The phenomenon of data explosion represents a severe challenge for the upcoming big data era.However,the current Internet architecture is insufficient for dealing with a huge amount of traffic owing to an increase in ...The phenomenon of data explosion represents a severe challenge for the upcoming big data era.However,the current Internet architecture is insufficient for dealing with a huge amount of traffic owing to an increase in redundant content transmission and the end-point-based communication model.Information-centric networking(ICN)is a paradigm for the future Internet that can be utilized to resolve the data explosion problem.In this paper,we focus on content-centric networking(CCN),one of the key candidate ICN architectures.CCN has been studied in various network environments with the aim of relieving network and server burden,especially in name-based forwarding and in-network caching functionalities.This paper studies the effect of several caching strategies in the CCN domain from the perspective of network and server overhead.Thus,we comprehensively analyze the in-network caching performance of CCN under several popular cache replication methods(i.e.,cache placement).We evaluate the performance with respect to wellknown Internet traffic patterns that follow certain probabilistic distributions,such as the Zipf/Mandelbrot–Zipf distributions,and flashcrowds.For the experiments,we developed an OPNET-based CCN simulator with a realistic Internet-like topology.展开更多
Pattern matching is a fundamental approach to detect malicious behaviors and information over Internet, which has been gradually used in high-speed network traffic analysis. However, there is a performance bottleneck ...Pattern matching is a fundamental approach to detect malicious behaviors and information over Internet, which has been gradually used in high-speed network traffic analysis. However, there is a performance bottleneck for multi-pattern matching on online compressed network traffic(CNT), this is because malicious and intrusion codes are often embedded into compressed network traffic. In this paper, we propose an online fast and multi-pattern matching algorithm on compressed network traffic(FMMCN). FMMCN employs two types of jumping, i.e. jumping during sliding window and a string jump scanning strategy to skip unnecessary compressed bytes. Moreover, FMMCN has the ability to efficiently process multiple large volume of networks such as HTTP traffic, vehicles traffic, and other Internet-based services. The experimental results show that FMMCN can ignore more than 89.5% of bytes, and its maximum speed reaches 176.470MB/s in a midrange switches device, which is faster than the current fastest algorithm ACCH by almost 73.15 MB/s.展开更多
Introduction: In 21st century, road traffic accidents (RTA) are considered as increasing epidemic of non-communicable disease which is abandoned and needs special attention to prevent them. The aim of this study was t...Introduction: In 21st century, road traffic accidents (RTA) are considered as increasing epidemic of non-communicable disease which is abandoned and needs special attention to prevent them. The aim of this study was to assess the factors and pattern of injuries associated with road traffic accidents. Methods: A cross sectional study was conducted among 112 RTA victims and 56 drivers in Palpa District of Nepal. The association of factors and pattern of injuries with exposure to accidents was assessed using Fisher’s exact test. Bivariate logistic regression examined the association between driving and socio-demographics factors and exposure to road accidents. Results: Of 112 RTA victims, 50% were in the age group of 21 to 40 years and 71.4% were male. Drivers who were in the age less than or equal to 30 years were more likely (OR: 3.6;95% CI: 1.0, 14.3) to expose to an accident than those who were above 30 years. Similarly, those having driving speed less than 40 km/hr were less likely to expose to an accident than those with speed 40 - 60 km/hr (OR: 6.0;95% CI: 0.8, 73.5) and those with speed more than 60 km/hr (OR 7.8;95% CI: 1.0, 100.1). Moreover, the driving experience was also found positively associated (OR: 5.6;95% CI: 1.1, 35.5) with the exposure to an accident. Conclusion: Being in younger age group, male gender, morning time, the driving speed, driving experiences, and driving hours on the road were positively associated with RTA. The efforts should be made to enforce laws in control of speed targeting experienced drivers and those with younger age groups.展开更多
