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学者姓名：於志勇
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Abstract ：
Cell selection and data offloading are the keys to obtaining MCS services with low sensing cost and low data processing delay. Due to the spatiotemporal correlation between data and the local-area coverage of edge servers, cell selection and data offloading will affect each other and require co-optimization. To achieve the co-optimization, we design the method OptInter based on the hierarchical reinforcement learning. OptInter can realize the interactive training between cell selection model and data offloading model. Finally, we evaluate our proposed method based on four datasets, each of which composited by real-world (e.g., NO$_{2}$2 concentration, AQI value, Didi order, and Didi trajectory) data and simulated data. Compared with the four baseline methods (e.g., OptMOEA/D, OptStageCD, OptStageDC, and OptWeight), the comprehensive performance of our proposed method can be improved by 11.83%, 20.48%, 10.14%, and 42.27% on average, respectively.
Keyword ：
cell selection cell selection co-optimization co-optimization data offloading data offloading hierarchical reinforcement learning hierarchical reinforcement learning Sparse mobile crowdsensing Sparse mobile crowdsensing
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| GB/T 7714 | Han, Lei , Yu, Zhiwen , Zhang, Xuan et al. Co-Optimization of Cell Selection and Data Offloading in Sparse Mobile Crowdsensing [J]. | IEEE TRANSACTIONS ON MOBILE COMPUTING , 2024 , 23 (5) : 6088-6103 . |
| MLA | Han, Lei et al. "Co-Optimization of Cell Selection and Data Offloading in Sparse Mobile Crowdsensing" . | IEEE TRANSACTIONS ON MOBILE COMPUTING 23 . 5 (2024) : 6088-6103 . |
| APA | Han, Lei , Yu, Zhiwen , Zhang, Xuan , Yu, Zhiyong , Shan, Weihua , Wang, Liang et al. Co-Optimization of Cell Selection and Data Offloading in Sparse Mobile Crowdsensing . | IEEE TRANSACTIONS ON MOBILE COMPUTING , 2024 , 23 (5) , 6088-6103 . |
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随着城市化进程的不断加快,工业发展、人口聚集使得空气质量问题日益严峻.出于对采集成本的考虑,对空气质量的主动采样正受到越来越多的关注.但现有模型要么只能迭代选择采样位置,要么难以实时更新采样算法.基于此,提出了一种基于压缩感知自适应测量矩阵的空气质量主动采样方法,将采样位置的选择问题转化为矩阵的列子集选择问题.该方法首先利用历史完整数据进行字典学习,然后将学习后的字典经过列子集选择后得到能够指导批量采样的自适应测量矩阵,最后结合利用空气质量数据特性构建的稀疏基矩阵恢复出未采样的数据.该方法使用压缩感知模型一体化实现采样和推断,避免了使用多个模型的不足.此外,考虑到空气质量的时序变动问题,在每一次的主动采样后,字典还会利用最新数据进行在线更新以指导下一次的采样.两个真实数据集上的实验结果表明,经过字典学习后得到的自适应测量矩阵在低于20％的多个采样率下,恢复性能优于所有基线.
Keyword ：
主动采样 主动采样 压缩感知 压缩感知 字典学习 字典学习 群智感知 群智感知 自适应测量矩阵 自适应测量矩阵
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| GB/T 7714 | 黄伟杰 , 郭贤伟 , 於志勇 et al. 基于压缩感知自适应测量矩阵的空气质量主动采样 [J]. | 计算机科学 , 2024 , 51 (7) : 116-123 . |
| MLA | 黄伟杰 et al. "基于压缩感知自适应测量矩阵的空气质量主动采样" . | 计算机科学 51 . 7 (2024) : 116-123 . |
| APA | 黄伟杰 , 郭贤伟 , 於志勇 , 黄昉菀 . 基于压缩感知自适应测量矩阵的空气质量主动采样 . | 计算机科学 , 2024 , 51 (7) , 116-123 . |
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Discovering communities in attributed networks is an important research topic in complex network analysis. Community detection based on multi-objective evolutionary computing (MOEA) models community detection as a multi-objective optimization problem and searches the optimal solutions by simulating the evolution of a biological population. However, the existing multi-objective evolutionary algorithms for community detection faces two challenges: their encoding schemes are designed based on network topology and neglects the information in node attributes; and they are easy to fall into local optimum. In this article, we propose a community detection algorithm empowered by multi-objective evolutionary computing, named ECEVO-MOEA, which conducts edge closeness encoding and embedding vector optimization alternately. On the one hand, the evolution of a biological population is completed by employing a new edge closeness encoding scheme and multiple attribute-aware objective functions. On the other hand, the update of embedding vectors is used to calculate similarity matrix and communities to improve solution quality, avoiding it from early convergence. Experiments on real networks demonstrate that ECEVO-MOEA achieves higher accuracy than the baseline algorithms.
