Recently,deep learning based city flow prediction has been extensively used in the establishment of smartcities.These methods are data-hungry,making them unscalable to areas lacking data.Although transfer learningcan ...Recently,deep learning based city flow prediction has been extensively used in the establishment of smartcities.These methods are data-hungry,making them unscalable to areas lacking data.Although transfer learningcan use data-rich source domains to assist target domain cities in city flow prediction,the performance of existingmethods cannot meet the needs of actual use,because the long-distance road network connectivity is ignored.Tosolve this problem,we propose a transfer learning method based on spatiotemporal graph convolution,in which weconstruct a co-occurrence space between the source and target domains,and then align the mapping of the sourceand target domains’data in this space,to achieve the transfer learning of the source city flow prediction modelon the target domain.Specifically,a dynamic spatiotemporal graph convolution module along with a temporalencoder is devised to simultaneously capture the concurrent spatiotemporal features,which implies the inherentrelationship among the road network structures,human travel habits,and city bike flow.Then,these concurrentfeatures are leveraged as cross-city invariant representations and nonlinearly spanned to a co-occurrence space.Thetarget domain features are thereby aligned with the source domain features in the co-occurrence space by using aMahalanobis distance loss,to achieve cross-city bike flow prediction.The proposed method is evaluated on the publicbike flow datasets in Chicago,New York,and Washington in 2015,and significantly outperforms state-of-the-arttechniques.展开更多
Efficient and precise traffic flow prediction is highly important in effective traffic management.This research presents a novel prediction model that integrates highway spatial changes and flow-related information(sp...Efficient and precise traffic flow prediction is highly important in effective traffic management.This research presents a novel prediction model that integrates highway spatial changes and flow-related information(speed and vehicle composition).The highway is divided into segments,using key reference points like tunnels,toll stations and ramps.An adaptive graph convolutional network is employed to capture relationships between these segments.The network automatically adjusts adjacency matrix weights,facilitating the extraction of spatial flow features.Incorporating flow-related information,a multi-task module attention fusion network is introduced.The main task is traffic flow prediction,with average travel speed and vehicle composition as auxiliary tasks.This approach enhances feature acquisition and improves prediction accuracy.In experiments using Fuzhou–Jingtan Expressway data,the model significantly enhances prediction accuracy by at least 55%.Ablation experiments validate the effectiveness of the designed modules,improving the model’s accuracy from 20%to 45%.展开更多
The rapid growth and increasing complexity of Internet of Things(IoT)devices have made network intrusion detection a critical challenge,especially in edge computing environments where data privacy is a primary concern...The rapid growth and increasing complexity of Internet of Things(IoT)devices have made network intrusion detection a critical challenge,especially in edge computing environments where data privacy is a primary concern.Machine learning-based intrusion detection techniques enhance IoT network security but often require centralized network data,posing significant risks to data privacy and security.Although federated learning(FL)-based network intrusion detection methods have emerged in recent years to address privacy concerns,they have not fully leveraged the advantages of graph neural networks(GNNs)for intrusion detection.To address this issue,we propose a federated spatiotemporal graph convolutional network(FedSTGCN)model,which integrates the capabilities of spatiotemporal GNNs(STGNNs)and federated learning.This framework enables collaborative model training across distributed IoT devices without requiring the sharing of raw data,thereby improving network intrusion detection accuracy while preserving data privacy.Extensive experiments are conducted on two widely used IoT intrusion detection datasets to evaluate the effectiveness of the proposed approach.The results demonstrate that FedSTGCN outperforms other methods in both binary and multiclass classification tasks,achieving over 97%accuracy in binary classification tasks and over 92%weighted F1-score in multiclass classification tasks.展开更多