The paper covers analysis and investigation of lighting automation system in low-traffic long-roads. The main objective is to provide optimal solution between expensive safe design that utilizes continuous street ligh...The paper covers analysis and investigation of lighting automation system in low-traffic long-roads. The main objective is to provide optimal solution between expensive safe design that utilizes continuous street lighting system at night for the entire road, or inexpensive design that sacrifices the safety, relying on using vehicles lighting, to eliminate the problem of high cost energy consumption during the night operation of the road. By taking into account both of these factors, smart lighting automation system is proposed using Pattern Recognition Technique applied on vehicle number-plates. In this proposal, the road is sectionalized into zones, and based on smart Pattern Recognition Technique, the control system of the road lighting illuminates only the zone that the vehicles pass through. Economic analysis is provided in this paper to support the value of using this design of lighting control system.展开更多
The rapid development of mobile network brings opportunities for researchers to analyze user behaviors based on largescale network traffic data. It is important for Internet Service Providers(ISP) to optimize resource...The rapid development of mobile network brings opportunities for researchers to analyze user behaviors based on largescale network traffic data. It is important for Internet Service Providers(ISP) to optimize resource allocation and provide customized services to users. The first step of analyzing user behaviors is to extract information of user actions from HTTP traffic data by multi-pattern URL matching. However, the efficiency is a huge problem when performing this work on massive network traffic data. To solve this problem, we propose a novel and accurate algorithm named Multi-Pattern Parallel Matching(MPPM) that takes advantage of HashMap in data searching for extracting user behaviors from big network data more effectively. Extensive experiments based on real-world traffic data prove the ability of MPPM algorithm to deal with massive HTTP traffic with better performance on accuracy, concurrency and efficiency. We expect the proposed algorithm and it parallelized implementation would be a solid base to build a high-performance analysis engine of user behavior based on massive HTTP traffic data processing.展开更多
Early detection and rapid resolution network congestion can considerably improve network capacity. Consequently, much research has been carried out on predicting traff ic patterns in 3G networks. This paper introduces...Early detection and rapid resolution network congestion can considerably improve network capacity. Consequently, much research has been carried out on predicting traff ic patterns in 3G networks. This paper introduces an access point centric approach that is implemented by two prediction models, the traffic abstraction model and the order-k Markov model. Traffi c predictions are carried out to support the congestion control in the semi-smart antenna systems. The simulation result shows that the cumulative error rate is below 25% even carrying out multi-step-ahead predictions.展开更多
Travel time through a ring road with a total length of 80 km has been predicted by a viscoelastic traffic model(VEM), which is developed in analogous to the non-Newtonian fluid flow. The VEM expresses a traffic pressu...Travel time through a ring road with a total length of 80 km has been predicted by a viscoelastic traffic model(VEM), which is developed in analogous to the non-Newtonian fluid flow. The VEM expresses a traffic pressure for the unfree flow case by space headway, ensuring that the pressure can be determined by the assumption that the relevant second critical sound speed is exactly equal to the disturbance propagation speed determined by the free flow speed and the braking distance measured by the average vehicular length. The VEM assumes that the sound speed for the free flow case depends on the traffic density in some specific aspects, which ensures that it is exactly identical to the free flow speed on an empty road. To make a comparison, the open Navier-Stokes type model developed by Zhang(ZHANG, H. M. Driver memory, traffic viscosity and a viscous vehicular traffic flow model. Transp. Res. Part B, 37, 27–41(2003)) is adopted to predict the travel time through the ring road for providing the counterpart results.When the traffic free flow speed is 80 km/h, the braking distance is supposed to be 45 m,with the jam density uniquely determined by the average length of vehicles l ≈ 5.8 m. To avoid possible singular points in travel time prediction, a distinguishing period for time averaging is pre-assigned to be 7.5 minutes. It is found that the travel time increases monotonically with the initial traffic density on the ring road. Without ramp effects, for the ring road with the initial density less than the second critical density, the travel time can be simply predicted by using the equilibrium speed. However, this simpler approach is unavailable for scenarios over the second critical.展开更多
