Characteristics of the Internet traffic data flow are studied based on the chaos theory. A phase space that is isometric with the network dynamic system is reconstructed by using the single variable time series of a n...Characteristics of the Internet traffic data flow are studied based on the chaos theory. A phase space that is isometric with the network dynamic system is reconstructed by using the single variable time series of a network flow. Some parameters, such as the correlative dimension and the Lyapunov exponent are calculated, and the chaos characteristic is proved to exist in Internet traffic data flows. A neural network model is construct- ed based on radial basis function （RBF） to forecast actual Internet traffic data flow. Simulation results show that, compared with other forecasts of the forward-feedback neural network, the forecast of the RBF neural network based on the chaos theory has faster learning capacity and higher forecasting accuracy.展开更多
Accurate prediction of road traffic flow is a significant part in the intelligent transportation systems.Accurate prediction can alleviate traffic congestion,and reduce environmental pollution.For the management depar...Accurate prediction of road traffic flow is a significant part in the intelligent transportation systems.Accurate prediction can alleviate traffic congestion,and reduce environmental pollution.For the management department,it can make effective use of road resources.For individuals,it can help people plan their own travel paths,avoid congestion,and save time.Owing to complex factors on the road,such as damage to the detector and disturbances from environment,the measured traffic volume can contain noise.Reducing the influence of noise on traffic flow prediction is a piece of very important work.Therefore,in this paper we propose a combination algorithm of denoising and BILSTM to effectively improve the performance of traffic flow prediction.At the same time,three denoising algorithms are compared to find the best combination mode.In this paper,the wavelet(WL) denoising scheme,the empirical mode decomposition(EMD) denoising scheme,and the ensemble empirical mode decomposition(EEMD) denoising scheme are all introduced to suppress outliers in traffic flow data.In addition,we combine the denoising schemes with bidirectional long short-term memory(BILSTM)network to predict the traffic flow.The data in this paper are cited from performance measurement system(PeMS).We choose three kinds of road data(mainline,off ramp,on ramp) to predict traffic flow.The results for mainline show that data denoising can improve prediction accuracy.Moreover,prediction accuracy of BILSTM+EEMD scheme is the highest in the three methods(BILSTM+WL,BILSTM+EMD,BILSTM+EEMD).The results for off ramp and on ramp show the same performance as the results for mainline.It is indicated that this model is suitable for different road sections and long-term prediction.展开更多
Named-data Networking(NDN) is a promising future Internet architecture, which introduces some evolutionary elements into layer-3, e.g., consumer-driven communication, soft state on data forwarding plane and hop-byhop ...Named-data Networking(NDN) is a promising future Internet architecture, which introduces some evolutionary elements into layer-3, e.g., consumer-driven communication, soft state on data forwarding plane and hop-byhop traffic control. And those elements ensure data holders to solely return the requested data within the lifetime of the request, instead of pushing data whenever needed and whatever it is. Despite the dispute on the advantages and their prices, this pattern requires data consumers to keep sending requests at the right moments for continuous data transmission, resulting in significant forwarding cost and sophisticated application design. In this paper, we propose Interest Set(IS) mechanism, which compresses a set of similar Interests into one request, and maintains a relative long-term data returning path with soft state and continuous feedback from upstream. In this way, IS relaxes the above requirement, and scales NDN data forwarding by reducing forwarded requests and soft states that are needed to retrieve a given set of data.展开更多
文摘Characteristics of the Internet traffic data flow are studied based on the chaos theory. A phase space that is isometric with the network dynamic system is reconstructed by using the single variable time series of a network flow. Some parameters, such as the correlative dimension and the Lyapunov exponent are calculated, and the chaos characteristic is proved to exist in Internet traffic data flows. A neural network model is construct- ed based on radial basis function （RBF） to forecast actual Internet traffic data flow. Simulation results show that, compared with other forecasts of the forward-feedback neural network, the forecast of the RBF neural network based on the chaos theory has faster learning capacity and higher forecasting accuracy.
基金Project supported by the Program of Humanities and Social Science of the Education Ministry of China(Grant No.20YJA630008)the Natural Science Foundation of Zhejiang Province,China(Grant No.LY20G010004)the K C Wong Magna Fund in Ningbo University,China。
文摘Accurate prediction of road traffic flow is a significant part in the intelligent transportation systems.Accurate prediction can alleviate traffic congestion,and reduce environmental pollution.For the management department,it can make effective use of road resources.For individuals,it can help people plan their own travel paths,avoid congestion,and save time.Owing to complex factors on the road,such as damage to the detector and disturbances from environment,the measured traffic volume can contain noise.Reducing the influence of noise on traffic flow prediction is a piece of very important work.Therefore,in this paper we propose a combination algorithm of denoising and BILSTM to effectively improve the performance of traffic flow prediction.At the same time,three denoising algorithms are compared to find the best combination mode.In this paper,the wavelet(WL) denoising scheme,the empirical mode decomposition(EMD) denoising scheme,and the ensemble empirical mode decomposition(EEMD) denoising scheme are all introduced to suppress outliers in traffic flow data.In addition,we combine the denoising schemes with bidirectional long short-term memory(BILSTM)network to predict the traffic flow.The data in this paper are cited from performance measurement system(PeMS).We choose three kinds of road data(mainline,off ramp,on ramp) to predict traffic flow.The results for mainline show that data denoising can improve prediction accuracy.Moreover,prediction accuracy of BILSTM+EEMD scheme is the highest in the three methods(BILSTM+WL,BILSTM+EMD,BILSTM+EEMD).The results for off ramp and on ramp show the same performance as the results for mainline.It is indicated that this model is suitable for different road sections and long-term prediction.
基金supported by the National Hightech R&D Program ("863" Program) of China (No.2013AA013505)the National Science Foundation of China (No.61472213)
文摘Named-data Networking(NDN) is a promising future Internet architecture, which introduces some evolutionary elements into layer-3, e.g., consumer-driven communication, soft state on data forwarding plane and hop-byhop traffic control. And those elements ensure data holders to solely return the requested data within the lifetime of the request, instead of pushing data whenever needed and whatever it is. Despite the dispute on the advantages and their prices, this pattern requires data consumers to keep sending requests at the right moments for continuous data transmission, resulting in significant forwarding cost and sophisticated application design. In this paper, we propose Interest Set(IS) mechanism, which compresses a set of similar Interests into one request, and maintains a relative long-term data returning path with soft state and continuous feedback from upstream. In this way, IS relaxes the above requirement, and scales NDN data forwarding by reducing forwarded requests and soft states that are needed to retrieve a given set of data.
