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Low-Earth Orbit Satellite Constellations (LEO-SCs) provide global, high-speed, and low latency Internet access services, which bridges the digital divide in the remote areas. As inter-satellite links are not supported in initial deployment (i.e. the Starlink), the communication between satellites is based on ground stations with radio frequency signals. Due to the rapid movement of satellites, this hybrid topology of LEO-SCs and ground stations is time-varying, which imposes a major challenge to uninterrupted service provisioning and network management. In this paper, we focus on solving two notable problems in such a ground station-assisted LEO-SC topology, i.e., traffic engineering and fast reroute, to guarantee that the packets are forwarded in a balanced and uninterrupted manner. Specifically, we employ segment routing to support the arbitrary path routing in LEO-SCs. To solve the traffic engineering problem, we proposed two source routings with traffic splitting algorithms, Delay-Bounded Traffic Splitting (DBTS) and DBTS+, where DBTS equally splits a flow and DBTS ​+ ​favors shorter paths. Simulation results show that DBTS ​+ ​can achieve about 30% lower maximum satellite load at the cost of about 10% more delay. To guarantee the fast recovery of failures, two fast reroute mechanisms, Loop-Free Alternate (LFA) and LFA+, are studied, where LFA pre-computes an alternate next-hop as a backup while LFA ​+ ​finds a 2-segment backup path. We show that LFA ​+ ​can increase the percentage of protection coverage by about 15%.

An important technique to optimize a network and improve network robustness is traffic engineering. As traffic demand increases, traffic engineering can reduce service degradation and failure in the network. To allow a network to adapt to changes in the traffic pattern, the research community proposed several traffic engineering techniques for the traditional networking architecture. However, the traditional network architecture is difficult to manage. Software Defined Networking (SDN) is a new networking model, which decouples the control plane and data plane of the networking devices. It promises to simplify network management, introduces network programmability, and provides a global view of network state. To exploit the potential of SDN, new traffic engineering methods are required. This paper surveys the state of the art in traffic engineering techniques with an emphasis on traffic engineering for SDN. It focuses on some of the traffic engineering methods for the traditional network architecture and the lessons that can be learned from them for better traffic engineering methods for SDN-based networks. This paper also explores the research challenges and future directions for SDN traffic engineering solutions. [ABSTRACT FROM AUTHOR]

Abstract Software-defined networking (SDN) revolutionizes network administration by centralizing control and decoupling the data plane from the control plane. Despite its advantages, the escalating volume of network traffic induces congestion at nodes, adversely affecting routing quality and overall performance. Addressing congestion has become imperative due to its emergence as a fundamental challenge in network management. Previous strategies often faced drawbacks in handling congestion, with issues arising from the inability to efficiently manage heavy packet surges in specific network regions. In response, this research introduces a novel approach integrating a multiplicative gated recurrent neural network with a congestion-aware hunter prey optimization (HPO) algorithm for effective traffic management in SDN. The framework leverages machine learning and deep learning techniques, acknowledged for their proficiency in processing traffic data. Comparative simulations showcase the congestion-aware HPO algorithm's superiority, achieving a normalized throughput 3.4–7.6% higher than genetic algorithm (GA) and particle swarm optimization (PSO) alternatives. Notably, the proposed framework significantly reduces data transmission delays by 58–65% compared to the GA and PSO algorithms. This research not only contributes a state-of-the-art solution but also addresses drawbacks observed in existing methodologies, thereby advancing the field of traffic engineering and congestion management in SDN. The proposed framework demonstrates notable enhancements in both throughput and latency, providing a more robust foundation for future SDN implementations.