Keyword ：
Community networks Community networks Complex networks Complex networks Detection algorithms Detection algorithms Encoding Encoding Evolutionary computation Evolutionary computation Image edge detection Image edge detection Pareto optimization Pareto optimization Search problems Search problems Social factors Social factors Statistics Statistics
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| GB/T 7714 | Guo, Kun , Chen, Zhanhong , Yu, Zhiyong et al. Evolutionary Computing Empowered Community Detection in Attributed Networks [J]. | IEEE COMMUNICATIONS MAGAZINE , 2024 , 62 (5) : 22-26 . |
| MLA | Guo, Kun et al. "Evolutionary Computing Empowered Community Detection in Attributed Networks" . | IEEE COMMUNICATIONS MAGAZINE 62 . 5 (2024) : 22-26 . |
| APA | Guo, Kun , Chen, Zhanhong , Yu, Zhiyong , Chen, Kai , Guo, Wenzhong . Evolutionary Computing Empowered Community Detection in Attributed Networks . | IEEE COMMUNICATIONS MAGAZINE , 2024 , 62 (5) , 22-26 . |
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Behavior-based Wi-Fi user authentication has gained popularity in user-centered smart systems. However, its wide adoption has been hindered by certain critical issues, including significant performance degradation when the environment changes, the inability to handle unknown activities, and weak security due to basing authentication on the recognition of a single, one-off activity. In this paper, we propose Wi-Dist, which authenticates a user using a behavior password, i.e. a pre-chosen sequence of activities. Wi-Dist addressed the previously mentioned technical challenges through a cross-layer joint optimization framework. In particular, we address environment dependency by incorporating adversarial learning and optimizing both the signal layer and the domain adaptation layer. This enhances the performance of the learned model across various environments. To effectively handle unknown behaviors, we utilize an adversarial learning-based network. This network establishes a pseudo-decision boundary between samples from known and unknown sources, ensuring robust authentication. Additionally, for authentication using continuous activities, we employ double-sliding windows activity monitoring. This approach, coupled with activity state correction, partitions activities for accurate recognition. We also conducted extensive experiments in indoor environments to demonstrate that Wi-Dist is effective and robust.
Keyword ：
action recognition action recognition Adaptation models Adaptation models Authentication Authentication channel state information channel state information Computational modeling Computational modeling cross-environment cross-environment Libraries Libraries Passwords Passwords Training data Training data Wi-Fi Wi-Fi Wireless fidelity Wireless fidelity
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| GB/T 7714 | Zhang, Lei , Jiang, Yunzhe , Ma, Yazhou et al. Toward Robust and Effective Behavior Based User Authentication With Off-the-Shelf Wi-Fi [J]. | IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY , 2024 , 19 : 8731-8746 . |
| MLA | Zhang, Lei et al. "Toward Robust and Effective Behavior Based User Authentication With Off-the-Shelf Wi-Fi" . | IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY 19 (2024) : 8731-8746 . |
| APA | Zhang, Lei , Jiang, Yunzhe , Ma, Yazhou , Mao, Shiwen , Huang, Wenyuan , Yu, Zhiyong et al. Toward Robust and Effective Behavior Based User Authentication With Off-the-Shelf Wi-Fi . | IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY , 2024 , 19 , 8731-8746 . |
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Sparse crowdsensing collects data from a subset of the sensing area and infers data for unsensed areas, reducing data collection costs. Previous works have primarily focused on independently collecting and inferring single types of data. However, real-world scenarios often involve multiple types of data that can complement each other by providing missing spatiotemporal distribution information. In this paper, we fully consider both intra-data correlations among data of the same type and inter-data correlations among data of different types, enabling collaborative execution of various tasks. In addition, we enhance the adaptability in practical application scenarios by utilizing real-time collected sparse data to guide task execution. For this purpose, we propose a multi-task adaptive budgeting framework for online sparse crowdsensing, called MTAB-SC. This framework consists of three parts: training data updating, data inference, and data collection. First, we propose a multi-task data updating method to keep models up-to-date. Second, we design a data inference network for multi-task data joint inference. Finally, to allocate suitable budgets for each task and facilitate collaborative data collection across multiple tasks, we propose an Adaptive Budgeting for Collaborative Data Collection model (AB-CoDC). The effectiveness of our proposals is demonstrated through extensive experiments on two real-world datasets.