Being a kind of non-Euclidean data,spatiotemporal graph data exists everywhere from trafficflow,air quality index to crime case,etc.Unlike the raster data,the irregular and disordered characteristics of spatiotemporal...Being a kind of non-Euclidean data,spatiotemporal graph data exists everywhere from trafficflow,air quality index to crime case,etc.Unlike the raster data,the irregular and disordered characteristics of spatiotemporal graph data have attracted the research interest of scholars,with the prediction of spatiotemporal graph data being one of the research hot spots.The emergence of spatiotemporal graph neural networks(ST-GNNs)provides a new insight for solving the problem of obtaining spatial correlation for spatiotemporal graph data prediction while achieving state-of-the-art performance.In this paper,comprehensive survey of research on ST-GNNs prediction domain isa presented,where the background of ST-GNNs is introduced before the computational paradigm of ST-GNN is thoroughly reviewed.From the perspective of model construction,59 well-known models in recent years are classified and discussed.Some of these models are further analyzed in terms of performance and efficiency.Subsequently,the categories and applicationfields of spatiotemporal graph data are summarized,providing a clear idea of technology selection for different applications.Finally,the evolution history and future direction of ST-GNNs are also summarized,to facilitate future researchers to timely understand the current state of prediction research by ST-GNNs.展开更多
By modeling the spatiotemporal data of the power grid, it is possible to better understand its operational status, identify potential issues and risks, and take timely measures to adjust and optimize the system. Compa...By modeling the spatiotemporal data of the power grid, it is possible to better understand its operational status, identify potential issues and risks, and take timely measures to adjust and optimize the system. Compared to the bus-branch model, the node-breaker model provides higher granularity in describing grid components and can dynamically reflect changes in equipment status, thus improving the efficiency of grid dispatching and operation. This paper proposes a spatiotemporal data modeling method based on a graph database. It elaborates on constructing graph nodes, graph ontology models, and graph entity models from grid dispatch data, describing the construction of the spatiotemporal node-breaker graph model and the transformation to the bus-branch model. Subsequently, by integrating spatiotemporal data attributes into the pre-built static grid graph model, a spatiotemporal evolving graph of the power grid is constructed. Furthermore, the concept of the “Power Grid One Graph” and its requirements in modern power systems are elucidated. Leveraging the constructed spatiotemporal node-breaker graph model and graph computing technology, the paper explores the feasibility of grid situational awareness. Finally, typical applications in an operational provincial grid are showcased, and potential scenarios of the proposed spatiotemporal graph model are discussed.展开更多
基金supported by the National Natural Science Foundation of China(Nos.62103124 and 62033012)the Major Special Science and Technology Project of Anhui Province,China(No.202003a07020009)the Open Project Program of Key Laboratory of Ministry of Education of System Control and Information Processing,China(No.SCIP20230109)。
文摘Recently,deep learning based city flow prediction has been extensively used in the establishment of smartcities.These methods are data-hungry,making them unscalable to areas lacking data.Although transfer learningcan use data-rich source domains to assist target domain cities in city flow prediction,the performance of existingmethods cannot meet the needs of actual use,because the long-distance road network connectivity is ignored.Tosolve this problem,we propose a transfer learning method based on spatiotemporal graph convolution,in which weconstruct a co-occurrence space between the source and target domains,and then align the mapping of the sourceand target domains’data in this space,to achieve the transfer learning of the source city flow prediction modelon the target domain.Specifically,a dynamic spatiotemporal graph convolution module along with a temporalencoder is devised to simultaneously capture the concurrent spatiotemporal features,which implies the inherentrelationship among the road network structures,human travel habits,and city bike flow.Then,these concurrentfeatures are leveraged as cross-city invariant representations and nonlinearly spanned to a co-occurrence space.Thetarget domain features are thereby aligned with the source domain features in the co-occurrence space by using aMahalanobis distance loss,to achieve cross-city bike flow prediction.The proposed method is evaluated on the publicbike flow datasets in Chicago,New York,and Washington in 2015,and significantly outperforms state-of-the-arttechniques.
基金supported by National Science and Technology Major Project(Grant No.2022ZD0115501)National Key R&D Program of China(Grant No.2022YFF0604905).
