Network Traffic Management Research Articles

Enhancing Road Safety with Smart Infrastructure: Development and Implementation of Advanced Zebra Crossings and Speed Breaking

Abstract: This study addresses a critical issue in road safety by proposing innovative solutions to mitigate accidents caused by speeding and reckless driving, particularly in high-risk areas such as highways, hospitals, and schools. With road traffic accidents accounting for approximately 1.3 million fatalities annually, along with 20 to 50 million injuries or disabilities, the inefficacy of traditional traffic control systems is a growing concern. Factors such as poor visibility, adverse weather, and driver distractions further compromise the effectiveness of conventional signs and signals. This research introduces a "smart zebra crossing" system that integrates ultrasonic sensors and embedded LED lights to provide real-time alerts to both drivers and pedestrians, thereby enhancing visibility and safety, especially under low-light conditions. Additionally, a "smart speed breaking" system is proposed, which dynamically adjusts its height based on vehicle speed, detected through ultrasonic sensors, prompting drivers to decelerate when necessary. Both systems are linked to a central traffic management network, facilitating real-time adjustments in speed limits and providing alerts to connected vehicles. The integration of these technologies represents a significant advancement in road safety, offering an adaptive, intelligent solution for improving traffic management and reducing the risk of accidents, particularly in vulnerable zones.

Analisis Penggunaan Metode XConnect Main and Backup Link Berbasis VPN MPLS Layer 2 pada Jaringan Metro-Ethernet

The rapid development of information and communication technology requires excellent scalability, which is crucial for network performance availability. MPLS is a technology that can be used for communication in high-speed backbone networks based on virtual private networks (VPN). MPLS provides solutions to various current computer network problems such as speed, scalability, quality of service (QoS), and network traffic management. This research aims to optimize the scalability of MPLS Layer 2 VPN-based network traffic by applying the xconnect main and backup method implemented in metro Ethernet networks. The network simulation implementation was created using the Emulated Virtual Environment – Next Generation (EVE-NG) application to obtain data on convergence time and QoS parameters according to TIPHON standards, as well as the use of MPLS and Route Reflector to enhance MPLS backbone performance. This research shows that both main and backup MPLS L2VPN paths have good index values in packet delivery according to TIPHON standards, with an average rating of 3.0. Thus, this research can help optimize path scalability to improve the quality of MPLS L2VPN network traffic.

A convergent cross‐mapping approach for unveiling congestion spatial causality in urban traffic networks

AbstractSpatial causality in urban traffic networks explores how events or conditions in one location affect those in another. Unveiling congestion spatial causality is crucial for identifying congestion‐inducing bottlenecks in traffic networks and offering valuable insights for traffic network management and control. This study introduces the traffic‐convergent‐cross‐mapping (T‐CCM) method, a state‐space‐reconstruction approach from the dynamic system perspective, to identify causality among roads within urban traffic networks using time series data. Simultaneously, it effectively addresses the intricate challenges of uncertainty and interdependency among sensors caused by traffic flow dynamics. Empirical findings from real‐world (PeMS‐Bay area) traffic speed data validate the effectiveness of the T‐CCM method in detecting causality. This study reveals bidirectional causal effects between downstream and upstream roads in short‐term congestion generation and dissipation periods, which can pinpoint congestion origins and inform quick traffic management response. Furthermore, it elucidates the long‐term causality impacts between distant roads, particularly with regard to traveler choices and road land use attributes, guiding infrastructure investment and public transit improvements.

Adaptive Flow Timeout Management in Software-Defined Optical Networks

Current trends in network traffic management rely on the efficient control of individual flows. Software-defined networking popularized this notion. Per-flow management is perfectly viable in standard IP networks, in which packet processing is in the electric domain. However, optical networks provide more restrictions and constraints making per-flow traffic management difficult. One of the most important challenges is to reduce the concurrent number of flows present in the flow tables to make the switching process quicker. In this paper, we propose a mechanism to manage flow timeout values that uses idle timeout and hard timeout parameters. To calculate the appropriate values of the parameters, the mechanism analyzes the packet inter-arrival times. The algorithm also takes into account the current occupancy of the flow table.

Approach to creation model of software-oriented communication network of the State Border Guard Service of Ukraine.

