Heterogeneous Transportation Networks Research Articles

Intelligent Traffic Engineering for 6G Heterogeneous Transport Networks

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.

Priority RPL for IOT Based Smart Manufacturing Industries

A routing protocol used in heterogeneous transport networks for low-power, lossy networks. This is a routing protocol for wireless networks. This protocol follows the same specifications as Zigbee, 6 lopan is IEEE 802.15. 4 Enables both many-to-one and one-to-one communication. To address the need for enhancing in this study proposes a novel methodology called RPL-PG (Routing Protocol for Low-Power and Lossy Networks Priority Generation). Initially sensors like Temperature, Humidity, Vibration, Proximity, Gas and Current Monitoring Sensors are used for smart manufacturing. Consequently, Destination Oriented Directed Acyclic Graph (DODAG) is used for RPL configuration. Based on selected RPL configuration the priority is generated using assign priority count and priority-based queuing. Finally, Fuzzy rules are used to select the RPL path and then update the DODAG finally reached the destination. The study involves setting up a simulated environment using appropriate tools, such as MATLAB. Experimental findings evaluate and compares performance measures, such as Energy Consumption, Network Life Time, Packet Loss Ratio, Packet Delivery Ratio (PDR), E2E Delay, and Network Throughput. The Energy Consumption of the proposed RPL-PG method achieves 43.6 % lower than 38 % and 35.8 % in terms of OMC-RPL and RMA-RP respectively.

Trustworthy Edge Storage Orchestration in Intelligent Transportation Systems Using Reinforcement Learning

A large scale fast-growing data generated in intelligent transportation systems (ITS) has become a ponderous burden on the coordination of heterogeneous transportation networks, which makes the traditional cloud-centric storage architecture no longer satisfy new data analytics requirements. Meanwhile, the lack of storage trust between ITS devices and edge servers could lead to security risks in the data storage process. However, a unified data distributed storage architecture for ITS with intelligent management and trustworthiness is absent in the previous works. To address these challenges, this paper proposes a distributed trustworthy storage architecture with reinforcement learning in ITS, which also promotes edge services. We adopt an intelligent storage scheme to store data dynamically with reinforcement learning based on trustworthiness and popularity, which improves resource scheduling and storage space allocation. Besides, trapdoor hashing based identity authentication protocol is proposed to secure transportation network access. Due to the interaction between cooperative devices, our proposed trust evaluation mechanism is provided with extensibility in the various ITS. Simulation results demonstrate that our proposed distributed trustworthy storage architecture outperforms the compared ones in terms of trustworthiness and efficiency.

Enhancing model-based feedback perimeter control with data-driven online adaptive optimization

Most feedback perimeter control approaches that are based on the Macroscopic Fundamental Diagram (MFD) and are tested in detailed network structures restrict inflow from the external boundary of the network. Although such a measure is beneficial for the network performance, it creates virtual queues that do not interact with the rest of the traffic and assumes small unrestricted flow (i.e. almost zero disturbance). In reality, these queues can have a negative impact to traffic conditions upstream of the protected network that is not modelled. In this work an adaptive optimization scheme for perimeter control of heterogeneous transportation networks is developed and the aforementioned boundary control limitation is dropped. A nonlinear model is introduced that describes the evolution of the multi-region system over time, assuming the existence of well-defined MFDs. Multiple linear approximations of the model (for different set-points) are used for designing optimal multivariable integral feedback regulators. Since the resulting regulators are derived from approximations of the nonlinear dynamics, they are further enhanced in real-time with online learning/adaptive optimization, according to performance measurements. An iterative data-driven technique is integrated with the model-based design and its objective is to optimize the gain matrices and set-points of the multivariable perimeter controller based on real-time observations. The efficiency of the derived multi-boundary control scheme is tested in microsimulation for a large urban network with more than 1500 roads that is partitioned in multiple regions. The proposed control scheme is demonstrated to achieve a better distribution of congestion (by creating “artificial” inter-regional queues), thus preventing the network degradation and improving total delay and outflow.