Keyword ：
Collaboration Collaboration Correlation Correlation Crowdsensing Crowdsensing Data collection Data collection model updates model updates multi-agent reinforcement learning multi-agent reinforcement learning multi-task collaboration multi-task collaboration Multitasking Multitasking Online sparse crowdsensing Online sparse crowdsensing Sensors Sensors Task analysis Task analysis
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| GB/T 7714 | Tu, Chunyu , Yu, Zhiyong , Han, Lei et al. Adaptive Budgeting for Collaborative Multi-Task Data Collection in Online Sparse Crowdsensing [J]. | IEEE TRANSACTIONS ON MOBILE COMPUTING , 2024 , 23 (7) : 7983-7998 . |
| MLA | Tu, Chunyu et al. "Adaptive Budgeting for Collaborative Multi-Task Data Collection in Online Sparse Crowdsensing" . | IEEE TRANSACTIONS ON MOBILE COMPUTING 23 . 7 (2024) : 7983-7998 . |
| APA | Tu, Chunyu , Yu, Zhiyong , Han, Lei , Guo, Xianwei , Huang, Fangwan , Guo, Wenzhong et al. Adaptive Budgeting for Collaborative Multi-Task Data Collection in Online Sparse Crowdsensing . | IEEE TRANSACTIONS ON MOBILE COMPUTING , 2024 , 23 (7) , 7983-7998 . |
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Accurate monitoring of urban waterlogging contributes to the city's normal operation and the safety of residents' daily travel. However, due to feedback delays or high costs, existing methods make large-scale, fine-grained waterlogging monitoring impossible. A common method is to forecast the city's global waterlogging status using its partial waterlogging data. This method has two challenges: first, existing predictive algorithms are either driven by knowledge or data alone; and second, the partial waterlogging data is not collected selectively, resulting in poor predictions. To overcome the aforementioned challenges, this paper proposes a framework for large-scale and fine-grained spatiotemporal waterlogging monitoring based on the opportunistic sensing of limited bus routes. This framework follows the Sparse Crowdsensing and mainly comprises a pair of iterative predictor and selector. The predictor uses the collected waterlogging status and the predicted status of the uncollected area to train the graph convolutional neural network. It combines both knowledge-driven and data-driven approaches and can be used to forecast waterlogging status in all regions for the upcoming term. The selector consists of a two-stage selection procedure that can select valuable bus routes while satisfying budget constraints. The experimental results on real waterlogging and bus routes in Shenzhen show that the proposed framework could easily perform urban waterlogging monitoring with low cost, high accuracy, wide coverage, and fine granularity.
Keyword ：
active learning active learning graph convolutional network graph convolutional network route selection route selection sparse crowdsensing sparse crowdsensing waterlogging prediction waterlogging prediction
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| GB/T 7714 | Wang, Jingbin , Zhang, Weijie , Yu, Zhiyong et al. Route selection for opportunity-sensing and prediction of waterlogging [J]. | FRONTIERS OF COMPUTER SCIENCE , 2024 , 18 (4) . |
| MLA | Wang, Jingbin et al. "Route selection for opportunity-sensing and prediction of waterlogging" . | FRONTIERS OF COMPUTER SCIENCE 18 . 4 (2024) . |
| APA | Wang, Jingbin , Zhang, Weijie , Yu, Zhiyong , Huang, Fangwan , Zhu, Weiping , Chen, Longbiao . Route selection for opportunity-sensing and prediction of waterlogging . | FRONTIERS OF COMPUTER SCIENCE , 2024 , 18 (4) . |
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Sparse mobile crowdsensing (MCS) is a cost-effective data collection paradigm that aims to recruit users to collect data from a part of sensing subareas and infer the rest. In a more realistic scenario, users participate in real-time and collect data along the way. For missing data inference, the significance of data collected from different subareas often varies over time. However, since users' trajectories are uncertain, recruiting users who can cover important spatio-temporal subareas presents a challenge. Additionally, how to segment the budget wisely during recruitment is another challenge. To tackle these challenges, we propose a dual reinforcement learning (RL)-based online user recruitment strategy with adaptive budget segmentation, called DualRL-U, which consists of two alternating decision steps, i.e., the user recruitment decision and the budget retention decision. Specifically, for the user recruitment decision, we use RL to connect the user with data inference accuracy to estimate their contributions. For the budget retention decision, we use RL to connect the budget with the number of times the user can sense to evaluate the cost effectiveness. In this way, a dual RL model is constructed to achieve effective recruitment by alternately executing user recruitment decisions and budget retention decisions. Extensive experiments on real-world sensing data sets show the effectiveness of DualRL-U.