Enhancing the level of information resource protection of the State Border Guard Service of Ukraine, as a component of Ukraine's defense forces, is an important task, especially in times of war. An effective and reliable corporate network of the border agency becomes a key component for ensuring security, coordination, and responsiveness to both internal and external threats. One aspect of developing the information space is the development of the transport network of the State Border Guard Service. Analysis of existing technologies, identifying their advantages and disadvantages, allowed for the identification of Software-Defined Wide Area Networking technology as the one that best meets the conformity criteria defined by the experts of the Communication and Information Systems Management. This technology not only allows for optimizing network traffic and ensuring high-speed data transmission but also guarantees flexibility in network management and enhances security. For the State Border Guard Service, responsible for the security of the country's borders, the use of advanced Software-Defined Wide Area Networking technologies can be crucial for optimizing operations and ensuring the highest level of information security. The article focuses on developing a conceptual model for building the corporate network of the State Border Guard Service based on Software-Defined Wide Area Networking technology. The main goal of the research is to consider the possibility of implementing this technology in the context of its application by Ukraine's defense forces and to analyze its advantages and disadvantages. The research task was to study the technical aspects of implementing Software-Defined Wide Area Networking, namely: configuration of network devices, traffic management, security monitoring, and data protection. Cisco's Software-Defined Wide Area Networking has applied an optimal approach to network architecture, which separates the control plane from the data plane of all peripheral routers and implements all control functions in a centralized software controller called vSmart. Practical recommendations from the research allowed for ensuring continuous network service by rerouting around failures and potential causes of downtime.

Reliability analysis of urban road traffic network under targeted attack strategies considering traffic congestion diffusion

The cascading failures caused by traffic congestion diffusion may deteriorate traffic network reliability. Comprehending urban traffic congestion mechanisms is essential for road network planning and traffic management against cascading failures. To uncover this, the reliability of urban road traffic network (URTN) under cascading failure considering different attack strategies is analyzed. The cascading failure model is established based on the improved nonlinear load-capacity relationship. Five kinds of attack strategies including Strength Attack (SA), Betweenness Centrality Attack (BCA), Eigenvector Centrality Attack (ECA), Closeness Centrality Attack (CCA), and Random Attack (RA) are selected. In particular, the capacity affected by traffic congestion is considered, providing a new perspective for the study of traffic congestion diffusion. A state update equation for networks is proposed to simulate the network congestion diffusion. Finally, a case study is conducted by using the URTN of Shanghai as the background. The results show that the network will experience large-scale congestion when high-importance nodes are attacked. The congestion degree is the highest under CCA strategy, network efficiency is the lowest under ECA strategy, and traffic quality is the poorest under CCA strategy. As the congestion critical failure threshold decreases, the speed and scale of cascading failures caused by traffic congestion diffusion are greater. Maintaining proper traffic management and control capability can largely reduce the cascading effect to a great extent and improve the reliability of the network. The results can provide a research basis for traffic management to improve network reliability.

Foresight plus: serverless spatio-temporal traffic forecasting

Building a real-time spatio-temporal forecasting system is a challenging problem with many practical applications such as traffic and road network management. Most forecasting research focuses on achieving (often marginal) improvements in evaluation metrics such as MAE/MAPE on static benchmark datasets, with less attention paid to building practical pipelines which achieve timely and accurate forecasts when the network is under heavy load. Transport authorities also need to leverage dynamic data sources such as roadworks and vehicle-level flow data, while also supporting ad-hoc inference workloads at low cost. Our cloud-based forecasting solution Foresight, developed in collaboration with Transport for the West Midlands (TfWM), is able to ingest, aggregate and process streamed traffic data, enhanced with dynamic vehicle-level flow and urban event information, to produce regularly scheduled forecasts with high accuracy. In this work, we extend Foresight with several novel enhancements, into a new system which we term Foresight Plus. New features include an efficient method for extending the forecasting scale, enabling predictions further into the future. We also augment the inference architecture with a new, fully serverless design which offers a more cost-effective solution and which seamlessly handles sporadic inference workloads over multiple forecasting scales. We observe that Graph Neural Network (GNN) forecasting models are robust to extensions of the forecasting scale, achieving consistent performance up to 48 hours ahead. This is in contrast to the 1 hour forecasting periods popularly considered in this context. Further, our serverless inference solution is shown to be more cost-effective than provisioned alternatives in corresponding use-cases. We identify the optimal memory configuration of serverless resources to achieve an attractive cost-to-performance ratio.