Strategic Concept for the Protection of Regionally Interconnected Surface Transportation Networks

Surface transportation systems crucially define the daily functionality and operations of modern societies: they are utilised by millions of commuters worldwide, on a daily basis. As such, they are open and freely accessible by design and in the past have been exploited for malicious purposes and have also been severely disrupted by natural phenomena. Like many modern critical infrastructures, multimodal and heterogeneous transportation networks are interconnected as integral part of larger synergistic systems forming a “network of networks”. The present manuscript introduces a holistic concept for the protection of heterogeneous transportation networks that is applicable on a strategic level. The basis of the proposed model is the concept that security incidents may be propagated between assets of interconnected networks. The proposed methodology emphasizes the strategic level protection both from the perspective of the network operator and the emergency responder, linking all phases of the disaster cycle into a unique concept of operations.

Nowcasting the spread of chikungunya virus in the Americas.

BackgroundIn December 2013, the first locally-acquired chikungunya virus (CHIKV) infections in the Americas were reported in the Caribbean. As of May 16, 55,992 cases had been reported and the outbreak was still spreading. Identification of newly affected locations is paramount to intervention activities, but challenging due to limitations of current data on the outbreak and on CHIKV transmission. We developed models to make probabilistic predictions of spread based on current data considering these limitations.Methods and FindingsBranching process models capturing travel patterns, local infection prevalence, climate dependent transmission factors, and associated uncertainty estimates were developed to predict probable locations for the arrival of CHIKV-infected travelers and for the initiation of local transmission. Many international cities and areas close to where transmission has already occurred were likely to have received infected travelers. Of the ten locations predicted to be the most likely locations for introduced CHIKV transmission in the first four months of the outbreak, eight had reported local cases by the end of April. Eight additional locations were likely to have had introduction leading to local transmission in April, but with substantial uncertainty.ConclusionsBranching process models can characterize the risk of CHIKV introduction and spread during the ongoing outbreak. Local transmission of CHIKV is currently likely in several Caribbean locations and possible, though uncertain, for other locations in the continental United States, Central America, and South America. This modeling framework may also be useful for other outbreaks where the risk of pathogen spread over heterogeneous transportation networks must be rapidly assessed on the basis of limited information.

STAR-TRANS Modeling Language

Transportation networks are open and accessible, by design, and thus vulnerable to malicious attacks. Transportation networks are integral parts of larger systems, where individual transportation networks form a network-of-networks within a defined geographical region. A security incident on an asset can propagate to new security incidents in interconnected assets of the same or different networks, resulting in cascading failures in the overall network-of-networks. The present work introduces the STAR-TRANS Modeling Language (STML) and provides a reference implementation case. STML is a feature-rich, domain specific, high-level modeling language, capable of expressing the concepts and processes of the Strategic Risk Assessment and Contingency Planning in Interconnected Transportation Networks (STAR- TRANS) framework. STAR-TRANS is a comprehensive transportation security risk assessment framework for assessing related risks that provides cohered contingency management procedures for interconnected, interdependent and heterogeneous transport networks. STML has been used to produce the STAR-TRANS Impact Assessment Tool.

A Risk Assessment Framework For Interconnected And Interdependent Surface Transport Networks

The functioning and viability of modern societies is heavily depended upon the continuous and uninterruptible operation of critical infrastructures. Surface transportation systems are in the heart of the daily lives of millions of citizens globally. As such, they are open and freely accessible by design and in the past have been exploited for terrorism attacks. Like many critical infrastructures, different multimodal and heterogeneous transportation networks are interconnected as integral part of larger synergistic systems forming a “network of networks”. These underlying and often concealed interconnections between network assets enable adverse effects to manifest at assets that are initially unaffected by a security incident. The present paper introduces a holistic Risk Assessment Framework for heterogeneous, transportation networks that is applicable at a strategic level, where risk is propagated between interconnected networks through an “Incident Propagation Matrix” taking into account the nature of the interconnection and the type of threat. The proposed methodology views and models the risk analysis process from the perspective of the network operator and emergency responders and emphasizes the reduction of the impacts on business continuity. DOI 10.5592/otmcj.2013.2.6 Research paper

Cost driven traffic assignment in transportation networks

A cost driven traffic assignment model is proposed to maximise the global benefit of transportation network users. A decentralised primal-dual time synchronised approach is used to solve for the optimal traffic rates subjecting the objective function to the bounds of the network tolerable costs. For a successful operation of the transportation network, the affordable upper bounds on the costs have to be set carefully. A breakeven analysis was presented to estimate the network charges. It came across that the longer the route, the higher its charge. The breakeven surcharges represent the minimum quantities a network may charge from its commuters for a profitable operation. The presented scheme demonstrates a great control on the queue sizes and motivates resource allocation in heterogeneous transportation networks. The experimental results reveal an attractive convergence, performance and sensible resource assignment between the network users while achieving comprehensive optimality in a distributed approach.

