Search

To improve the specific quality of service, internal network management and security analysis in the future mobile network, accurate flow-awareness in the global network through packet sampling has been a viable solution. However, the current traffic measurement method with the five tuples cannot recognize the deep information of flows, and the Deep Packet Inspection (DPI) deployed at the gateways or access points is lack of traffic going through the internal nodes(e.g., base station, edge server). In this paper, by means of Deep Q-Network (DQN) and Software-Defined Networking (SDN) technique, we propose a flow-level sampling framework for edge devices in the Mobile Edge Computing (MEC) system. In the framework, an original learning-based sampling strategy considering the iterative influences of nodes is used for maximizing the long-term sampling accuracy of both mice and elephant flows. We present an approach to effectively collect traffic packets generated from base stations and edge servers in two steps: 1) adaptive node selection, and 2) dynamic sampling duration allocation by Deep Q-Learning. The results show that the approach can improve the sampling accuracy, especially for mice flows. [ABSTRACT FROM AUTHOR]

Power communication network is very important for the management of the power system. With the exponential growth of the power equipment and the drastic increase in the amount of power monitoring data, there is a huge challenge for the smart network access of the power communication network. Then, software defined network (SDN) which is flexible and scalable is suitable for the power communication network. In order to increase the high quality of service (QoS) of provided communication for users, the traffic QoS sensing is the key of the smart network access in the power communication network. Then, we propose a SDN-Based active measurement method to measure the evaluation parameters of the smart network access in the power communication network. Firstly, we introduce the instructions provided by the OpenFlow protocol, which can be used to collect statistics from OpenFlow switches. Secondly, we define the parameters that should be measured in the power communication network to evaluate the performance of the network and give the corresponding measurement method with them. Next, we proposed a SDN-based active measurement algorithm that updates its measurement interval based on the measurement throughput changes. Finally, we built a simulation platform using the Mininet simulator and the POX controller to verify the proposed measurement method, and the simulation results show the accuracy of our proposed method and the measurement load of our proposed method are both better than that of Polling measurement methods. [ABSTRACT FROM AUTHOR]

In this paper we apply the method of stochastic characteristics to a Lighthill–Whitham–Richards model. The stochastic perturbation can be seen as errors in measurement of the traffic density. For concrete examples we solve the equation perturbed by a standard Brownian motion and the geometric Brownian motion without drift. [ABSTRACT FROM AUTHOR]

Vehicle-to-Vehicle (V2V) Communication is one of the most promising application opportunities of Visible Light Communication (VLC). This paper suggests a novel transmission technique for VLC-V2V applications, called 2x2 Differential Transmission (2x2 DT). This transmission method exhibits excellent common-mode optical noise rejection performance. This beneficiary property is presented first with simulation results and later with real-life urban traffic measurement as well. After the promising simulation results, a demonstrational automotive-grade VLC-V2V link is presented and investigated for evaluation purposes. The link does not contain electrical filters, optical filters nor optical lenses, hence link costs are kept low and versatility is preserved. With our proposed 2x2 differential solution, both the transmission quality and common-mode optical noise rejection performance is outstanding. Due to the increased sensitivity of the differential VLC transmission regarding crosstalk between channels, a method is suggested for crosstalk reduction for high-quality differential transmission. [ABSTRACT FROM AUTHOR]

Traffic prediction is significantly important for performance analysis and network planning in Software Defined Networking (SDN). However, to effectively predict network traffic in current networks is very difficult and nearly prohibitive. As a new cutting-edge network technology, SDN decouples the control and data planes of network switch devices to enable the flexibility of network measurements and managements. The SDN architecture of the flow-based forwarding idea brings forth a promising of network traffic capture and prediction. We propose a lightweight traffic prediction algorithm for SDN applications. Firstly, different from traditional network traffic measurements, our method uses the flow-based forwarding idea in SDN to extract traffic statistic from data plane. The statistical variable describes network flow information forwarded in SDN and enables more accurate measurements of flow traffic via a direct and low-overhead way compared with traditional traffic measurements. Secondly, based on the temporal nature of traffic, the time-correlation theory is utilized to model flow traffic, where the time-series analysis theory and regressive modeling approach are used to characterize network traffic in SDN. A fully new method is proposed to perform traffic prediction. Thirdly, we propose the flow-based forwarding traffic prediction algorithm to forecast to SDN traffic. The detailed algorithm process is discussed and analyze. Finally, sufficient experiments are presented and designed to validate the proposed method. Simulation results show that our method is feasible and effective. [ABSTRACT FROM AUTHOR]

Software Defined Networking (SDN) architecture separates control plane and data plane, making network flexible and programmable. Since the large number of devices connected to the Internet of things (IoT) networks, the SDN-based network architecture makes the deployment and configuration of IoT much easier. In the IoT network, the fine-grained network traffic is critical to network management, then we propose a novel scheme to measure the fine-grained network traffic in the SDN-based IoT networks. In SDN-based IoT networks, the controller is very easy to be attacked, we introduce the blockchain technology into the measurement framework to ensure the security and consistency of the statistics. To measure flow traffic with low overhead and high accuracy, we collect the statistics of coarse-grained traffic of flows and fine-grained traffic of links, and model the network traffic as an ARIMA model and forecast the network traffic with the coarse-grained measurement of flows. Then, we propose an objective function to decrease the estimation errors. Due to the objective function is an NP-hard problem, we present a heuristic algorithm to obtain the optimal solution of the fine-grained measurement. Finally, we conduct some simulations to verify the validity of the proposed measurement scheme. Simulation results show that our approach is feasible and effective. [ABSTRACT FROM AUTHOR]