As a result of the ubiquitous network applications and services, exacerbated by the overarching digital revolution the need and demand for efficient and dependable connectivity solutions have surged to unprecedented levels. Quality of Service (QoS)-based routing has emerged as a critical solution, enabling service differentiation, efficient resource allocation, and improved network performance. In this study, we introduce a novel Genetic Algorithm-powered QoS-aware Cross-Network Traffic Engineering framework, GATE-BC, at the confluence of Software Defined Networking (SDN) and Blockchain (BC) technologies. GATE-BC orchestrates end-to-end (E2E) QoS traffic, providing resource-efficient, reliable, and latency-tolerant delivery of intelligent network services in BC-enabled SDNs. Leveraging BC features such as decentralization, transparency, and immutability, GATE-BC eliminates the need for centralized entities in QoS-supported cross-network routing models. We compare GATE-BC framework with three other traffic management and engineering approaches: QoSChain (QC), Hierarchical Routing Approach (HRA), and Distributed Routing Approach (DRA). The extensive simulations reveal that GATE-BC outperforms the other routing strategies in terms of Path Setup Time (PST), Network Message Overhead (NMO), Request Acceptance Ratio (RAR), Network Bandwidth Consumption (NBC), Average Path Length (APL), and Average Network Length (ANL) metrics under various network topologies. Furthermore, GATE-BC employs three different feasible path selection strategies based on bandwidth (GATE-BC_BW), delay (GATE-BC_D), and reliability (GATE-BC_R) QoS parameters to satisfy the service levels requested.

This article embarks on a detailed examination of the square Dolní Náměstí in the town of Odolená Voda, concentrating on the implications of implementing shared spaces from perspectives of safety and traffic management. In the study advanced surveillance technologies were employed to systematically document the movements of a variety of road users, serving to pinpoint potential conflict zones. This designation underscores the necessity to reassess urban design and traffic control strategies to meet modern safety standards. Efforts were directed towards aligning urban infrastructure with pedestrian movement patterns, aiming to diminish safety risks and augment accessibility. The study hypothesizes that certain strategic alterations in urban design, such as the realignment of pedestrian pathways and the imposition of vehicle speed controls, could considerably improve the quality of shared spaces. Central to this research is the ambition to forge a new methodological framework for assessing the capacity of shared spaces mathematically. This initiative seeks to fill a significant research gap by quantitatively evaluating the capacity of shared spaces to fulfil a varied set of urban mobility requirements. This framework aims to establish a foundation for the systematic appraisal and enhancement of shared spaces. Recommendations for urban planners and traffic engineers are presented, promoting a design strategy that merges pedestrian preferences with traffic management objectives to cultivate safer, more efficient, and universally accessible urban areas. These recommendations endeavor to steer the evolution of shared spaces towards a harmonious balance of user needs. This work aims to make a substantive contribution to urban planning, traffic management, and pedestrian safety domains, paving the way for future exploratory studies and practical deployments in shared space advancements. It emphasizes the critical need to integrate both vehicular and pedestrian considerations into urban design processes, striving to create urban spaces conducive to sustainable mobility and improved urban living standards.

Lane changing behavior, a pivotal aspect of vehicular dynamics, significantly influences traffic flow, safety, and overall road network efficiency. This review article synthesizes the current body of knowledge surrounding lane changing behavior, encompassing both mandatory and discretionary lane changes. The intricate interplay between various factors, including speed differentials, driver characteristics, road geometry, and external stimuli, shape the decision-making processes underlying lane changes. The article explores the underlying psychological, cognitive, and socio-demographic influences on lane changing behavior, highlighting the role of emerging technologies such as advanced driver assistance systems (ADAS) and vehicle-to-vehicle (V2V) communication. Furthermore, the review delves into existing models and methodologies employed to analyze lane changing behavior, addressing their limitations and potential for advancement. Real-world applications, such as work zones and incident management, are examined to underline the practical implications of understanding and predicting lane changing behavior. By presenting a comprehensive overview of the subject, this review aims to deepen the understanding of the complexities inherent in lane changing behavior, facilitating informed decision-making for traffic management, safety enhancement, and future research endeavors.

This research aims to examine the traffic road safety curriculum, which is the extraordinary curriculum for children in Thailand. This educates about traffic road safety to children based on traffic engineering knowledge. The entire content of the curriculum is based on case studies that depict both hazardous and safe situations. It covers all road users, including pedestrians, intersection rules, private vehicle users, and public transportation users. In this research, animations are used instead of actual crash recordings to minimize the negative impact of severity. As this curriculum is new and not a part of the standard curriculum for children in Thailand, ensuring an effective curriculum and learning processes is crucial. Therefore, assessing the curriculum from actual users is one of the approaches to check and maintain the effectiveness of the curriculum. Direct assessments, both formative and summative, are conducted to evaluate children's learning abilities regarding traffic safety before and after training. Statistical analysis of the data reveals that the fundamental knowledge of traffic safety in children increases by 20.28 percent after training. The study shows that students are more likely to change their behaviors towards increased safety, with an average change of 34.30 percent. When evaluating satisfaction with the traffic safety training activities, most participating students express a high level of satisfaction with the teaching materials and lesson contents. It is evident that the curriculum is highly effective in enhancing learning skills while providing an enjoyable and participatory experience. Ultimately, the ones who benefit from this curriculum are the teachers who implement it and the children who will come to learn and absorb the knowledge and practices related to safety. The hope is that this curriculum will help increase awareness of safety among children and have a positive impact on their road usage behavior, making it safer in the future. [ABSTRACT FROM AUTHOR]

