Decentralised robust flow controller design for networks with multiple bottlenecks

Cataloged from PDF version of article.Decentralised rate-based flow controller design in multi-bottleneck data-communication networks is considered. An H problem is formulated to find decentralised controllers which can be implemented locally at the bottleneck nodes. A suboptimal solution to this problem is found and the implementation of the decentralised controllers is presented. The controllers are robust to time-varying uncertain multiple time-delays in different channels. They also satisfy tracking and weighted fairness requirements. Lower bounds on the actual stability margins are derived and their relation to the design parameters is analysed. A number of simulations are also included to illustrate the time-domain performance of the proposed controllers

Decentralised robust flow controller design for networks with multiple bottlenecks

Decentralised rate-based flow controller design in multi-bottleneck data-communication networks is considered. An H∞ problem is formulated to find decentralised controllers which can be implemented locally at the bottleneck nodes. A suboptimal solution to this problem is found and the implementation of the decentralised controllers is presented. The controllers are robust to time-varying uncertain multiple time-delays in different channels. They also satisfy tracking and weighted fairness requirements. Lower bounds on the actual stability margins are derived and their relation to the design parameters is analysed. A number of simulations are also included to illustrate the time-domain performance of the proposed controllers

A Review of Traffic Signal Control.

The aim of this paper is to provide a starting point for the future research within the SERC sponsored project "Gating and Traffic Control: The Application of State Space Control Theory". It will provide an introduction to State Space Control Theory, State Space applications in transportation in general, an in-depth review of congestion control (specifically traffic signal control in congested situations), a review of theoretical works, a review of existing systems and will conclude with recommendations for the research to be undertaken within this project

The State-of-the-art of Coordinated Ramp Control with Mixed Traffic Conditions

Ramp metering, a traditional traffic control strategy for conventional vehicles, has been widely deployed around the world since the 1960s. On the other hand, the last decade has witnessed significant advances in connected and automated vehicle (CAV) technology and its great potential for improving safety, mobility and environmental sustainability. Therefore, a large amount of research has been conducted on cooperative ramp merging for CAVs only. However, it is expected that the phase of mixed traffic, namely the coexistence of both human-driven vehicles and CAVs, would last for a long time. Since there is little research on the system-wide ramp control with mixed traffic conditions, the paper aims to close this gap by proposing an innovative system architecture and reviewing the state-of-the-art studies on the key components of the proposed system. These components include traffic state estimation, ramp metering, driving behavior modeling, and coordination of CAVs. All reviewed literature plot an extensive landscape for the proposed system-wide coordinated ramp control with mixed traffic conditions.Comment: 8 pages, 1 figure, IEEE INTELLIGENT TRANSPORTATION SYSTEMS CONFERENCE - ITSC 201

Vision-Based Lane-Changing Behavior Detection Using Deep Residual Neural Network

Accurate lane localization and lane change detection are crucial in advanced driver assistance systems and autonomous driving systems for safer and more efficient trajectory planning. Conventional localization devices such as Global Positioning System only provide road-level resolution for car navigation, which is incompetent to assist in lane-level decision making. The state of art technique for lane localization is to use Light Detection and Ranging sensors to correct the global localization error and achieve centimeter-level accuracy, but the real-time implementation and popularization for LiDAR is still limited by its computational burden and current cost. As a cost-effective alternative, vision-based lane change detection has been highly regarded for affordable autonomous vehicles to support lane-level localization. A deep learning-based computer vision system is developed to detect the lane change behavior using the images captured by a front-view camera mounted on the vehicle and data from the inertial measurement unit for highway driving. Testing results on real-world driving data have shown that the proposed method is robust with real-time working ability and could achieve around 87% lane change detection accuracy. Compared to the average human reaction to visual stimuli, the proposed computer vision system works 9 times faster, which makes it capable of helping make life-saving decisions in time

Accurate and Resource-Efficient Monitoring for Future Networks

Monitoring functionality is a key component of any network management system. It is essential for profiling network resource usage, detecting attacks, and capturing the performance of a multitude of services using the network. Traditional monitoring solutions operate on long timescales producing periodic reports, which are mostly used for manual and infrequent network management tasks. However, these practices have been recently questioned by the advent of Software Defined Networking (SDN). By empowering management applications with the right tools to perform automatic, frequent, and fine-grained network reconfigurations, SDN has made these applications more dependent than before on the accuracy and timeliness of monitoring reports. As a result, monitoring systems are required to collect considerable amounts of heterogeneous measurement data, process them in real-time, and expose the resulting knowledge in short timescales to network decision-making processes. Satisfying these requirements is extremely challenging given today’s larger network scales, massive and dynamic traffic volumes, and the stringent constraints on time availability and hardware resources. This PhD thesis tackles this important challenge by investigating how an accurate and resource-efficient monitoring function can be realised in the context of future, software-defined networks. Novel monitoring methodologies, designs, and frameworks are provided in this thesis, which scale with increasing network sizes and automatically adjust to changes in the operating conditions. These achieve the goal of efficient measurement collection and reporting, lightweight measurement- data processing, and timely monitoring knowledge delivery