文摘Efficient and precise traffic flow prediction is highly important in effective traffic management.This research presents a novel prediction model that integrates highway spatial changes and flow-related information(speed and vehicle composition).The highway is divided into segments,using key reference points like tunnels,toll stations and ramps.An adaptive graph convolutional network is employed to capture relationships between these segments.The network automatically adjusts adjacency matrix weights,facilitating the extraction of spatial flow features.Incorporating flow-related information,a multi-task module attention fusion network is introduced.The main task is traffic flow prediction,with average travel speed and vehicle composition as auxiliary tasks.This approach enhances feature acquisition and improves prediction accuracy.In experiments using Fuzhou–Jingtan Expressway data,the model significantly enhances prediction accuracy by at least 55%.Ablation experiments validate the effectiveness of the designed modules,improving the model’s accuracy from 20%to 45%.
文摘The rapid growth and increasing complexity of Internet of Things(IoT)devices have made network intrusion detection a critical challenge,especially in edge computing environments where data privacy is a primary concern.Machine learning-based intrusion detection techniques enhance IoT network security but often require centralized network data,posing significant risks to data privacy and security.Although federated learning(FL)-based network intrusion detection methods have emerged in recent years to address privacy concerns,they have not fully leveraged the advantages of graph neural networks(GNNs)for intrusion detection.To address this issue,we propose a federated spatiotemporal graph convolutional network(FedSTGCN)model,which integrates the capabilities of spatiotemporal GNNs(STGNNs)and federated learning.This framework enables collaborative model training across distributed IoT devices without requiring the sharing of raw data,thereby improving network intrusion detection accuracy while preserving data privacy.Extensive experiments are conducted on two widely used IoT intrusion detection datasets to evaluate the effectiveness of the proposed approach.The results demonstrate that FedSTGCN outperforms other methods in both binary and multiclass classification tasks,achieving over 97%accuracy in binary classification tasks and over 92%weighted F1-score in multiclass classification tasks.
基金supported by National Social Science Fund of China[grant number 21JCA004]Soft Science Research Project of Ministry of Housing and Urban-Rural Development of China[grant number R20200287]Open Research Fund of Key Laboratory of Digital Cartography and Land Information Application,Ministry of Natural Resources[grant number ZRZYBWD202102].
文摘Being a kind of non-Euclidean data,spatiotemporal graph data exists everywhere from trafficflow,air quality index to crime case,etc.Unlike the raster data,the irregular and disordered characteristics of spatiotemporal graph data have attracted the research interest of scholars,with the prediction of spatiotemporal graph data being one of the research hot spots.The emergence of spatiotemporal graph neural networks(ST-GNNs)provides a new insight for solving the problem of obtaining spatial correlation for spatiotemporal graph data prediction while achieving state-of-the-art performance.In this paper,comprehensive survey of research on ST-GNNs prediction domain isa presented,where the background of ST-GNNs is introduced before the computational paradigm of ST-GNN is thoroughly reviewed.From the perspective of model construction,59 well-known models in recent years are classified and discussed.Some of these models are further analyzed in terms of performance and efficiency.Subsequently,the categories and applicationfields of spatiotemporal graph data are summarized,providing a clear idea of technology selection for different applications.Finally,the evolution history and future direction of ST-GNNs are also summarized,to facilitate future researchers to timely understand the current state of prediction research by ST-GNNs.
基金supported by the Project of China Southern Power Grid Digital Grid Research Institute Co.,Ltd.(210002KK52222026)。
文摘By modeling the spatiotemporal data of the power grid, it is possible to better understand its operational status, identify potential issues and risks, and take timely measures to adjust and optimize the system. Compared to the bus-branch model, the node-breaker model provides higher granularity in describing grid components and can dynamically reflect changes in equipment status, thus improving the efficiency of grid dispatching and operation. This paper proposes a spatiotemporal data modeling method based on a graph database. It elaborates on constructing graph nodes, graph ontology models, and graph entity models from grid dispatch data, describing the construction of the spatiotemporal node-breaker graph model and the transformation to the bus-branch model. Subsequently, by integrating spatiotemporal data attributes into the pre-built static grid graph model, a spatiotemporal evolving graph of the power grid is constructed. Furthermore, the concept of the “Power Grid One Graph” and its requirements in modern power systems are elucidated. Leveraging the constructed spatiotemporal node-breaker graph model and graph computing technology, the paper explores the feasibility of grid situational awareness. Finally, typical applications in an operational provincial grid are showcased, and potential scenarios of the proposed spatiotemporal graph model are discussed.