Optimization Framework for Supporting 40 Gb/s and 100 Gb/s Services over Heterogeneous Optical Transport Networks

Continuous traffic growth and crunched profit margins are leading network operators to deploying high-capacity backbone infrastructures with minimal capital investment. However, the cost-effective design of optical transport networks (OTNs) remains a complex challenge subjected to multiple constraints, e.g., maximum bit rate per channel, electrical multiplexing capabilities, wavelength count per fiber, optical interface cost, maximum transparent reach, etc. To efficiently solve this multi-constrained dimensioning problem in presence of heterogeneous client demands and optical channel rates of 40 Gb/s and 100 Gb/s, this paper presents a novel hybrid optimization framework. The proposed approach is based on an iterative combination of linear programming and rounding algorithms for the demand routing, with graph coloring heuristics for the wavelength assignment. The performance of this framework is assessed and compared with a similar approach that resorts to an integer linear programming (ILP) model to route the demands. The results obtained show that our proposal is able to reach the same low network expenditures as the ILP while requesting less computation time. We also confirm that the most cost-effective network solutions are attained when optical line rate heterogeneity is jointly applied with diverse multiplexing capabilities at the OTN electrical layer, such as grooming and inverse-multiplexing.

Dynamic resource control mechanism for multimedia overlay transport in NGN

This paper investigates a dynamic resource control mechanism to efficiently deliver NGN multimedia services while supporting quality of service (QoS) requirements and bandwidth flexibility in next generation networks (NGN). The proposed resource control aims to realize the dynamic and automatic setup and release of multimedia resource sessions across heterogeneous transport networks including IP, SONET/SDH, and WDM technologies. It can also provide not only low cost operations but also flexibility for multimedia resource control.

The development of a strategic risk analysis framework for interconnected surface transportation systems

Transportation networks are centre to the daily lives of millions of citizens around the world. By design, they are open and freely accessible systems that may be exploited for malicious purposes. Transportation networks, especially in dense urban environments, are integral part of larger synergistic systems forming a network of networks. The present paper introduces a strategic risk assessment framework for assessing risks stemming from security incidents in interconnected, interdependent and heterogeneous transportation networks. The developed framework attempts to build upon existing operational risk analysis frameworks of transportation operators and the organisation of major events. The derived risk assessment approach is designed to estimate risk in interconnected transportation networks and ultimately estimate a holistic risk in the network of networks.

Advance reservations for service-aware GMPLS-based optical networks

In service-oriented networks, cooperation between users, applications and network tends to come closer. With this objective, new architectures arise to cope with the upcoming demands. Of great importance is the definition of the proper interfaces and network services. One of these new services is known as the Advance Reservation Service. This service allows prior reservation of network resources in order to provide guaranteed client connections. The introduction of advance reservations in heterogeneous transport networks imposes the need to find new mechanisms that consider temporal characteristics of scheduled connections and their impact on immediate connections. In this paper, we investigate these issues, propose novel algorithms to improve overall network performance, and describe the implementation carried out over the ASON/GMPLS CARISMA testbed.

Systematic and efficient GMPLS-based recovery mechanism with soft-protection using enhanced LMP

generalized multi-protocol label switching (GMPLS-based) protection and restoration technologies will play significant role in next-generation broadband IP network to ensure the survivability. In this article, a systematic GMPLS-based recovery mechanism is proposed using enhanced link management protocol (LMP), which can support bidirectional fault notification relay more effectively. The major contributions and propositions in this article involve: 1) Soft-protection configuration for higher resource utilization with optimized routing; 2) Intelligent Shift from protection manner to restoration manner for the reduction of recovery latency; and 3) Toggleable revertive mode to increase restoration flexibility and to reduce loss of stability. Additionally, by leveraging valuable legacy including shared risk link group (SRLG) processing and fast reroute technique, the proposed recovery mechanism achieves higher resource utilization, fast and robust operation, satisfactory stability and flexibility, and is convenient in the application over heterogeneous transport networks.