Adaptive network traffic control with approximate dynamic programming based on a non-homogeneous Poisson demand model

ABSTRACT In this study, we develop a stochastic dynamic traffic-flow model subject to practical restrictions under the non-homogeneous Poisson vehicle arrival process. Using the cell transmission strategy, we establish traffic dynamics between two intersections. We also discuss simulating the random demand of source links from an estimated intensity function. Additionally, we propose an algorithm to optimize time interval division for aggregated data, aiming to enhance estimation performance. We explore applying our traffic flow model to the adaptive traffic network management problem, which is formulated as a Markov decision process. Leveraging approximate dynamic programming with recursive least squares-temporal difference learning, we achieve adaptive optimal policies. To validate our approach, we conduct a series of numerical experiments with random demands. The results of non-homogeneous Poisson demand conducted using random numbers and a real-word dataset indicate high efficiency with the piecewise constant, I-SMOOTH, and MNO-PQRS estimators. Compared to the Webster and Max-pressure control systems, our proposed approximate dynamic programming-based model exhibits superior stability and applicability.

Distributed Traffic Synthesis and Classification in Edge Networks: A Federated Self-Supervised Learning Approach

With the rising demand for wireless services and increased awareness of the need for data protection, existing network traffic analysis and management architectures are facing unprecedented challenges in classifying and synthesizing the increasingly diverse services and applications. This paper proposes FS-GAN, a federated self-supervised learning framework to support automatic traffic analysis and synthesis over a large number of heterogeneous datasets. FS-GAN is composed of multiple distributed Generative Adversarial Networks (GANs), with a set of generators, each being designed to generate synthesized data samples following the distribution of an individual service traffic, and each discriminator being trained to differentiate the synthesized data samples and the real data samples of a local dataset. A federated learning-based framework is adopted to coordinate local model training processes of different GANs across different datasets. FS-GAN can classify data of unknown types of service and create synthetic samples that capture the traffic distribution of the unknown types. We prove that FS-GAN can minimize the Jensen-Shannon Divergence (JSD) between the distribution of real data across all the datasets and that of the synthesized data samples. FS-GAN also maximizes the JSD among the distributions of data samples created by different generators, resulting in each generator producing synthetic data samples that follow the same distribution as one particular service type. Extensive simulation results show that the classification accuracy of FS-GAN achieves over 20% improvement in average compared to the state-of-the-art clustering-based traffic analysis algorithms. FS-GAN also has the capability to synthesize highly complex mixtures of traffic types without requiring any human-labeled data samples.

Optimization of regulation and fiscal policies for urban residential land use and traffic network management

Residential land use density regulation is an important policy instrument for reducing the local external diseconomies caused by concentration of population and buildings in urban areas. But density regulation also affects the distribution of population across the whole urban space, which determines the travel demands hence traffic flows over the transportation network in the city. In this paper, we formulate a model integrating the residential location choice equilibrium, traffic network equilibrium and the housing market equilibrium, subject to floor area ratio (FAR) regulation, a representative practical strategy for density regulation. The restriction of FAR regulation on housing supply is formulated as a development coordination cost function dependent on the level of FAR that is actually realized under the designated upper FAR limit. Complementary location-specific tax/subsidy policies, and road pricing schemes, are also addressed in a unified manner.The mathematical structure of the model is explored, and efficient algorithms are developed for solving the equilibrium equations, and for the optimization of FAR regulations and the pecuniary strategies.

Learning traffic as videos: Short-term traffic flow prediction using mixed-pointwise convolution and channel attention mechanism

In the construction of intelligent transportation systems, short-term traffic flow prediction is of great significance for the advancement of traffic network management. But due to the presence of many complex factors in both spatial and temporal domains, it remains a complex and challenging task. Existing literature usually employs the convolutional neural network (CNN)-based methods in capturing spatiotemporal correlations. These CNN-based methods often use a single-channel data structure to represent different periodic patterns, which makes the model susceptible to over-parameterization when capturing periodic dependencies and prone to information loss after convolution. To overcome these limitations, this paper presents a hybrid deep learning method for short-term traffic flow prediction. In this method, a video-shaped multi-channel data structure is designed to represent different periodic patterns more efficiently. Next, a new mixed-pointwise convolution is introduced for capturing periodic dependencies without the negative impacts mentioned above. Lastly, an improved channel attention mechanism is proposed to learn channel inter-dependencies with controllable parameter usage. The proposed method is lightweight, yet highly effective. Compared to the state-of-the-art baseline method, it reduces the root mean squared error by up to 6.7% on the PeMSD4 dataset and 13.3% on the PeMSD7 dataset, while also achieving substantial improvement in two additional metrics, exhibiting strong robustness and great scalability across various settings.

