Statistical methods to estimate vehicle count using traffic cameras

Traffic camera has played an important role in enabling intelligent and real-time traffic monitoring and control. In this paper, we focus on establishing a correlation model for the traffic cameras’ vehicle counts and increase the spatial-resolution of a city’s vehicle counting traffic camera system by means of correlation-based estimation. We have developed two methods for constructing traffic models, one using statistical machine learning based on Gaussian models and the other using analytical derivation from the origin-destination (OD) matrix. The Gaussian-based method outperforms existing correlation coefficient based methods. When training data are not available, our analytical method based on OD matrix can still perform well. When there is only a limited number of cameras, we develop heuristic algorithms to determine the most desirable locations to place the cameras so that the errors of traffic estimations at the locations without traffic cameras are minimized. We show some improvements in the performance of our proposed methods over an existing method in a variety of simulations.

Oscillations in Complex Communication Networks under Attack

In this paper we study the traffic dynamics of a scale-free complex network under an intentional attack at the node with the largest betweenness (i.e., the hub). This node is removed from the network after the attack. Consequently, the traffic load which used to go through the hub has to find other paths. A weight is defined for each node to indicate how long a packet has to wait at this node. A shortest time delay routing strategy is then applied based on the weight. We find that with different values of the capacity redundancy parameter, the traffic dynamics are quite different. When the capacity has large redundancy, then all the nodes work in a free-flow state even after the attack. If the capacity redundancy is not that large, then congestion may occur at some nodes. Due to the shortest time delay routing strategy, this congestion occurs periodically. If the capacity is very small, then the traffic dynamics become complicated, and oscillations and chaotic phenomena take place.     

Allocating point-to-point connections over point-to-multipoint optical channels

Optical channels are currently able to carry 10 Gbit/s and even 40 Gbit/s traffic flows. However, it is not usual to have such amounts of traffic between any pair of client nodes. This article proposes using point-to-multipoint optical channels for the allocation of point-to-point connections in transparent wavelength-routed optical networks. Specifically, when an optical connection between a source-destination node pair has to be established, the optical signal is also sent to some adjacent nodes by introducing passive optical splitters; in this way a light-tree is built. Then, the already established point-to-multipoint optical channel can be used to groom further point-to-point connections between the same source node and each of the other nodes composing the light-tree. The benefits of this strategy are 2-fold: first, the reduction of optical transmission equipments allowing cost savings with respect to the traditional typical point-to-point approach and, second, the optimization of the optical channels utilization meeting in such a way Traffic Engineering objectives. The merits of proposed approach are evaluated by simulation.     

一种基于应用需求的网络路径可用带宽测量策略

报文对间隔及报文单向延迟的变化可以反映报文发送速率与可用带宽的大小关系,根据这个规律本文提出了一种基于应用需求的网络路径可用带宽测量策略COPP.COPP发送多组发送速率不断增大的报文对序列,并从每组序列中求出所有成为转换点的报文对,然后根据这些报文对所受干扰的不同程度赋以它们不同的权重,从而得到路径可用带宽.COPP可根据具体应用不同的带宽需求动态决定测量范围,仿真实验研究了测量中探测报文大小的选择,并通过与Pathload等其它带宽测量方法的比较验证了COPP具有低开销、高精度、平稳性好且对网络状态变化敏感等优点.     

Design of capacitated networks with tree configurations

This paper considers the problem of designing a capacitated network with a tree configuration (CTP). For a given set of nodes with their capacities,k types of link facilities with various characteristics, and installation cost for connecting each pair of nodes using each type of link facility, the problem is to find a tree network which satisfies the given traffic requirements between all pairs of nodes and minimizes total installation cost. We formulate (CTP) as an integer programming problem using path variables. To solve the linear programming relaxation which has exponentially many variables, we develop a polynomial-time column generation procedure. Moreover, to tighten the formulation, an efficient preprocessing procedure is devised and some classes of valid inequalities are found. Using the results, we develop a branch- and-cut algorithm with column generation where an efficient branching rule is adopted. Computational results show that the algorithm can solve practically-sized problems to optimality within a reasonable time.     

How to reconstruct end-to-end traffic based on time-frequency analysis and artificial neural network

End-to-end traffic, which describes the inherent characteristics and end-to-end behaviors of communication networks, is the crucial input parameter of network management and network traffic engineering. This paper proposes a new reconstruction algorithm to develop the research on reconstruction of end-to-end traffic in large-scale communication networks. We firstly conduct the time-frequency analysis on end-to-end traffic, and then localize its features to gain its time-frequency properties before decomposing it into the low-frequency and high-frequency components. We find that if decomposing appropriately, the low-frequency component of end-to-end traffic can accurately reflect its change trend, while its high-frequency component can well show the burst and fluctuation nature. This motivates us to find a reasonable time-frequency decomposition strategy to extract the low-frequency and high-frequency components of end-to-end traffic. Moreover, this further inspires us to use the regressive model to model the low-frequency part, exploit artificial neural network to characterize the high-frequency component, and then combine these two parts according to the regressive model and artificial neural network to precisely reconstruct end-to-end traffic. Simulation results show that in contrast to previous methods our algorithm is much more effective and promising.     