WiFi has become one of the most popular ways to access the Internet.However,in large-scale campus wireless networks,it is challenging for network administrators to provide optimized access quality without knowledge on...WiFi has become one of the most popular ways to access the Internet.However,in large-scale campus wireless networks,it is challenging for network administrators to provide optimized access quality without knowledge on fine-grained traffic characteristics and real network performance.In this paper,we implement PerfMon,a network performance measurement and diagnosis system,which integrates collected multi-source datasets and analysis methods.Based on PerfMon,we first conduct a comprehensive measurement on application-level traffic patterns and behaviors from multiple dimensions in the wireless network of T university(TWLAN),which is one of the largest campus wireless networks.Then we systematically study the application-level network performance.We observe that the application-level traffic behaviors and performance vary greatly across different locations and device types.The performance is far from satisfactory in some cases.To diagnose these problems,we distinguish locations and device types,and further locate the most crucial factors that affect the performance.The results of case studies show that the influential factors can effectively characterize performance changes and explain for performance degradation.展开更多
Over the past decade, automatic traffic accident recognition has become a prominent objective in the area of machine vision and pattern recognition because of its immense application potential in developing autonomous...Over the past decade, automatic traffic accident recognition has become a prominent objective in the area of machine vision and pattern recognition because of its immense application potential in developing autonomous Intelligent Transportation Systems (ITS). In this paper, we present a new framework toward a real-time automated recognition of traffic accident based on the Histogram of Flow Gradient (HFG) and statistical logistic regression analysis. First, optical flow is estimated and the HFG is constructed from video shots. Then vehicle patterns are clustered based on the HFG-features. By using logistic regression analysis to fit data to logistic curves, the classifier model is generated. Finally, the trajectory of the vehicle by which the accident was occasioned, is determined and recorded. The experimental results on real video sequences demonstrate the efficiency and the applicability of the framework and show it is of higher robustness and can comfortably provide latency guarantees to real-time surveillance and traffic monitoring applications.展开更多
This paper examines the noise and rotation resistance capacity of Hopfield Neural Network (HNN) given four corrupted traffic sign images. In the study, Signal-to-Noise Ratio (SNR), recall rate and pattern complexity a...This paper examines the noise and rotation resistance capacity of Hopfield Neural Network (HNN) given four corrupted traffic sign images. In the study, Signal-to-Noise Ratio (SNR), recall rate and pattern complexity are defined and employed to evaluate the recall performance. The experimental results indicate that the HNN possesses significant recall capacity against the strong noise corruption, and certain restoring competence to the rotation. It is also found that combining noise with rotation does not further challenge the HNN corruption resistance capability as the noise or rotation alone does.展开更多
This letter reports traffic flow sensitivity to visco-elasticity, with the traffic flow modeling briefly described at first and then used to do traffic flow simulations whose results can reflect the properties of spat...This letter reports traffic flow sensitivity to visco-elasticity, with the traffic flow modeling briefly described at first and then used to do traffic flow simulations whose results can reflect the properties of spatial-temporal evolution of ring traffic flow. It reveals that visco-elasticity plays crucial role in formation of traffic flow patterns, implying that self-organization of traffic flow is crucial in determining traffic flow status.展开更多
The traffic activity offifth generation(5G)networks demand for new energy management techniques that is dynamic deep and longer duration of sleep as compared to the fourth generation(4G)network technologies that deman...The traffic activity offifth generation(5G)networks demand for new energy management techniques that is dynamic deep and longer duration of sleep as compared to the fourth generation(4G)network technologies that demand always for varied control and data signalling based on control base station(CBS)and data base station(DBS).Hence,this paper discusses the energy management in wireless cellular networks using wide range of control for twice the reduction in energy conservation in non-standalone deployment of 5G network.As the new radio(NR)based 5G network is configured to transmit signal blocks for every 20 ms,the proposed algorithm implements withstanding capacity of on or off based energy switching,which in-turn operates in wide range control by carrying out reduced computational complexity.The proposed Wide range of control for base station in green cellular network using sleep mode for switch(WGCNS)algorithm toon and off the base station will work in heavy load with neighbouring base station.For reducing the overhead duration in air,heuristic versions of the algorithm are proposed at the base station.The algorithm operates based on the specification with suggested protocol-level to give best amount of energy savings.The proposed algorithm reduces 40%to 83%of residual energy based on the traffic pattern of the urban scenario.展开更多
