Traffic Network Optimization Research Articles

Trust-Centric and Economically Optimized Resource Management for 6G-Enabled Internet of Things Environment

The continuous evolvement of IoT networks has introduced significant optimization challenges, particularly in resource management, energy efficiency, and performance enhancement. Most state-of-the-art solutions lack adequate adaptability and runtime cost-efficiency in dynamic 6G-enabled IoT environments. Accordingly, this paper proposes the Trust-centric Economically Optimized 6G-IoT (TEO-IoT) framework, which incorporates an adaptive trust management system based on historical behavior, data integrity, and compliance with security protocols. Additionally, dynamic pricing models, incentive mechanisms, and adaptive routing protocols are integrated into the framework to optimize resource usage in diverse IoT scenarios. TEO-IoT presents an end-to-end solution for security management and network traffic optimization, utilizing advanced algorithms for trust score estimation and anomaly detection. The proposed solution is emulated using the NS-3 network simulator across three datasets: Edge-IIoTset, N-BaIoT, and IoT-23. Results demonstrate that TEO-IoT achieves an optimal resource usage of 92.5% in Edge-IIoTset and reduces power consumption by 15.2% in IoT-23, outperforming state-of-the-art models like IDSOFT and RAT6G.

Optimal roadworks schedule in multi-agent transportation models

In this paper we consider the problem of the optimal roadworks scheduling in the road traffic network optimization. The main goal of this research is to find the optimal order of roadworks and minimize their negative impact on the transportation network throughput. In order to model the dynamics of a transportation system, we propose a large-scale, multi-agent vehicle routing modelling framework for traffic simulation and impact of roadworks on traffic throughput. We present a quick and effective heuristic algorithm for streamlining the road repairs. Finally, the numeric experiment in a real-world setting is conducted. The obtained results indicate the flexibility of the proposed algorithm, which can be effectively applied by decision makers who need to carry out roadworks with limited time and resources.

Analysis of the Application of Artificial Intelligence in Computer Network Technology

The application of artificial intelligence (AI) technology in computer network technology is becoming increasingly extensive and in-depth. This paper analyzes the application of artificial intelligence in network security, network management, Internet of Things, edge computing and network services in detail. In network management, AI technology realizes network traffic optimization, automatic configuration and fault prediction, and improves the efficiency of network management. In the field of Internet of Things, AI technology helps manage and analyze a large amount of device data, supporting the development of smart homes and smart cities. In edge computing, AI technology enhances the data processing capabilities of edge nodes and reduces latency. In network services, artificial intelligence technology optimizes routing, voice recognition and personalized recommendation services. However, the application of artificial intelligence in network technology also faces challenges such as data privacy, computing resources and laws and regulations. This paper aims to provide a comprehensive reference for industry insiders to promote the application and development of artificial intelligence technology in computer networks.

Analysis of the Application of Artificial Intelligence in Computer Network Technology

The application of artificial intelligence (AI) technology in computer network technology is becoming increasingly extensive and in-depth. This paper analyzes the application of artificial intelligence in network security, network management, Internet of Things, edge computing and network services in detail. In network management, AI technology realizes network traffic optimization, automatic configuration and fault prediction, and improves the efficiency of network management. In the field of Internet of Things, AI technology helps manage and analyze a large amount of device data, supporting the development of smart homes and smart cities. In edge computing, AI technology enhances the data processing capabilities of edge nodes and reduces latency. In network services, artificial intelligence technology optimizes routing, voice recognition and personalized recommendation services. However, the application of artificial intelligence in network technology also faces challenges such as data privacy, computing resources and laws and regulations. This paper aims to provide a comprehensive reference for industry insiders to promote the application and development of artificial intelligence technology in computer networks.

A Novel Method for Reducing Vehicle Emissions Utilizing IoT-Based IS-APCPSO Algorithm

ABSTRACT Urban road traffic network optimization based on the Internet of Things (IoT) is one of the important methods to reduce vehicle exhaust and ease traffic congestion. A two-level planning model on road traffic network optimization in IoT and the IS-APCPSO algorithm is deployed, which can effectively solve the above problems. The experimental results show that the total vehicle emissions of the proposed scheme are lower than those of traditional schemes. Moreover, the real-time perception, interactive coupling, and coordinated control of “people-vehicle-road-environment” can be achieved through the use of the IoT. We discuss the sensitivity of the model to establish a scene selection that takes into account different regional and road traffic conditions, avoiding the subjective randomness in model parameter selection. A novel multi-objective method based on IoT proposed in this paper helps to alleviate “urban diseases,” such as traffic congestion, vehicle emissions, and energy waste, and to emphasize the overall benefits of energy-saving and emission reduction in urban road networks. By setting up traffic lights reasonably and guiding the driver behavior, the integrated control and management of traffic flow and exhaust emission under multiple driving cycles are realized.