Estimating Dynamic Traffic Matrices by Using Viable Routing Changes

In this paper we propose a new approach for dealing with the ill-posed nature of traffic matrix estimation. We present three solution enhancers: an algorithm for deliberately changing link weights to obtain additional information that can make the underlying linear system full rank; a cyclo-stationary model to capture both long-term and short-term traffic variability, and a method for estimating the variance of origin-destination (OD) flows. We show how these three elements can be combined into a comprehensive traffic matrix estimation procedure that dramatically reduces the errors compared to existing methods. We demonstrate that our variance estimates can be used to identify the elephant OD flows, and we thus propose a variant of our algorithm that addresses the problem of estimating only the heavy flows in a traffic matrix. One of our key findings is that by focusing only on heavy flows, we can simplify the measurement and estimation procedure so as to render it more practical. Although there is a tradeoff between practicality and accuracy, we find that increasing the rank is so helpful that we can nevertheless keep the average errors consistently below the 10% carrier target error rate. We validate the effectiveness of our methodology and the intuition behind it using commercial traffic matrix data from Sprint's Tier-1 backbone.     

Algorithms for the global design of WDM networks including the traffic grooming

In this paper, we propose a model and algorithms for the global design problem of wavelength division multiplexing (WDM) networks including the traffic grooming. This problem consists in finding the number of fibres between each pair of nodes (i.e. the physical topology), finding the number of transponders at each node, choosing the set of lightpaths (i.e. the virtual topology), routing these lightpaths over the physical topology and, finally, grooming and routing the traffic over the lightpaths. Since this problem is NP-hard, we propose two heuristic algorithms and a tabu search metaheuristic algorithm to find solutions for real-size instances within a reasonable amount of computational time.     

Evaluation and characterization of available bandwidth probing techniques

The packet pair mechanism has been shown to be a reliable method to measure the bottleneck link capacity on a network path, but its use for measuring available bandwidth is more challenging. In this paper, we use modeling, measurements, and simulations to better characterize the interaction between probing packets and the competing network traffic. We first construct a simple model to understand how competing traffic changes the probing packet gap for a single-hop network. The gap model shows that the initial probing gap is a critical parameter when using packet pairs to estimate available bandwidth. Based on this insight, we present two available bandwidth measurement techniques, the initial gap increasing (IGI) method and the packet transmission rate (PTR) method. We use extensive Internet measurements to show that these techniques estimate available bandwidth faster than existing techniques such as Pathload, with comparable accuracy. Finally, using both Internet measurements and ns simulations, we explore how the measurement accuracy of active probing is affected by factors such as the probing packet size, the length of probing packet train, and the competing traffic on links other than the tight link.     

Centralized and Distributed Algorithms for Routing and Weighted Max-Min Fair Bandwidth Allocation

Given a set of demands between pairs of nodes, we examine the traffic engineering problem of flow routing and fair bandwidth allocation where flows can be split to multiple paths (e.g., MPLS tunnels). This paper presents an algorithm for finding an optimal and global per-commodity max-min fair rate vector in a polynomial number of steps. In addition, we present a fast and novel distributed algorithm where each source router can find the routing and the fair rate allocation for its commodities while keeping the locally optimal max-min fair allocation criteria. The distributed algorithm is a fully polynomial epsilon-approximation (FPTAS) algorithm and is based on a primal-dual alternation technique. We implemented these algorithms to demonstrate its correctness, efficiency, and accuracy.      

A fast lightweight approach to origin-destination IP traffic estimation using partial measurements

In this paper, a novel approach is proposed for estimating traffic matrices. Our method, called PamTram for PArtial Measurement of TRAffic Matrices, couples lightweight origin-destination (OD) flow measurements along with a computationally lightweight algorithm for producing OD estimates. The first key aspect of our method is to actively select a small number of informative OD flows to measure in each estimation interval. To avoid the heavy computation of optimal selection, we use intuition from game theory to develop randomized selection rules, with the goals of reducing errors and adapting to traffic changes. We show that it is sufficient to measure only one flow per measurement period to drastically reduce errors-thus rendering our method lightweight in terms of measurement overhead. The second key aspect is an explanation and proof that an Iterative Proportional Fitting algorithm approximates traffic matrix estimates when the goal is a minimum mean-squared error; this makes our method lightweight in terms of computation overhead. A one-step error bound is provided for PamTram that bounds the average error for the worst scenario. We validate our method using data from Sprint's European Tier-1 IP backbone network and demonstrate its consistent improvement over previous methods.     