Enterprise Information System (EIS) is based on Internet of things (IoT) and aggregates a large amount of data of companies. Real-time reliable data transmission and data processing are very important for EIS. Network traffic of IoT is very important for network management and traffic planning in EIS. However, the measurement overheads and measurement accuracy are a contradiction for the fine-grained traffic measurement requirements. Artificial Intelligence (AI) has long promised to learn the natural feature of network traffic and make some actions about the prediction of traffic. In this paper, we propose an AI-based Lightweight Adaptive Measurement Method (ALAMM) for SDN to reduce the traffic measurement overheads and improve the measurement accuracy. Firstly, we use the AI to model and predict the flow traffic in the network. Based on the traffic prediction results, we propose an adaptive method to decide the traffic sampling frequency. Secondly, we send the measurement primitives to switches to measure the coarse-grained traffic of flows and links. Finally, the matrix filling and optimization method are proposed to recovery the fine-grained measurement and optimize the measurement result. Simulation results show that our approach can obtain network traffic with low overhead and high accuracy. [ABSTRACT FROM AUTHOR]

Estimating the number of distinct flows, also called the cardinality, is an important issue in many network applications, such as traffic measurement, anomaly detection, etc. The challenge is that high accuracy should be achieved with line speed and small auxiliary memory. Flajolet-Martin algorithm, LogLog algorithm, and HyperLogLog algorithm form a line of work in this area with improving performance. In this paper, we propose refined versions of these algorithms to achieve higher accuracy. The key observations are (1) the "leftmost" hash functions used by these algorithms can be generalized to reach higher accuracy, (2) the amendment coefficient can be highly biased in some certain streams or datasets so dynamically setting the amendment coefficient instead of using the one derived in pure math can lead to much better accuracy. Experimental results show great improvement of accuracy and stability of the refined versions over original algorithms. [ABSTRACT FROM AUTHOR]

Intelligent traffic management system (ITMS) is used to improve traffic flow by integrating information from different data repositories and online sensors, detecting incidents, and taking actions on traffic routing, and thus helps to reduce both fuel consumption and associated emission of green house gases. Collecting and modeling tremendous amount of continuous data from all road segments is a complex task. Data mining techniques are involved to shape the unstructured data to a structural formulation and make easier decision system for ITMS problems. In addition, making analytical decision on optimum route planning requires real-time road segment weight calculation from continuous data, in different time domains, for every day in a year. Dynamic road weights are calculated or upgraded using different environmental, road and vehicle-related decision attributes. Road segment weight decision is complicated due to the decision overlapping between the attribute clusters. Classification technique is required to provide accurate data modeling without any chaos overlapping scenario. Deep-neuro-fuzzy classification can help to improve the performance of the classification as well as remove the weight overlapping burdens. Thus, in this paper we are proposing a python-based compact model with c-means clustering and deep-neuro-fuzzy classification for road weight measurement in ITMS. [ABSTRACT FROM AUTHOR]

With the rapid growth of network traffic volumes in terms of the content and mobile Data, how to cope with these two new trends of Internet has been widely discussed. Among related solutions, Named Data Networking (NDN) is aimed to achieve highly effective content distribution. Besides, the mobility support is also one of its important research contents, attracting great attentions. Thus, in this paper, we provide a survey on the mobility support in NDN. Specifically, we firstly present the problem statement for both mobile producers and consumers in NDN, then classify and illustrate mechanisms of existing schemes based on their characteristics. Finally, we discuss the advantages and disadvantages of these countermeasures. [ABSTRACT FROM AUTHOR]

The sensor location problem is that of locating the minimum number of traffic sensors at intersections of a road network such that the traffic flow on the entire network can be determined. In this paper, we provide a new necessary condition on the location of these sensors to enable the traffic flow throughout the network to be computed. This condition is not sufficient in general, but we show that for a large class of problem instances, the condition is sufficient. Many typical road networks are included in this category, and we show how our condition can be used to inform traffic sensor placement. [ABSTRACT FROM AUTHOR]

With the rapid growth of link speed, obtaining detailed traffic statistics becomes much more difficult. In order to reduce the resource consumption of measurement systems, more and more passive traffic measurement employs sampling at the packet level. Packet sampling has become an attractive and scalable method to measure flow data on high-speed links. However, knowing the length distributions of traffic flows passing through a network link is very useful for network operation and management. In this paper, we consider the problem of estimating flow length distributions based on sampled flow data. This paper introduces two algorithms for estimating flow length distributions, fitting estimation and factoring estimation. The fitting estimation uses piecewise Pareto distribution to fit the original traffic for a small sampling period. The factoring estimation is used for a large sampling period. It first factorizes the large sampling period into a product of some smaller integer factors, then iteratively invokes fitting estimation or other algorithms such as $${\textit{EM}}$$ in the arranged order of the factors. Evaluations of the proposed algorithms on large Internet traces obtained from several sources demonstrate that they have high measurement accuracy with low computation overhead. The algorithms allow us to recover the complete flow length distributions. [ABSTRACT FROM AUTHOR]

The problem of development and customization of optoelectronic land traffic flow monitoring systems with account taken of specified operational conditions is examined. The methodology for automated design of algorithmic support for the main subsystem of the mentioned systems, namely, the one for vehicle registration number recognition is suggested. [ABSTRACT FROM AUTHOR]

A strict proof of the hyperbolicity of the multi-class LWR (Lighthill-Whitham-Richards) traffic flow model, as well as the descriptions on those nonlinear waves characterized in the traffic flow problems were given. They were mainly about the monotonicity of densities across shocks and in rarefactions. As the system had no characteristic decomposition explicitly, a high resolution and higher order accuracy WENO (weighted essentially non-oscillatory) scheme was introduced to the numerical simulation, which coincides with the analytical description. [ABSTRACT FROM AUTHOR]