Keyword ：
Adaptive budget segmentation Adaptive budget segmentation Costs Costs Crowdsensing Crowdsensing online user recruitment online user recruitment Recruitment Recruitment reinforcement learning (RL) reinforcement learning (RL) Sensors Sensors sparse mobile crowd-sensing (MCS) sparse mobile crowd-sensing (MCS) Task analysis Task analysis Trajectory Trajectory Uncertainty Uncertainty
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| GB/T 7714 | Guo, Xianwei , Tu, Chunyu , Hao, Yongtao et al. Online User Recruitment With Adaptive Budget Segmentation in Sparse Mobile Crowdsensing [J]. | IEEE INTERNET OF THINGS JOURNAL , 2024 , 11 (5) : 8526-8538 . |
| MLA | Guo, Xianwei et al. "Online User Recruitment With Adaptive Budget Segmentation in Sparse Mobile Crowdsensing" . | IEEE INTERNET OF THINGS JOURNAL 11 . 5 (2024) : 8526-8538 . |
| APA | Guo, Xianwei , Tu, Chunyu , Hao, Yongtao , Yu, Zhiyong , Huang, Fangwan , Wang, Leye . Online User Recruitment With Adaptive Budget Segmentation in Sparse Mobile Crowdsensing . | IEEE INTERNET OF THINGS JOURNAL , 2024 , 11 (5) , 8526-8538 . |
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Efficiently obtaining the up-to-date information in the disaster-stricken area is the key to successful disaster response. Unmanned aerial vehicles (UAVs), workers and cars can collaborate to accomplish sensing tasks, such as life detection task in disaster-stricken areas. In this paper, we explicitly address the route planning for a group of agents, including UAVs, workers, and cars, with the goal of maximizing the sensing task completion rate. we propose a MARL-based heterogeneous multi-agent route planning algorithm called MANF-RL-RP. The algorithm has made targeted designs in terms of global-local dual information processing and model structure for heterogeneous multi-agent, making it effectively considers the collaboration among heterogeneous agents and the long-term impact of current decisions. Finally, we conducted detailed experiments based on the rich simulation data. In comparison to the baseline algorithms, namely Greedy-SC-RP and MANF-DNN-RP, MANF-RL-RP has exhibited a significant performance improvement. Compared to MANF-DNN-RP and Greedy-SC-RP, the task completion rate based on MANF-RL-RP increased by an average of 8.82% and 56.8%, respectively.
Keyword ：
collaborative route planning collaborative route planning disaster response disaster response Mobile crowdsensing Mobile crowdsensing mulit-agent reinforcement learning mulit-agent reinforcement learning
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| GB/T 7714 | Han, Lei , Tu, Chunyu , Yu, Zhiwen et al. Collaborative Route Planning of UAVs, Workers, and Cars for Crowdsensing in Disaster Response [J]. | IEEE-ACM TRANSACTIONS ON NETWORKING , 2024 , 32 (4) : 3606-3621 . |
| MLA | Han, Lei et al. "Collaborative Route Planning of UAVs, Workers, and Cars for Crowdsensing in Disaster Response" . | IEEE-ACM TRANSACTIONS ON NETWORKING 32 . 4 (2024) : 3606-3621 . |
| APA | Han, Lei , Tu, Chunyu , Yu, Zhiwen , Yu, Zhiyong , Shan, Weihua , Wang, Liang et al. Collaborative Route Planning of UAVs, Workers, and Cars for Crowdsensing in Disaster Response . | IEEE-ACM TRANSACTIONS ON NETWORKING , 2024 , 32 (4) , 3606-3621 . |
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Spatiotemporal Graph (STG) forecasting is an essential task within the realm of spatiotemporal data mining and urban computing. Over the past few years, Spatiotemporal Graph Neural Networks (STGNNs) have gained significant attention as promising solutions for STG forecasting. However, existing methods often overlook two issues: the dynamic spatial dependencies of urban networks and the heterogeneity of urban spatiotemporal data. In this paper, we propose a novel framework for STG learning called Dynamic Meta-Graph Convolutional Recurrent Network (DMetaGCRN), which effectively tackles both challenges. Specifically, we first build a meta graph generator to dynamically generate graph structures, which integrates various dynamic features, including input sensor signals and their historical trends, periodic information (timestamp embeddings), and meta-node embeddings. Among them, a memory network is used to guide the learning of meta-node embeddings. The meta-graph generation process enables the model to simulate the dynamic spatial dependencies of urban networks and capture data heterogeneity. Then, we design a Dynamic Meta-Graph Convolutional Recurrent Unit (DMetaGCRU) to simultaneously model spatial and temporal dependencies. Finally, we formulate the proposed DMetaGCRN in an encoder-decoder architecture built upon DMetaGCRU and meta-graph generator components. Extensive experiments on four real-world urban spatiotemporal datasets validate that the proposed DMetaGCRN framework outperforms state-of-the-art approaches.