AdaptiveMutate: a technique for privacy preservation

Mobile apps are known to be rich sources for gathering privacy-sensitive information about smartphone users. Despite the presence of encryption, passive network adversaries who have access to the network infrastructure can eavesdrop on the traffic and therefore fingerprint a user’s app by means of packet-level traffic analysis. Since it is difficult to prevent the adversaries from accessing the network, providing secrecy in hostile environments becomes a serious concern.In this study, we propose AdaptiveMutate, a privacy-leak thwarting technique to defend against the statistical traffic analysis of apps. First, we present a method for the identification of mobile apps using traffic analysis. Further, we propose a confusion system in which we obfuscate packet lengths, and/or inter-arrival time information leaked by the mobile traffic to make it hard for intruders to differentiate between the altered app traffic and the actual one using statistical analysis. Our aim is to shape one class of app traffic to obscure its features with the minimum overhead. Our system strives to dynamically maximize its efficiency by matching each app with the corresponding most dissimilar app. Also, AdaptiveMutate has an adaptive capability that allows it to choose the most suitable feature to mutate, depending on the type of apps analyzed and the classifier used, if known.We evaluate the efficiency of our model by conducting a comprehensive simulation analysis that mutates different apps to each other using AdaptiveMutate. We conclude that our algorithm is most efficient when we mutate a feature of one app to its most dissimilar one in another app. When applying the identification technique, we achieve a classification accuracy of 91.1%. Then, using our obfuscation technique, we are able to reduce this accuracy to 7%. Also, we test our algorithm against a recently published approach for mobile apps classification and we are able to reduce its accuracy from 94.8% to 17.9%. Additionally, we analyze the tradeoff between the shaping cost and traffic privacy protection, specifically, the associated overhead and the feasibility for real-time implementation.     

On the Feasibility of Dynamic Congestion-Based Pricing in Differentiated Services Networks

Differentiated services can ensure that traffic on some codepoints receives higher quality of service (QoS) than traffic on other codepoints, but without additional mechanisms it cannot target any particular QoS. Congestion-based pricing has been suggested as a method to target QoS in other network architectures. Here, we investigate whether congestion-based pricing can be used to control aggregate traffic into each codepoint by motivating users to choose the codepoints appropriate for each application. We first ask what information needs to be exchanged; we assert that both price and QoS information must be available for users to make decisions. We then ask how effective congestion-based pricing in diffServ can be; we find that it is feasible only for networks with sufficiently high bandwidth to guarantee that QoS can be quickly measured.      

Performance of shared-memory switches under multicast burstytraffic

We study shared-memory switches under multicast bursty traffic and characterize the relation between their performance and the multicast distribution that defines the mix of multicast traffic arriving at the switches. We consider two schemes that have been used in practical realizations of these switches to replicate multicast cells: (1) replication-at-receiving (RAR), where multiple copies of a multicast cell are stored in the buffer and served independently, and (2) replication-at-sending (RAS), where a single instance of a multicast cell is stored in the buffer, and the cell is replicated as it is transmitted to the output ports. For each scheme, we study two configurations: (1) the shared-memory-only (SMO) configuration, where the bandwidth of the replication mechanism is sufficient to accommodate even the worst-case replication requirements, and (2) the shared-memory-with-replication-first-in-first-out (SM+RFIFO) configuration, where the bandwidth of the replication mechanism is lower than that required by the worst case, and thus an additional buffer is used in front of the shared memory to temporarily store cells while they are replicated. For all cases, using simulation, we find upper bounds for the buffer requirements to achieve a desired cell-loss rate. We show that these upper bounds are significantly larger than the buffer requirements under unicast traffic and are approached even for very small volumes of multicast traffic; thus, these upper bounds should be used in practice to size the buffers to achieve the desired performance under traffic with general multicast distributions. We also study shared-memory switches with output demultiplexers and characterize and compare the different multicasting schemes that are used in these switches     

Design Method of Logical Topologies with Quality of Reliability in WDM Networks

As the bandwidth capacity of WDM networks continues to grow rapidly,traffic loss caused by a failure of network components is becoming unacceptable. To prevent such traffic loss and thus enhance network reliability, a protection method that prepares backup lightpaths for each working path is now being developed. In this paper, we first introduce the concept of QoR (quality of reliability), which is a realization of QoS with respect to the reliability needed in a WDM network. We define QoR in terms of the recovery time from when a failure occurs to when traffic on the affected primary lightpath is switched to the backup lightpath. After that, we propose a heuristic algorithm that can be used to design a logical topology that satisfies the QoR requirement for every node pair. The objective is to minimize the number of wavelengths needed for a fiber in the logical topology to carry the traffic with the required QoR. We compare this algorithm with two existing algorithms and show that it enables more effective use of wavelength resources; with the proposed algorithm, up to 25% fewer wavelengths are needed than with the other algorithms.     