Navigating Network Traffic: An Exploration of Maze Algorithm Applications in Machine Learning

To Determine network performance and highlight the need for integrated and intelligent solutions in response to the growing amount of data generated by smart devices. It launches a link between web browsing and queuing, emphasizing the importance of factors such as time and availability. The proposed method is evaluating some studies on machine learning techniques used for network traffic forecasting, Internet tools, and techniques for analyzing traffic flows. Network traffic is the data flow across a computer network, emphasizing the need for scalable and intelligent solutions due to the rise in data generation from smart devices. Additionally, it draws an analogy between navigating network paths and solving mazes in everyday life based on factors like time, effort, and convenience. Navigating paths resembles solving mazes, as individuals choose routes from origin to destination based on factors like time, effort, and convenience, akin to maze-solving techniques. Algorithms use real-time analytics, machine learning, and historical data to improve network efficiency. Finally, the maze algorithm is measured the network traffic optimization. It is very useful in machine learning, real-time analysis, and historical data to improve network performance.

Detection and Optimization of Traffic Networks Based on Voronoi Diagram

Traffic peak is an important parameter of modern transport systems. It can be used to calculate the indices of road congestion, which has become a common problem worldwide. With accurate information about traffic peaks, transportation administrators can make better decisions to optimize the traffic networks and therefore enhance the performance of transportation systems. We present a traffic peak detection method, which constructs the Voronoi diagram of the input traffic flow data and computes the prominence of candidate peak points using the diagram. Salient peaks are selected based on the prominence. The algorithm takes O(n log n) time and linear space, where n is the size of the input time series. As compared with the existing algorithms, our approach works directly on noisy data and detects salient peaks without a smoothing prestep and thus avoids the dilemma in choosing an appropriate smoothing scale and prevents the occurrence of removing/degrading real peaks during smoothing step. The prominence of candidate peaks offers the subsequent analysis the flexibility to choose peaks at any scale. Experiments illustrated that the proposed method outperforms the existing smoothing-based methods in sensitivity, positive predictivity, and accuracy.

Comparative Analysis of Selected Open-Source Solutions for Traffic Balancing in Server Infrastructures Providing WWW Service

As the number of users increased over the years, pioneering technologies and solutions in given areas ceased to be sufficient even in terms of performance. Therefore, there was a need for their development or even redesign and redefinition. One of the issues that undoubtedly has a huge impact on the current shape of the global network and the way information is processed in it is the issue of traffic balancing, especially the one in the server infrastructure related to the WWW service, providing users with the possibility of efficient and reliable web browsing. The paper presents a comparative analysis of selected open-source solutions used for traffic balancing in server infrastructures providing WWW service based on selected criteria. The designed architecture of the test environment and the test results of selected tools implementing the traffic-balancing functionality are presented. Methodologies, test plans, and comparison criteria are proposed. A comparative analysis of results based on specific criteria was performed. The balance between network traffic optimization and load balancing distribution among servers is crucial for the development of energy-efficient data processing centers.

Real-time signal-vehicle coupled control: An application of connected vehicle data to improve intersection safety

The proliferation of Connected Vehicles and their ability to collect a large amount of data presents an opportunity for the real-time safety optimization of traffic networks. At intersections, Adaptive Traffic Signal Control (ATSC) systems and dynamic speed advisories are among the proactive real-time safety interventions that can assist in preventing rear-end collisions. This study proposes a Signal-Vehicle Coupled Control (SVCC) system incorporating ATSC and speed advisories to optimize safety in real-time. By applying a rule-based approach in conjunction with a Soft-Actor Critic RL framework, the system assigns speed advisories to platoons of vehicles on each approach and extends the current signal time accordingly. Dynamic traffic parameters are collected in real-time and are used to estimate the current conflict rate at the intersection, which is then used both as an input to the model and to evaluate performance. The system was tested on two different intersections modeled using real-world data through the simulation platform VISSIM. Traffic conflicts were reduced by 41–55%, and vehicle delay was reduced by 21–24%. The results also show that the system functions at lower levels of market penetration, with diminishing returns beyond 50% MPR. The proposed system presents an SVCC framework that is both effective and low in computational intensity to optimize safety at signalized intersections.