Keyword ：
Dynamic graph generation Dynamic graph generation Heterogeneity Heterogeneity Meta-graph Meta-graph Spatiotemporal graph forecasting Spatiotemporal graph forecasting
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| GB/T 7714 | Guo, Xianwei , Yu, Zhiyong , Huang, Fangwan et al. Dynamic meta-graph convolutional recurrent network for heterogeneous spatiotemporal graph forecasting [J]. | NEURAL NETWORKS , 2024 , 181 . |
| MLA | Guo, Xianwei et al. "Dynamic meta-graph convolutional recurrent network for heterogeneous spatiotemporal graph forecasting" . | NEURAL NETWORKS 181 (2024) . |
| APA | Guo, Xianwei , Yu, Zhiyong , Huang, Fangwan , Chen, Xing , Yang, Dingqi , Wang, Jiangtao . Dynamic meta-graph convolutional recurrent network for heterogeneous spatiotemporal graph forecasting . | NEURAL NETWORKS , 2024 , 181 . |
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Integrating data from multiple parties to achieve cross-institutional machine learning is an important trend in Industry 4.0 era. However, the privacy risks from sharing data pose a significant challenge to data integration. To integrate data without sharing data and meet large-scale samples' modeling needs, we propose two vertical federation learning algorithms for ridge regression via least-squares solution for two-party and multi-party scenarios, respectively. Compared with the state-of-the-art algorithms, our algorithms only need one round of calculation for the optimization instead of iteration. Furthermore, our algorithms can effectively handle large-scale samples due to the number of cryptographic operations in our algorithms being independent of the number of samples. Through our proposed the matrix secure agent computing theory and $\delta$d-data indistinguishability theory, we provide quantitative theoretical guarantees for the security of our algorithms. Our algorithms satisfy complete data indistinguishability under the "semi-honest" assumption and the quantitative security under the "malicious" assumption. The experiments show that our proposed algorithm takes only about 400 seconds to handle up to 9.6 million large-scale samples, while the state-of-the-art algorithms take close to 1000 seconds to handle every 1000 samples, which embodies the advantage of our algorithms in handling large-scale samples.
Keyword ：
Cryptography Cryptography Data models Data models Federated learning Federated learning Industry 4.0 Industry 4.0 Information sharing Information sharing least-squares solution least-squares solution Machine learning Machine learning Protocols Protocols ridge regression ridge regression Security Security vertical federation learning vertical federation learning
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| GB/T 7714 | Cai, Jianping , Liu, Ximeng , Yu, Zhiyong et al. Efficient Vertical Federated Learning Method for Ridge Regression of Large-Scale Samples [J]. | IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTING , 2023 , 11 (2) : 511-526 . |
| MLA | Cai, Jianping et al. "Efficient Vertical Federated Learning Method for Ridge Regression of Large-Scale Samples" . | IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTING 11 . 2 (2023) : 511-526 . |
| APA | Cai, Jianping , Liu, Ximeng , Yu, Zhiyong , Guo, Kun , Li, Jiayin . Efficient Vertical Federated Learning Method for Ridge Regression of Large-Scale Samples . | IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTING , 2023 , 11 (2) , 511-526 . |
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