Traffic Engineering (TE) enables management of traffic in a manner that optimizes utilization of network resources in an efficient and balanced manner. However, existing TE solutions face issues relating to scalability and complexity. In recent years, Segment Routing (SR) has emerged as a promising source routing paradigm. As one of the most important applications of SR, Segment Routing Traffic Engineering (SR-TE), which enables a headend to steer traffic along specific paths represented as ordered lists of instructions called segment lists, has the capability to overcome the above challenges due to its flexibility and scalability. In this paper, we conduct a comprehensive survey on SR-TE. A thorough review of SR-TE architecture is provided in the first place, reviewing the core components and implementation of SR-TE such as SR Policy, Flexible Algorithm and SR-native algorithm. Strengths of SR-TE are also discussed, as well as its major challenges. Next, we dwell on the recent SR-TE researches on routing optimization with various intents, e.g., optimization on link utilization, throughput, QoE (Quality of Experience) and energy consumption. Afterwards, node management for SR-TE are investigated, including SR node deployment and candidate node selection. Finally, we discuss the existing challenges of current research activities and propose several research directions worth of future exploration.

Software-defined networking (SDN) revolutionizes network administration by centralizing control and decoupling the data plane from the control plane. Despite its advantages, the escalating volume of network traffic induces congestion at nodes, adversely affecting routing quality and overall performance. Addressing congestion has become imperative due to its emergence as a fundamental challenge in network management. Previous strategies often faced drawbacks in handling congestion, with issues arising from the inability to efficiently manage heavy packet surges in specific network regions. In response, this research introduces a novel approach integrating a multiplicative gated recurrent neural network with a congestion-aware hunter prey optimization (HPO) algorithm for effective traffic management in SDN. The framework leverages machine learning and deep learning techniques, acknowledged for their proficiency in processing traffic data. Comparative simulations showcase the congestion-aware HPO algorithm's superiority, achieving a normalized throughput 3.4–7.6% higher than genetic algorithm (GA) and particle swarm optimization (PSO) alternatives. Notably, the proposed framework significantly reduces data transmission delays by 58–65% compared to the GA and PSO algorithms. This research not only contributes a state-of-the-art solution but also addresses drawbacks observed in existing methodologies, thereby advancing the field of traffic engineering and congestion management in SDN. The proposed framework demonstrates notable enhancements in both throughput and latency, providing a more robust foundation for future SDN implementations. [ABSTRACT FROM AUTHOR]

Perkembangan penggunaan internet di Indonesia saat ini mengalami peningkatan yang sangat pesat. Dilihat dari peningkatan jumlah pengguna internet tersebut maka secara tidak langsung fitur dan layanan yang disediakan turut berkembang. Salah satu fitur tersebut adalah layanan video dan audio. Layanan video dan audio termasuk dalam layanan multimedia streaming. Pertukaran informasi yang begitu cepat dan beragam menjadi alasan bahwa jenis file video lebih banyak dinikmati karena sudah mengandung informasi gambar bergerak dan audio. Namun terdapat beberapa permasalahan yang mempengaruhi performansi dari video streaming, seperti laju transmisi yang lambat, sehingga diperlukan QoS (Quality of Service) yang baik pada sisi streaming server. Maka dari itu dikembangkan sebuah jaringan MPLS Traffic Engineering yang menggabungkan metode Differentiated Service untuk menjawab permasalahan tersebut. Pada penelitian ini diimplementasikan jaringan MPLS TE dan jaringan MPLS TE Diffserv pengujian dilakukan pada layanan video streaming. Hasil dari penelitian didapatkan bahwa nilai delay,jitter,throughput dan packet loss mengalami perbaikan setelah ditambahkan metode Differentiated Service. Untuk nilai delay berkisar pada rentang 150 ms, nilai throughput berkisar pada rentang 55000 bits/s hingga 56000 bits/s,nilai standar deviasi jitter pada rentang 300 ms dan untuk packet loss 0%.