ANALYSIS OF THE IMPACT OF INFORMATION SECURITY TOOLS ON NETWORK BANDWIDTH

The paper considers the construction of secure communication networks and their elements. The analysis of simulation modeling of the communication network segment under attack conditions, the influence of information security tools on network traffic, and examples of calculations are given. The analysis of the method of network traffic optimization is performed taking into account the use of various information security products, as well as options for developing a security policy. The results of experiments under various conditions of information security are given: in the absence of an attack without the use of information security products; in the absence of an attack using information security products; under a DoS attack without the use of information security products; under a DoS-attack with the use of information security products. Several methods for optimizing traffic using information security products are considered. A security policy is considered to achieve a given level of information security in a computer system with a minimum reduction in the network traffic and the cost of information security products.

An optimized IS-APCPSO algorithm for large scale complex traffic network

Chaotic particle swarm optimization algorithm is improved by incorporating antibody concentration, adaptive propagation, optimization mechanism of the multi-population evolution strategy, elite particles chaotic traversal mechanism and constraint processing mechanism. In this paper, an improved adaptive propagation chaotic particle swarm optimization algorithm based on immune selection (IS-APCPSO algorithm for short) is proposed. The performance of several algorithms has been compared by multimodal function, functions with high dimensional and complex constraints, bi-level programming function and a classic example of traffic network optimization. The experimental results prove that the proposed algorithm in accelerating convergence rate, increasing the diversity of particles, and preventing premature phenomenon is effective. The novel algorithm is expected to be used in the model solution of large-scale complex traffic network optimization problem.

Social Network chatting apps network traffic optimization

Objectives: This paper proposes a method to improve the energy efficiency delivery of instant message group chat messages. A user interaction with instant messenger using in smart phone causes high power state due to undisciplined network transmission between network interface (3G or Wi-Fi) and networking operator. Methods/Statistical Analysis: We investigated messaging network traffic pattern in social network chatting apps. In our experimental findings, instant messaging group chat messages (multi-user chatting between friends, colleagues family etc.) delivered by chat server is not energy efficient by We Chat. In our dataset, we found problem in pending group chat messages and not cached play songs in instant messaging app. An energy efficient friendly network traffic is to fetch contains like sound, text, images, video running on smart phone operating system possibly in low power and acquire operating system go back to sleep as soon as possible. In our case user does not participate in group chat. Instant messaging chatting apps like We Chat, QQ messenger. Social network chatting apps are replacing use of regular Short Message Service (SMS) service by mobile carriers, because of free to use them. In recent many researchers proposed bundled, batched and aggregate a data transmission technique to overcome undisciplined data transmission power wastage. In instant messaging app a simple greeting messages cannot have bundled, because user cannot to errant to delay messages. Findings: We had two case studies traffic pattern offline user scenario and online user scenario case. To achieve this goal, we achieved firstly, to reduce smart phone network interface active time by intimate user by light weight push message about group chat pending messages notification in 3G environment. Secondly, instant messaging apps received songs keep in pre-cached to reduce data packets requests in 3G. Application/Improvement: In our propose model, we delayed group chat traffic pattern and play songs in 3G connectivity. In addition, its play songs and receive group chat messages in Wi-Fi connectivity which cause lower energy battery drain in smart phone. Keywords: Energy Efficient, Network Interface 3G or Wi-Fi, Network Traffic, Social Network Apps

Study on road section environmental traffic capacity model and algorithm under double constraints

The coordinated development of city traffic and environment is a key research content in traffic field in twenty-first Century. Among them, road section environmental traffic capacity analysis is one of the important research issues. It can provide solid theoretical basis and reliable data support for road network traffic optimization control, road traffic pollution control and city traffic structure optimization. This paper analyzed main factors which impacted environmental traffic capacity from two aspects, including road capacity constraint conditions and road traffic pollution control constraint conditions. Then, road section environmental traffic capacity optimization model was established, and method of improved augmented Lagrange function was used to solve the model. Case study showed that, (1) The environmental traffic capacity optimal model and methodology were effective; (2) In order to ensure road section environmental traffic capacity greater than (or equal to) road capacity, some measures could be taken including adjusting motor vehicle type proportion as well as improving emission characteristics of motor vehicles exhausting pollutants.