基金supported by Basic Science Research Program through the National Research Foundation of Korea(NRF)funded by the Ministry of Education(2014R1A1A2057796)and(2015R1D1A1A01059049)
文摘The phenomenon of data explosion represents a severe challenge for the upcoming big data era.However,the current Internet architecture is insufficient for dealing with a huge amount of traffic owing to an increase in redundant content transmission and the end-point-based communication model.Information-centric networking(ICN)is a paradigm for the future Internet that can be utilized to resolve the data explosion problem.In this paper,we focus on content-centric networking(CCN),one of the key candidate ICN architectures.CCN has been studied in various network environments with the aim of relieving network and server burden,especially in name-based forwarding and in-network caching functionalities.This paper studies the effect of several caching strategies in the CCN domain from the perspective of network and server overhead.Thus,we comprehensively analyze the in-network caching performance of CCN under several popular cache replication methods(i.e.,cache placement).We evaluate the performance with respect to wellknown Internet traffic patterns that follow certain probabilistic distributions,such as the Zipf/Mandelbrot–Zipf distributions,and flashcrowds.For the experiments,we developed an OPNET-based CCN simulator with a realistic Internet-like topology.
基金supported by China MOST project (No.2012BAH46B04)
文摘Pattern matching is a fundamental approach to detect malicious behaviors and information over Internet, which has been gradually used in high-speed network traffic analysis. However, there is a performance bottleneck for multi-pattern matching on online compressed network traffic(CNT), this is because malicious and intrusion codes are often embedded into compressed network traffic. In this paper, we propose an online fast and multi-pattern matching algorithm on compressed network traffic(FMMCN). FMMCN employs two types of jumping, i.e. jumping during sliding window and a string jump scanning strategy to skip unnecessary compressed bytes. Moreover, FMMCN has the ability to efficiently process multiple large volume of networks such as HTTP traffic, vehicles traffic, and other Internet-based services. The experimental results show that FMMCN can ignore more than 89.5% of bytes, and its maximum speed reaches 176.470MB/s in a midrange switches device, which is faster than the current fastest algorithm ACCH by almost 73.15 MB/s.
基金This work is supported by the following projects: National Natural Science Foundation of China grant 60772136, 111 Development Program of China NO.B08038, National Science & Technology Pillar Program of China NO.2008BAH22B03 and NO. 2007BAH08B01.
文摘Introduction: In 21st century, road traffic accidents (RTA) are considered as increasing epidemic of non-communicable disease which is abandoned and needs special attention to prevent them. The aim of this study was to assess the factors and pattern of injuries associated with road traffic accidents. Methods: A cross sectional study was conducted among 112 RTA victims and 56 drivers in Palpa District of Nepal. The association of factors and pattern of injuries with exposure to accidents was assessed using Fisher’s exact test. Bivariate logistic regression examined the association between driving and socio-demographics factors and exposure to road accidents. Results: Of 112 RTA victims, 50% were in the age group of 21 to 40 years and 71.4% were male. Drivers who were in the age less than or equal to 30 years were more likely (OR: 3.6;95% CI: 1.0, 14.3) to expose to an accident than those who were above 30 years. Similarly, those having driving speed less than 40 km/hr were less likely to expose to an accident than those with speed 40 - 60 km/hr (OR: 6.0;95% CI: 0.8, 73.5) and those with speed more than 60 km/hr (OR 7.8;95% CI: 1.0, 100.1). Moreover, the driving experience was also found positively associated (OR: 5.6;95% CI: 1.1, 35.5) with the exposure to an accident. Conclusion: Being in younger age group, male gender, morning time, the driving speed, driving experiences, and driving hours on the road were positively associated with RTA. The efforts should be made to enforce laws in control of speed targeting experienced drivers and those with younger age groups.