The immense potential of optical networks provides a highly favorable option for addressing rapidly increasing bandwidth needs. Elastic Optical Networks (EONs) exhibit considerable potential in enhancing availability, failure resilience, load balancing, and efficient resource allocations, rendering them a viable option for forthcoming high-speed networks. The integration of Software-Defined Networking (SDN) architecture with EONs, which separates the data and control planes, results in a dynamic and cooperative combination known as Software-Defined Optical Networking (SDON). However, the effective allocation of spectrum poses operational difficulties in this context. The proposed study introduces a framework called Cross-ISP (Internet Service Provider) Traffic Engineering enhanced by Particle Swarm Optimization (CITE-PSO) that is designed to facilitate Quality of Service (QoS)-focused cross-ISP spectrum assignment in SDONs through the integration of blockchain technology. The proposed framework aims to eliminate the need for centralized mediators while ensuring effective coordination of inter-ISP traffic based on QoS considerations. The research presents a novel application of blockchain technology that increases network efficiency and minimizes QoS signaling overhead during inter-ISP routing in SDONs. The proposed framework is evaluated under Hop-by-Hop Wavelength Switching (HWS) and Border-Node-Only Wavelength Switching (BWS) to assess its performance. The simulation results demonstrate that the CITE-PSO framework is proficient in managing the inter-ISP routing with QoS capabilities in the SDON architecture. This proficiency is measured by the various metrics across HWS and BWS scenarios.

In the recent ten years, with the development of new applications through Internet such as multimedia or networked control applications, users need more and more quality of service (QoS). However, the requested QoS is not the same depending on the application. Most of the new models to manage internet traffic are based on specific QoS criteria which should be optimized. This paper presents main multiprotocol label switching (MPLS) approaches such as MPLS adaptive traffic engineering (MATE), load distribution in MPLS (LDM) and load balancing over widest disjoints paths (LBWDP) that are new models for traffic engineering. It also introduces periodic multi-step (PEMS) algorithm that adapts the offered quality depending on the class of the routed traffic.

The article discusses the development and implementation of periodical's TrafficWiki, an online resource aimed at modernizing traffic engineering knowledge and providing up-to-date information to students and practitioners in the field.

This research addresses multiple challenges faced by ship networks, including limited bandwidth, unstable network connections, high latency, and command priority. To solve these problems, we used reinforcement learning-based methods to simulate traffic engineering in ship networks. We focused on three aspects—traffic balance, instruction priority, and complex network structure—to evaluate reinforcement learning performance in these scenarios. Performance: We developed a reinforcement learning framework for ship network traffic engineering that treats the routing policy as the state and the network state as the environment. The agent generates routing changes and uses actions to optimize traffic services. The experimental results show that reinforcement learning optimizes network traffic balance, reasonably arranges instruction priorities, and copes with complex network structures, greatly improving the network's quality of service (QoS). Through an in-depth analysis of the experimental data, we noticed that network consumption was reduced by 9.1% under reinforcement learning. Reinforcement learning effectively implemented priority routing of high-priority instructions while reducing the occupancy rate of the edge with the highest occupancy rate in the network by 18.53%. [ABSTRACT FROM AUTHOR]

Recently, telesurgery, as a branch of telemedicine, is growing at great speed. In telesurgery, a remote surgeon can communicate with operating room through communication networks in order to perform remote operation. Because human life is at stake in telesurgery, it is necessary to implement appropriate Traffic Engineering (TE) techniques to manage and route telesurgical data traffics over communication networks. Implementing proper TE technique can increase Quality of Experience (QoE) for the remote surgeon, and as a result, it increases the accuracy and performance of telesurgery. In recent years, the advent of Software Defined Network (SDN) has facilitated network providers to develop efficient TE methods to guarantee QoE for their customer applications. In this paper, a novel multi-objective TE technique called SMOTE is proposed to guarantee QoE for telesurgery applications over SDN. The goal of this paper is to enable a network administrator to provide a satisfactory remote operation by providing QoE for the remote surgeon. The objectives, which are considered in our proposed method, are the minimization of end-to-end delay and packet loss. SMOTE uses Non-dominated Sorting Genetic Algorithm (NSGA-II) a well-known multi-objective algorithm, to solve the optimization model for calculating optimal routes between the remote surgeon and the operating room. We conducted comprehensive simulations to evaluate the performance of SMOTE in terms of QoE. Simulation results confirmed that SMOTE guarantees QoE in telesurgery. [ABSTRACT FROM AUTHOR]