Bayonet-corpus: a trajectory prediction method based on bayonet context and bidirectional GRU

Predicting travel trajectory of vehicles can not only provide personalized services to users, but also have a certain effect on traffic guidance and traffic control. In this paper, we build a Bayonet-Corpus based on the context of traffic intersections, and use it to model a traffic network. Besides, Bidirectional Gated Recurrent Unit (Bi-GRU) is used to predict the sequence of traffic intersections in one single trajectory. Firstly, considering that real traffic networks are usually complex and disorder and cannot reflect the higher dimensional relationship among traffic intersections, this paper proposes a new traffic network modeling algorithm based on the context of traffic intersections: inspired by the probabilistic language model, a Bayonet-Corpus is constructed from traffic intersections in real trajectory sequence, so the high-dimensional similarity between corpus nodes can be used to measure the semantic relation of real traffic intersections. This algorithm maps vehicle trajectory nodes into a high-dimensional space vector, blocking complex structure of real traffic network and reconstructing the traffic network space. Then, the bayonets sequence in real traffic network is mapped into a matrix. Considering the trajectories sequence is bidirectional, and Bi-GRU can handle information from forward and backward simultaneously, we use Bi-GRU to bidirectionally model the trajectory matrix for the purpose of prediction.     

因特网流量矩阵的流形结构

当前,流量矩阵已经被广泛应用于异常检测、流量预测、流量工程等领域,但是现有研究仅仅发现流量矩阵存在线性结构。为了寻找流量矩阵中可能存在的非线性结构,构建流量矩阵模型并从实际因特网骨干网Abilene中采集流量矩阵数据集,应用经典的流形学习算法进行实测数据分析,发现这些高维(81维或121维)的流量矩阵数据集实际上是嵌入的固有维度为5维的低维流形,且其受采样密度和噪声数据等各种因素的影响呈现出不同的结构。     

Bio-inspired power control and channel allocation for cellular networks with D2D communications

With the tremendous increment of traffic in the next generation mobile networks, device to device (D2D) communication is proposed to relieve the traffic burden of the base station and improve the overall network capacity. It supports direct communications between devices and could reuse the resources of cellular users (CUs). Despite the advantages, D2D communications bring great challenges in interference management. In this paper, we study the power control and channel allocation problems in three scenarios: (1) one CU and one D2D pair; (2) one CU and multiple D2D pairs; (3) multiple CUs and multiple D2D pairs. The goal is to coordinate the mutual interferences and maximize the overall network capacity. We derive sufficient conditions to guarantee the efficiency of D2D communications in scenarios with one CU and one D2D pair. We propose the bio-inspired PSO-P power control algorithm for the scenarios with one CU and multiple D2D pairs, and the PSO-CP algorithm for the scenarios with multiple CUs and multiple D2D pairs to jointly assign channels and powers. Simulation results show that the proposed algorithms are efficient in improving the overall network capacity.

     

Statistical multiplexing and mix-dependent alternative routing inmultiservice VP networks

We consider problems in traffic integration and routing for virtual path (VP)-based multiservice networks. The objective is to exploit statistical multiplexing among various traffic types in order to improve system utilization. Difficulties arise due to statistical multiplexing since a connection's bandwidth requirement depends on the characteristics of the interfering traffic. We first consider whether segregating heterogeneous traffic with different quality of service (QoS) requirements on separate VPs is desirable. Next we consider routing heterogeneous permanent connections given a predefined traffic type mix onto multiple VPs between a source destination pair. We show that it is not necessarily advantageous to have each VP carry every traffic type. In fact, perhaps surprisingly, an optimum solution to this problem suggests that only a small number of traffic types, or even homogeneous traffic, need be present on each VP. Based on this observation, we propose a simple alternative routing algorithm with routing sequences depending on the traffic mix     

Estimation of frequencies and damping factors by two-dimensionalESPRIT type methods

In this paper, we consider two-dimensional (2-D) signals modeled by the sum of damped cisoids. We propose two high-resolution approaches to estimate their frequencies and damping factors. Both high-resolution methods are based on the shift-invariance structure of the signal subspace related to each dimension. The first one estimates the frequency components in both dimensions as in the matrix enhancement and matrix pencil (MEMP) method before pairing them with a new algorithm. The second one consists of the direct estimation of the signal frequency pairs without an additional step to pair the frequencies related to each dimension. We show how these methods can estimate the scattering points of radar images