Air traffic network optimization via Laplacian energy maximization

Designing robust air traffic network is an ongoing research effort that seeks to improve the network robustness when one or more air routes are added to the existing network. We demonstrate that Laplacian energy is a fair and promising measure of network robustness based on a case study of a real air traffic network and extensive numerical experiments. Therefore, this paper aims at maximizing the Laplacian energy to enhance network robustness. The corresponding weighted Laplacian energy maximization problem is formulated as flight route addition problem to facilitate practical operations. Three methods are proposed to solve the flight route addition problem, including tabu search, greedy algorithm and integer program. Their trade-off between optimality performance and computational efficiency is demonstrated through the numerical results on a scale-free network. A case study on a real air traffic network is also included for further investigation.

Research on Path Optimization of Urban Traffic Guidance System

With the rapid development of economics and technology; the number of vehicles has largely increased. In this paper, traffic guidance and traffic control systems were researched as well as the Internet of Things (IOT). The author tried to combine these three parts to send traffic data to road users so as to let them choose the best route to travel. Meanwhile, traffic network optimization has been realized to reduce traffic congestion areas. This paper has optimized regional traffic signal control systems based on IOT, traffic guidance as well as traffic assignment, involved data sources, IOT design patterns, data collection as well as the relationship between guidance obeisance rate and traffic jam. It also involved the definition of ideal traffic shortest routes, planning and designing of traffic control systems. Results and researches could hope to combine with reality in order to reduce traffic congestion.

Simulation System for Optimizing Urban Traffic Network Based on Multi-scale Fusion

The research on Urban traffic simulation is more widely, But there are some problems, For example, the traffic system in the spatial scale is from microscopic to macroscopic highly integrated. In the time scale is from the second grade to the height of continuous integration. Simulation and objective traffic system complexity determines that any single scale are difficult to be better traffic phenomena. On this basis, the simulation system was proposed based on multi-scale fusion, from the macro, meso, micro in three dimensions using the corresponding algorithm, and carries on the design to the system. Finally, Based on multiscale fusion of urban traffic network optimization simulation system has carried on the experiment. Through the example of Nanjing Fujian road and Traffic Signal Priority of the three arches to analyze network simulation experiment. The experiment proves that the system is practical and reliable.

An ISP-friendly inter-overlay coordination framework for multiple coexisting P2P systems

As Peer-to-Peer (P2P) systems are widely deployed in the Internet, P2P traffic control becomes a challenge for Internet Service Providers (ISPs) and P2P system vendors. Some recent works consider the interaction between ISPs and P2P systems, and propose ISP-friendly P2P traffic control mechanisms for reducing cross-ISP P2P traffic. In this paper, we consider another fundamental problem: the performance optimization and the interaction among multiple coexisting P2P systems. Specifically, we propose an ISP-friendly inter-overlay coordination framework (COOD) for controlling P2P traffic. The COOD framework consists of three important components: network traffic optimization, P2P service differentiation, and ISP policy enforcement. Our packet-level simulation results show that, compared with current P2P traffic control mechanisms, the COOD framework can provide better overall performance to multiple coexisting P2P systems, achieve service differentiation, and implement flexible mechanisms to effectively control cross-ISP P2P traffic.

Reversible lane-based traffic network optimization with an advanced traveller information system

In the case of two-way traffic, there are two opposite-direction flows on every road and serious unsymmetrical flows exist in rush-hour periods. One of the primary methods used for handling this kind of traffic flow is the use of reversible lanes. Which and how many lanes should be adjusted are optimization problems. They can be treated as a network design problem, i.e. the optimal decision on the resource distribution of a street and highway system in response to a growing travel demand. This article studies a new form of transportation network design problem by performing the strategy of reversible lanes. In order to describe this problem, a bi-level programming model is given in which the upper level model is to make the total system cost and flow entropy minimum, while the lower level is a stochastic user equilibrium assignment with an advanced traveller information system. Finally, the numerical example shows that, by adopting the reversible lane and advanced traveller information system, the total system cost will be greatly reduced.