文摘The paper covers analysis and investigation of lighting automation system in low-traffic long-roads. The main objective is to provide optimal solution between expensive safe design that utilizes continuous street lighting system at night for the entire road, or inexpensive design that sacrifices the safety, relying on using vehicles lighting, to eliminate the problem of high cost energy consumption during the night operation of the road. By taking into account both of these factors, smart lighting automation system is proposed using Pattern Recognition Technique applied on vehicle number-plates. In this proposal, the road is sectionalized into zones, and based on smart Pattern Recognition Technique, the control system of the road lighting illuminates only the zone that the vehicles pass through. Economic analysis is provided in this paper to support the value of using this design of lighting control system.
基金supported in part by National Natural Science Foundation of China(61671078)the Director Funds of Beijing Key Laboratory of Network System Architecture and Convergence(2017BKL-NSACZJ-06)
文摘The rapid development of mobile network brings opportunities for researchers to analyze user behaviors based on largescale network traffic data. It is important for Internet Service Providers(ISP) to optimize resource allocation and provide customized services to users. The first step of analyzing user behaviors is to extract information of user actions from HTTP traffic data by multi-pattern URL matching. However, the efficiency is a huge problem when performing this work on massive network traffic data. To solve this problem, we propose a novel and accurate algorithm named Multi-Pattern Parallel Matching(MPPM) that takes advantage of HashMap in data searching for extracting user behaviors from big network data more effectively. Extensive experiments based on real-world traffic data prove the ability of MPPM algorithm to deal with massive HTTP traffic with better performance on accuracy, concurrency and efficiency. We expect the proposed algorithm and it parallelized implementation would be a solid base to build a high-performance analysis engine of user behavior based on massive HTTP traffic data processing.
文摘Early detection and rapid resolution network congestion can considerably improve network capacity. Consequently, much research has been carried out on predicting traff ic patterns in 3G networks. This paper introduces an access point centric approach that is implemented by two prediction models, the traffic abstraction model and the order-k Markov model. Traffi c predictions are carried out to support the congestion control in the semi-smart antenna systems. The simulation result shows that the cumulative error rate is below 25% even carrying out multi-step-ahead predictions.
基金Project supported by the Russian Foundation for Basic Research(No.18-07-00518)the National Natural Science Foundation of China(No.10972212)
文摘Travel time through a ring road with a total length of 80 km has been predicted by a viscoelastic traffic model(VEM), which is developed in analogous to the non-Newtonian fluid flow. The VEM expresses a traffic pressure for the unfree flow case by space headway, ensuring that the pressure can be determined by the assumption that the relevant second critical sound speed is exactly equal to the disturbance propagation speed determined by the free flow speed and the braking distance measured by the average vehicular length. The VEM assumes that the sound speed for the free flow case depends on the traffic density in some specific aspects, which ensures that it is exactly identical to the free flow speed on an empty road. To make a comparison, the open Navier-Stokes type model developed by Zhang(ZHANG, H. M. Driver memory, traffic viscosity and a viscous vehicular traffic flow model. Transp. Res. Part B, 37, 27–41(2003)) is adopted to predict the travel time through the ring road for providing the counterpart results.When the traffic free flow speed is 80 km/h, the braking distance is supposed to be 45 m,with the jam density uniquely determined by the average length of vehicles l ≈ 5.8 m. To avoid possible singular points in travel time prediction, a distinguishing period for time averaging is pre-assigned to be 7.5 minutes. It is found that the travel time increases monotonically with the initial traffic density on the ring road. Without ramp effects, for the ring road with the initial density less than the second critical density, the travel time can be simply predicted by using the equilibrium speed. However, this simpler approach is unavailable for scenarios over the second critical.