This study proposes a dynamic traffic engineering method to improve network resource utilization efficiency without affecting important services in an integrated service network where multiple grades of service are accommodated. Network resource utilization is biased over time because future fluctuations in traffic demand are unpredictable. Although a service network provider prefers to re-optimize network resource utilization using a dynamic traffic engineering, it is often superfluous for users who do not want their mission-critical communications, for example, executive web meetings or online surgery, to be affected. Therefore, we classified traffic into very important packets and commoners, and only commoners were dynamically optimized. To classify traffic, we assigned the identifier to a packet on the application side. In this approach, only the application and edge nodes of the network required modification; the core network routers were unaffected. Furthermore, we established the architecture of the edge node using a software-defined network controller and P4 switch. We also provided an algorithm for optimization based on linear programming and demonstrated that it is possible to improve network resource utilization efficiency without affecting very important packets and that the computational and memory costs are practical.

Effective teaching methods for the coming generations of pragmatic students and teachers are evolving towards much greater involvement on all sides, attempting to bridge real life and academia in innova-tive ways. Our theoretical foundation lays upon the Triple Helix, n-Tuple Helix theory and the innova-tion triangle, but in a problem-based multidisciplinary setting. After introducing and validating the Mul-tiCreation approach in teaching/learning for the business-academia collaboration, where multiple disci-plines, diverse profiles of students, professors, and managers have taken up various roles to address relevant business issues, we were encouraged to expand its applicability beyond the business world - toward societal problems. We retained the problem-based and participatory learning but shifted the positioning of the problem towards the safety of primary school children, incorporating economic, pro-ject-based, managerial, traffic engineering, logistical, regulatory, and governance issues. By including two primary schools, collaborating with their principals, advisory teams, and teachers, as well as chil-dren and parents, in two different cities, we aimed to provide complete research, engineering and educa-tion for the stakeholders so that the schools could just hand in documentation to the relevant municipal bodies to be acted upon effectuation. The involved students came from two faculties -of economics and traffic engineering, steered by three university professors on the subjects of project management and basic and advanced techniques for traffic management. The blended learning components took place as prescribed in the MultiCreation approach, during two semesters of two academic years. They combined countermeasures and solutions. The MultiCreation approach has been enhanced regarding its components and workflow and its effectiveness has been validated in a multi-stakeholder environ-ment of direct and indirect educational beneficiaries.

This study employs a hotspot areas-based data envelopment analysis (DEA) to investigate the coupling efficiency between bike-sharing demand and land use. Hotspot areas are used as decision-making units and re-sampling data are imposed spatial constraints. The elbow method is used to select the crucial indicators after exploring the correlation between coupling efficiency evaluation indexes using the grey correlation model. Since this coupling efficiency evaluation belongs to a 'multi-input and single-output' system, DEA was introduced to build a quantitative model for bike-sharing demand and land-use coupling efficiency. This paper also identifies the factors that restrict bike-sharing demand generation. More importantly, different spatial scenarios are deployed to demonstrate heterogeneous characteristics of DEA efficiency in different locations. The results of a case study in Beijing, China show that DEA efficiency generally exhibits a trend from low to high from the inside to the outside of the urban centre. The land-use factors with relatively high redundancy rates are financial insurance facilities, hotels and living facilities. [ABSTRACT FROM AUTHOR]

Aiming at the current situation and existing problems of traffic engineering facilities on the straight section of grassland highway, the indoor simulation experiments were carried out and test scene was built to study the driver’s heart rate variability under six kinds of information guantity conditions. By using grey relational degree analysis method and establishing regression mathematical curve model, this paper quantitatively studied the variation rule of driver’s heart rate variability index with the information guantity of traffic engineering facilities, and the appropriate range of information guantity was determined. The test results showed that: the optimum HRV indexes for drivers with the change of traffic engineering facilities information guantity were SDNN and LF/ HF. Experienced and inexperienced drivers had a relatively high level of psychological adjustment, concentration and attention to traffic engineering facilities under the condition of Z2 (10-20 bits/ km) information guantity, it was more conducive to safe driving. With the change of traffic engineering facilities information guantity, compared with inexperienced drivers, experienced drivers paid more attention to traffic engineering facilities, had higher level of psychological adjustment, and paid more attention. [ABSTRACT FROM AUTHOR]