Design and implement a real-time network traffic management system using SNMP protocol

The research focuses on designing and implementing a system for managing incoming server traffic using the Simple Network Management Protocol (SNMP). The essence of the method is to provide a real-time monitoring system that empowers network managers to oversee all network activities efficiently. The key difference of the developed system lies in using the SNMP protocol to monitor and control the network without needing a third-party tool. The proposed approach addresses administrators' common network challenges, including inefficient bandwidth utilization and complex network resource monitoring. Upon implementation, the system gathered essential network equipment information from the Management Information Base (MIB). The research results indicate that the SNMP-based network management system prevents network devices from reaching their allotted bandwidth limits. The system notably reduces CPU utilization from 28 % to 8 % and decreases bandwidth usage from 1.7 MiB/s to 260 bytes/s. Additionally, the system's alerting capability enhances the network's resilience against incoming traffic anomalies that may otherwise interrupt server operations. An example of using the proposed system to manage incoming server traffic in small networks. It has several practical uses in managing and maintaining network infrastructure in different fields. Also, it enables real-time monitoring of network devices, resource management, fault detection, and performance optimization. The system can be used in small organizations to monitor network performance, track faults, and manage network resources efficiently. In addition, SNMP-based network monitoring systems can find applications in almost any field where networked devices and infrastructure require monitoring, management, and optimization

A Real-Time Streaming System for Customized Network Traffic Capture.

Logging network traffic offers valuable insights into data flow, enabling the proactive analysis and troubleshooting of issues as they arise. Moreover, it provides a means to access and examine the exchanged information among network users that would otherwise be inaccessible. To enhance network traffic analysis, the integration of innovative technologies that facilitate real-time querying and pattern matching proves indispensable. This research paper presents a system that exemplifies such advancements-an innovative network traffic logging tool. The tool specifically focuses on performing real-time network packet transfer to Apache Kafka and ksqlDB, leveraging their capabilities to ensure swift and dependable storage of network packets in Apache Kafka topics. By showcasing this solution, the paper demonstrates the benefits and effectiveness of employing modern technologies for network traffic analysis and management.

Joint Controller Placement and Control-Service Connection in Hybrid-Band Control

By separating the forwarding and control planes, Software-Defined Networking (SDN) facilitates flexible traffic routing and network management for a service network. Because of the impact of controller deployment on message transmission distances and network latency, controller placement problems have drawn many researchers' attention. However, assumptions in most existing research that all control packets are either transmitted in the service network (i.e., in-band control) or through predetermined control-service connection (i.e., out-of-band control) are not reasonable due to bandwidth resources occupation on the service network or high construction costs. In this paper, we are the first to jointly discuss the controller placement and control-service connection problem for latency minimization in the hybrid-band control mode, which is essentially a bi-level programming optimization problem. Specifically, we introduce auxiliary variables to simplify the above NP-hard problem. Next, Generalized Benders decomposition is used to obtain an optimal solution in theory. In addition, we propose a time-efficient fireworks algorithm with a little latency increment for large-scale networks. Extensive evaluations show that the two proposed algorithms accomplish the desired objectives and respectively achieve up to 35% and 25% latency decrement than greedy algorithms.

Comparative Analysis of Machine Learning and Deep Learning Techniques for Intrusion Detection

Abstract: This comparative analysis examines the application of both machine learning and deep learning methods in network traffic classification. Network traffic classification holds significant importance in network security, traffic management, and Quality of Service provisioning. The analysis covers a range of popular machine learning techniques, such as Decision Tree, KNearest Neighbours, Naive Bayes, Logistic Regression, Multi-Layer Perceptron, and Feed Forward Neural Network with a sigmoid activation function. Each technique's strengths and weaknesses are discussed, along with the factors that influence the selection of a particular technique. Ultimately, the choice of machine learning approach depends on data characteristics, performance requirements, and available resources. The demand for prompt and accurate classification of Internet traffic has been steadily increasing, driven by the emergence of new applications in the field. Traditional approaches based on port numbers and packet payloads have become insufficient, prompting the adoption of pattern recognition techniques that leverage statistical flow-based features in training samples to classify unknown flows. To ensure real-time identification of traffic types, the chosen method must be capable of swift classification before the entire flow is completed. In this study, a supervised machine learning approach and deep learning techniques are proposed for the identification of various Internet applications. The proposed system exhibits the ability to detect application types based on just a few initial packets within each flow, enabling realtime operation. Promising results were achieved, with the Logistic Regression algorithm attaining the highest accuracy of 80.7%.

Future dynamic multimedia content access via aerial computing system

Multimedia content access sovereignty arises due to the intention of producers to enable subscribers from pre–defined regions access multimedia content. This limits the number of locations (with subscribers) that can access producer content. Therefore, the ability to access multimedia content across previously unconsidered locations is limited. The presented research addresses this challenge and focuses on multimedia content sharing among subscribers in arid and hyper–arid regions. The use of stratosphere based data centres (SBDCs) is proposed. The paper also presents multi–tier network architecture for network traffic management. This ensures that network traffic congestion does not limit access to multimedia content by subscribers across multiple regions. The use of SBDCs increases the number of locations that engage in the sharing of multimedia content. Evaluation shows that the proposed solution increases the number of data sharing locations by (75.8 – 88.2) % on average.

