigate the changing person posture ***,we use a joint measurement method for the spatial-temporal information to fully harness the data,and it can also naturally integrate the information into the visual features of supervised ReID and hence overcome the low resolution *** experimental results indicate that our proposed algorithm markedly improves the accuracy in addressing changing human postures and low-resolution issues compared with contemporary leading *** proposed method shows superior outcomes on widely recognized benchmarks,which are the Market-1501,MSMT17,and DukeMTMC-reID ***,the proposed algorithmattains a Rank-1 accuracy of 97.4% and 94.9% mAP(mean Average Precision)on the Market-1501 ***,it achieves a 94.2% Rank-1 accuracy and 91.8% mAP on the DukeMTMC-reID dataset.

关键词： Person reidentification ReID computer vision self-attention spatial-temporal information

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