This research began when observations were made on any-to-any-connection services that require QoS (Quality of Service) and tunnel measurements. Tunnel is a technique to interconnect between local networks that are separated through a public network. Research questions found looping caused by traffic issues when sending data in the MPLS service layer-2 tunneling service. Furthermore, this research hypothesis proposes optimizing QoS performance in the application of the SR-TE (Segment Routing-Traffic Engineering) method in the MPLS (Multi-Protocol Label Switching) network and analyzing traffic based on MAC addresses using the looping-protection method. This research contributes to optimizing the MPLS network and is a recommended solution for simplifying control-plane operation, especially the SR-TE method and looping-protection in the L2- VPN MPLS service. The purpose of this study is to analyze the performance of MPLS networks, as well as analyze the application of L2-VPN (Layer 2 Virtual Private Network) MPLS networks. The targeted TKT (Technology Readiness Level) is 3rd-Level, which is an analytical and experimental proof of the MPLS tunnel network model on Layer-2 VPN services. The results of the research concluded that testing the SR-TE method is a solution to simplify the process of labeling data traffic that is global labelling, then the looping-protection method is a solution to eliminate looping indications. The QoS obtained has also shown an "excellent" category based on TIPHON standards.

This article presents an integrated educational module for undergraduate traffic engineering at the microscale level. When dealing with a complex transportation system, the education program should cultivate students' acquisition of both in‐depth traffic specialization and breadth of engineering knowledge. For the educational instruction of the signal phase design, this article presents an integrated design that includes several engineering subjects: signal phase design, circuits and electronics, and object‐oriented programming (OOP). Due to the lengthy evolution of signal phase design and the high‐end industrial standards for this issue, this well‐structured problem requires an instructional design to adopt a worked example that integrates varied engineering domains. The integrated design session is intended to deliver education through designing a miniature signal controller while creating an immersive situation and encouraging social teamwork. Feedback from participating students has been positive, indicating the achievement of the planned learning objectives and better mastery of engineering practices. [ABSTRACT FROM AUTHOR]

To find the appropriate range of information quantity, we studied how the information quantity of traffic engineering facilities (TEFs) on straight roads of grassland highways affects a driver’s eye movements. We used a combination of survey, statistics, analysis of variance, and the establishment of virtual scene to do this research, and carried out simulated driving tests at six levels (Z0, Z1, Z2, Z3, Z4, and Z5) of TEF information quantity. The driver’s fixation duration, visual search breadth, and glance speed were evaluated in a quantitative way. Results showed that the information quantity had a significant impact on eye movements. It is concluded that the information quantity from 0 to 10 bits/km may cause problems to drivers, whereas the information quantity of 40 bits/km serves as the limit. The information quantity from 30 to 40 bits/km is the appropriate one for TEF on grassland highways.

Network Function Virtualization (NFV) is an approach that provides a network service provider with agility and cost-efficiency in managing 6G network services. Standard traffic engineering rules are known limited in assuring a very stringent delay requirement in NFV when a traffic flow is required to follow a sequence of network functions scattered in data center networks. This paper proposes an innovative model and algorithm of traffic engineering for service function chaining (SFC) to maximize cost-efficiency under a delay-guarantee constraint. We first formulate the problem as a mixed-integer linear programming model for obtaining the optimal solution. We then propose an algorithm based on the reinforcement learning principles for finding an approximation solution in a large-scale problem with the dynamics of service demands. Numerical results under both real-world datasets and synthetic network topologies demonstrate that our proposed model and algorithm allow an NFV service provider (NSP) to place a virtual network function and steer a traffic flow efficiently in terms of energy cost for a delay-guarantee SFC. Importantly, the results provide an insight into the optimal and approximation solutions for an NSP to select a suitable traffic engineering approach with regard to network dynamics.