An observational mechanism for detection of distributed denial-of-service attacks

<span>This study proposes a continuous mechanism for detecting distributed denial of service (DDoS) attacks from network traffic data. The mechanism aims to systematically organise traffic data and prepare them for DDoS attack detection using convolutional deep-learning neural networks. The proposed mechanism contains ten phases covering activities, including data preprocessing, feature selection, data labelling, model building, model evaluation, DDoS detection, attack pattern identification, alert creation, notification delivery, and periodical data sampling. The evaluation results suggested that the detection model built based on convolutional deep-learning neural networks and relevant network traffic features provided 97.2% detection accuracy. The study designed a holistic mechanism that considers the systematic network traffic data management for continuous monitoring and good performance of DDoS attack detection. The proposed mechanism could provide a solution for network traffic data management and enhance the existing methods for DDoS attack detection. In addition, it generally contributes to the cybersecurity body of knowledge.</span>

An AI-Driven Intelligent Traffic Management Model for 6G Cloud Radio Access Networks

This letter proposes a novel Cloud Radio Access Network (C-RAN) traffic analysis and management model that estimates probable RAN traffic congestion and mitigate its effect by adopting a suitable handling mechanism. A computation approach is introduced to classify heterogeneous RAN traffic into distinct traffic states based on bandwidth consumption and execution time of various job requests. Further, a cloud-based traffic management is employed to schedule and allocate resources among user job requests according to the associated traffic states to minimize latency and maximize bandwidth utilization. The experimental evaluation and comparison of the proposed model with state-of-the-art methods reveal that it is effective in minimizing the worse effect of traffic congestion and improves bandwidth utilization and reduces job execution latency up to 17.07% and 18%, respectively.

AI-Enabled Cryptographic Key Management Model for Secure Communications in the Internet of Vehicles

Recent advancements in the Internet of Vehicles (IoV) technology have pathed the way for the use of various smart services for the management of urban traffic, including authentication and key management. Key management protocols are an important means of addressing security and privacy concerns. However, they can be resource-intensive in terms of network traffic and workload management, particularly at times of traffic congestion, which in turn can increase the ECU and RSU processing load and adversely impact network communications. This paper introduces a more efficient key management method, named AI-enabled and Layered Key Management (ALKM), which uses an Artificial Intelligence (AI) approach and a layered workflow to reduce network traffic and workload. Specifically, the ALKM distributes dynamic synchronous time-dependent keys among Road Side Units (RSUs) rather than static cryptographic keys. It provides three layers of secure communications: public, tunnel, and hierarchy. The public layer creates a flat secure layer between the Traffic Management Center (TMC) and all RSUs. Using AI-enabled features, the tunnel layer predicts short-term and long-term congestion areas by analysis of the acquired trajectory data, and then establishes a secure communication channel between the selected RSUs and the TMC. In the hierarchy layer, in multiple tiers, the TMC and higher-level RSUs assist lower RSUs in message decryption (but not vice-versa). Our extensive analysis shows that the ALKM generates overall between 48% and 99% less network traffic per generated key than the Master Key method, depending on the operational lifetime of the keys used.

Xen Hypervisor Network Management System

On embedded devices, network traffic management is crucial. One of the most fundamental criteria in projects on which embedded devices are created is connecting devices to one another. Additionally, sharing the internet connection, if there is one, has evolved into a need in today's world. In this project, two different operating systems are running by using hypervisor on the same board. The aim of this study is to connect two operating systems running on Xen Hypervisor using a virtual bridge and share the internet connection. An Embedded Linux distribution created with the Yocto project was run on the host domain (Domain-0 in Xen Hypervisor terminology), and Android Automotive OS was run on the guest domain (DomU in Xen Hypervisor terminology). A special topology has been designed for two virtual domains to communicate. Network management is handled by Embedded Linux, as Embedded Linux runs on Domain-0. Moreover, some scripts have been developed on both domains to implement the designed topology. These scripts enable quick and automatic completion of the required actions. In conclusion, this study describes how to automatically establish a network that provides communication between two virtual domains on Xen Hypervisor running on embedded devices. This paper is applicable to any project, provided the Xen hypervisor framework is used. It offers a reliable and efficient solution to connection needs such as infotainment and cluster systems, especially in systems where communication and internet are becoming more widespread for embedded devices such as automotive.