基金supported by the National Key Research and Development Program of China(No.2020YFE0200500)。
文摘WiFi has become one of the most popular ways to access the Internet.However,in large-scale campus wireless networks,it is challenging for network administrators to provide optimized access quality without knowledge on fine-grained traffic characteristics and real network performance.In this paper,we implement PerfMon,a network performance measurement and diagnosis system,which integrates collected multi-source datasets and analysis methods.Based on PerfMon,we first conduct a comprehensive measurement on application-level traffic patterns and behaviors from multiple dimensions in the wireless network of T university(TWLAN),which is one of the largest campus wireless networks.Then we systematically study the application-level network performance.We observe that the application-level traffic behaviors and performance vary greatly across different locations and device types.The performance is far from satisfactory in some cases.To diagnose these problems,we distinguish locations and device types,and further locate the most crucial factors that affect the performance.The results of case studies show that the influential factors can effectively characterize performance changes and explain for performance degradation.
文摘Over the past decade, automatic traffic accident recognition has become a prominent objective in the area of machine vision and pattern recognition because of its immense application potential in developing autonomous Intelligent Transportation Systems (ITS). In this paper, we present a new framework toward a real-time automated recognition of traffic accident based on the Histogram of Flow Gradient (HFG) and statistical logistic regression analysis. First, optical flow is estimated and the HFG is constructed from video shots. Then vehicle patterns are clustered based on the HFG-features. By using logistic regression analysis to fit data to logistic curves, the classifier model is generated. Finally, the trajectory of the vehicle by which the accident was occasioned, is determined and recorded. The experimental results on real video sequences demonstrate the efficiency and the applicability of the framework and show it is of higher robustness and can comfortably provide latency guarantees to real-time surveillance and traffic monitoring applications.
基金Supported by the Natural Science Foundation of Zhejiang Province(No.2010A610105)
文摘This paper examines the noise and rotation resistance capacity of Hopfield Neural Network (HNN) given four corrupted traffic sign images. In the study, Signal-to-Noise Ratio (SNR), recall rate and pattern complexity are defined and employed to evaluate the recall performance. The experimental results indicate that the HNN possesses significant recall capacity against the strong noise corruption, and certain restoring competence to the rotation. It is also found that combining noise with rotation does not further challenge the HNN corruption resistance capability as the noise or rotation alone does.
基金support of Russian Foundation for Basic Research (RFBR 13-01-12056)National Natural Science Foundation of China (10972212)
文摘This letter reports traffic flow sensitivity to visco-elasticity, with the traffic flow modeling briefly described at first and then used to do traffic flow simulations whose results can reflect the properties of spatial-temporal evolution of ring traffic flow. It reveals that visco-elasticity plays crucial role in formation of traffic flow patterns, implying that self-organization of traffic flow is crucial in determining traffic flow status.
文摘The traffic activity offifth generation(5G)networks demand for new energy management techniques that is dynamic deep and longer duration of sleep as compared to the fourth generation(4G)network technologies that demand always for varied control and data signalling based on control base station(CBS)and data base station(DBS).Hence,this paper discusses the energy management in wireless cellular networks using wide range of control for twice the reduction in energy conservation in non-standalone deployment of 5G network.As the new radio(NR)based 5G network is configured to transmit signal blocks for every 20 ms,the proposed algorithm implements withstanding capacity of on or off based energy switching,which in-turn operates in wide range control by carrying out reduced computational complexity.The proposed Wide range of control for base station in green cellular network using sleep mode for switch(WGCNS)algorithm toon and off the base station will work in heavy load with neighbouring base station.For reducing the overhead duration in air,heuristic versions of the algorithm are proposed at the base station.The algorithm operates based on the specification with suggested protocol-level to give best amount of energy savings.The proposed algorithm reduces 40%to 83%of residual energy based on the traffic pattern of the urban scenario.