Quality of Service (QoS) refers to techniques that function on a network to dependably execute high-priority applications and traffic reliably run high-priority applications and traffic even when the network’s capacity is limited. It is expected that data transmission over next-generation WSNs (Wireless Sensor Networks) 5G (5th generation) and beyond will increase significantly, especially for multimedia content such as video. Installing multiple IoT (Internet of Things refers to the network of devices that are all connected to each other) nodes on top of 5G networks makes the design more challenging. Maintaining a minimal level of service quality becomes more challenging as data volume and network density rise. QoS is critical in modern networks because it ensures critical performance metrics and improves end-user experience. Every client attempts to fulfill QoS access needs by selecting the optimal access device(s). Controllers will then identify optimum routes to meet clients’ core QoS needs in their core network. QoS-aware delivery is one of the most important aspects of wireless communications. Various models are proposed in the literature; however, an adaptive buffer size according to service type, priority, and incoming communication requests is required to ensure QoS-aware wireless communication. This article offers a hybrid end-to-end QoS delivery method involving customers and controllers and proposes a QoS-aware service delivery model for various types of communication with an adaptive buffer size according to the priority of the incoming service requests. For this purpose, this paper evaluates various QoS delivery models devised for service delivery in real time over IP networks. Multiple vulnerabilities are outlined that weaken QoS delivery in different models. Performance optimization is needed to ensure QoS delivery in next-generation WSN networks. This paper addresses the shortcomings of the existing service delivery models for real-time communication. An efficient queuing mechanism is adopted that assigns priorities based on input data type and queue length. This queuing mechanism ensures QoS efficiency in limited bandwidth networks and real-time traffic. The model reduces the over-provisioning of resources, delay, and packet loss ratio. The paper contributes a symmetrically-designed traffic engineering model for QoS-ensured service delivery for next-generation WSNs. A dynamic queuing mechanism that assigns priorities based on input data type and queue length is proposed to ensure QoS for wireless next-generation networks. The proposed queuing mechanism discusses topological symmetry to ensure QoS efficiency in limited bandwidth networks with real-time communication. The experimental results describe that the proposed model reduces the over-provisioning of resources, delay, and packet loss ratio.

This study aims to analyze the influencing factors and mechanisms of investment and financing risks in transportation projects so that regions do not restrict the transportation investment and financing risk models in all areas to achieve intelligent transportation financial risk assessment. Firstly, the investment and financing modes are studied and analyzed. According to the analysis of intellectual investment and the financing report of traffic engineering infrastructure, a traffic engineering investment and a financing model based on intelligent computing is established, which is based on artificial intelligence (AI) big data analysis technology. Secondly, the investment and the financing risk model of traffic engineering is established based on multimodal learning. Finally, the urban traffic engineering of Xi'an is taken as the research object. Based on its investment and financing data in the construction of urban roads, the risk assessment is carried out. Combined with risk influencing factors, the accuracy of the intelligent calculation in the risk assessment model is calculated. Different grades of urban transportation projects have different risks in the investment and financing of transportation projects. The results show that different levels of urban transport projects have different risks in the investment and financing (IAF) performance of transport projects. Among them, the risk index of the first-class project is the highest, reaching 0.55. The risk index of the second-class project is 0.49. The results before and after using the flow engineering IAF risk model are compared. In the test results of traffic engineering risk, all target risks did not increase after the AI-based traffic engineering IAF is tested. The model test results for credit risk and financial risk are the highest at 70 and 60, respectively. Combined with the actual urban development situation, this study can provide investment and financing risk models for urban transportation projects in different regions and provide a reference for the resource control of transportation projects. This study uses AI to learn and analyze traffic engineering investment and financing data and more accurately provide data references for traffic engineering investment and financing risk models. [ABSTRACT FROM AUTHOR]

Lane changing behavior, a pivotal aspect of vehicular dynamics, significantly influences traffic flow, safety, and overall road network efficiency. This review article synthesizes the current body of knowledge surrounding lane changing behavior, encompassing both mandatory and discretionary lane changes. The intricate interplay between various factors, including speed differentials, driver characteristics, road geometry, and external stimuli, shape the decision-making processes underlying lane changes. The article explores the underlying psychological, cognitive, and socio-demographic influences on lane changing behavior, highlighting the role of emerging technologies such as advanced driver assistance systems (ADAS) and vehicle-to-vehicle (V2V) communication. Furthermore, the review delves into existing models and methodologies employed to analyze lane changing behavior, addressing their limitations and potential for advancement. Realworld applications, such as work zones and incident management, are examined to underline the practical implications of understanding and predicting lane changing behavior. By presenting a comprehensive overview of the subject, this review aims to deepen the understanding of the complexities inherent in lane changing behavior, facilitating informed decision-making for traffic management, safety enhancement, and future research endeavors. [ABSTRACT FROM AUTHOR]

Novel architectures incorporating transport networks and artificial intelligence (AI) are currently being developed for beyond 5G and 6G technologies. Given that the interfacing mobile and transport network nodes deliver high transactional packet volume in downlink and uplink streams, 6G networks envision adopting diverse transport networks, including non-terrestrial types of transport networks such as the satellite network, High-Altitude Platform Systems (HAPS), and DOCSIS cable TV. Hence, there is a need to match the traffic to the transport network. This paper focuses on such a matching problem and defines a method that leverages machine learning and mixed-integer linear programming. Consequently, the proposed scheme in this paper is to develop a traffic steering capability based on types of transport networks, namely, optical, satellite, and DOCSIS cable. Novel findings demonstrate a more than 90% accuracy of steered traffic to respective types of transport networks for dedicated transport network resources.

Software-Defined Networking is a networking paradigm that enables new approach for network applications and engineering. Its fundamental concept is based on the separation of a centralized control plane and distributed forwarding plane. It provides a global view of entire network topology, combined with the capability of programming network elements in a logically centralized manner. One of the SDN benefits is the ability to optimize network traffic more efficiently than in classical IP networks. This paper proposes a heuristic traffic engineering approach in SDN network based on multipath forwarding and switching of flows between paths. The presented approach relies on a dynamic selection of the best path in terms of network load. The path selection is made in two phases. First, an initial lowest cost choice of a path out of available k-paths is determined, then a heuristic reselection of flows according to paths load and flow properties is triggered. The developed concept has been verified using the OpenDaylight controller enhanced by the Traffic Engineering module, the Mininet network emulator, and realistic network topologies. Simulations have shown that the proposed approach provides better results over plain multi-path traffic engineering approach.

To empower advanced traffic engineering (TE) mechanism, while considering the infeasibility of one-step migration to software-defined networking (SDN), SDN nodes are incrementally deployed into legacy network, which gives rise to hybrid SDN. In hybrid SDN, redirecting flow of every source-destination pair through at least one SDN node, can enhance TE performance and obtain flow manageability, while on the other hand leading to increasing demands of TCAM resources in SDN nodes. In this paper, we make minimization of maximum link utilization as the TE objective, and comply with SDN waypoint enforcement and TCAM resource limitation. We first formulate the TE problem as an integer linear programming (ILP) model and solve it in a centralized manner, where SDN waypoint selection and splitting fractions for each flow are jointly determined. Then, based on a fact that the logically centralized control plane in hybrid SDN is composed of multiple physically decentralized controllers, each of which manages part of SDN nodes, as well as considering a real situation that a centralized solution is infeasible or too fragile for large-scale network, we develop a distributed algorithm deriving from Lagrangian decomposition theory to effectively solve the TE problem. The simulation results indicate that, when 30% of the SDN nodes are deployed, the proposed traffic engineering-aware distributed routing (TEDR) algorithm obtains maximum link utilization comparable to that of full SDN, and has a limited influence on the routing efficiency.

Urbanization causes many problems to human mobility, since people living in the cities tend to increase the vehicular traffic flow. City road infrastructure does not increase faster than the number of vehicles, thus causing traffic congestion in dense urban centers. Besides travel delay, vehicular traffic congestion results in serious problems to human beings (e.g., health problems due to stress), to the planet (e.g., increase in pollution) and to the economy (e.g., waste of a large amount of money due to time spent in traffic jams). In order to improve vehicular traffic flow in dense urban areas, this paper presents a new Vehicular Traffic Management Service based on Traffic Engineering theory, called Re-RouTE. The Re-RouTE service relies on the density of vehicles in roads and applies the flow-density macroscopic traffic engineering model to identify congested routes. Roads are represented by a weighted graph, which is then used to discover routes without traffic jams and with a small increase in the travel distance. The main goal of Re-RouTE is to reduce traffic jams while increasing the global vehicular traffic flow of the road network. Moreover, the service was designed to reduce traffic jams instead of moving them to a different road/area. Simulation results show the ability of Re-RouTE to improve travel time, travel distance, speed and the number of messages transmitted when compared to a literature solution.