New information model that allows logical distribution of the control plane for software-defined networking : the distributed active information model (DAIM) can enable an effective distributed control plane for SDN with OpenFlow as the standard protocol

University of Technology Sydney. Faculty of Engineering and Information Technology.In recent years, technological innovations in communication networks, computing applications and information modelling have been increasing significantly in complexity and functionality driven by the needs of the modern world. As large-scale networks are becoming more complex and difficult to manage, traditional network management paradigms struggle to cope with traffic bottlenecks of the traditional switch and routing based networking deployments. Recently, there has been a growing movement led by both industry and academia aiming to develop mechanisms to reach a management paradigm that separates the control plane from the data plane. A new emerging network management paradigm called Software-Defined Networking (SDN) is an attempt to overcome the bottlenecks of traditional data networks. SDN offers a great potential to ease network management, and the OpenFlow protocol in particularly often referred to a radical new idea in networking. SDN adopts the concept of programmable networks which separate the control decisions from forwarding hardware and thus enabling the creation of a standardised programming interface. Flow computation is managed by a centralised controller with the switches only performing simple forwarding functions. This allows researchers to implement their protocols and algorithms to control data packets without impacting on the production network. Therefore, the emerging OpenFlow technology provides more flexible control of networks infrastructure, are cost effective, open and programmable components of network architecture. SDN is very efficient at moving the computational load away from the forwarding plane and into a centralised controller, but a physically centralised controller can represent a single point of failure for the entire network. This centralisation approach brings optimality, however, it creates additional problems of its own including single-domain restriction, scalability, robustness and the ability for switches to adapt well to changes in local environments. This research aims at developing a new distributed active information model (DAIM) to allow programmability of network elements and local decision-making processes that will essentially contribute to complex distributed networks. DAIM offers adaptation algorithms embedded with intelligent information objects to be applied to such complex systems. By applying the DAIM model and these adaptation algorithms, managing complex systems in any distributed network environment can become scalable, adaptable and robust. The DAIM model is integrated into the SDN architecture at the level of switches to provide a logically distributed control plane that can manage the flow setups. The proposal moves the computational load to the switches, which allows them to adapt dynamically according to real-time demands and needs. The DAIM model can enhance information objects and network devices to make their local decisions through its active performance, and thus significantly reduce the workload of a centralised SDN/OpenFlow controller. In addition to the introduction (Chapter 1) and the comprehensive literature reviews (Chapter 2), the first part of this dissertation (Chapter 3) presents the theoretical foundation for the rest of the dissertation. This foundation is comprised of the logically distributed control plane for SDN networks, an efficient DAIM model framework inspired by the O:MIB and hybrid O:XML semantics, as well as the necessary architecture to aggregate the distribution of network information. The details of the DAIM model including design, structure and packet forwarding process are also described. The DAIM software specification and its implementation are demonstrated in the second part of the thesis (Chapter 4). The DAIM model is developed in the C++ programming language using free and open source NetBeans IDE. In more detail, the three core modules that construct the DAIM ecosystem are discussed with some sample code reviews and flowchart diagrams of the implemented algorithms. To show DAIM’s feasibility, a small-size OpenFlow lab based on Raspberry Pi’s has been set up physically to check the compliance of the system with its purpose and functions. Various tasks and scenarios are demonstrated to verify the functionalities of DAIM such as executing a ping command, streaming media and transferring files between hosts. These scenarios are created based on OpenVswitch in a virtualised network using Mininet. The third part (Chapter 5) presents the performance evaluation of the DAIM model, which is defined by four characteristics: round-trip-time, throughput, latency and bandwidth. The ping command is used to measure the mean RTT between two IP hosts. The flow setup throughput and latency of the DAIM controller are measured by using Cbench. Also, Iperf is the tool used to measure the available bandwidth of the network. The performance of the distributed DAIM model has been tested and good results are reported when compared with current OpenFlow controllers including NOX, POX and NOX-MT. The comparisons reveal that DAIM can outperform both NOX and POX controllers. The DAIM’s performance in a physical OpenFlow test lab and other parameters that can affect the performance evaluation are also discussed. Because decentralisation is an essential element of autonomic systems, building a distributed computing environment by DAIM can consequently enable the development of autonomic management strategies. The experiment results show the DAIM model can be one of the architectural approaches to creating the autonomic service management for SDN. The DAIM model can be utilised to investigate the functionalities required by the autonomic networking within the ACNs community. This efficient DAIM model can be further applied to enable adaptability and autonomy to other distributed networks such as WSNs, P2P and Ad-Hoc sensor networks

Development of Eco-Friendly Ramp Control for Connected and Automated Electric Vehicles

With on-board sensors such as camera, radar, and Lidar, connected and automated vehicles (CAVs) can sense the surrounding environment and be driven autonomously and safely by themselves without colliding into other objects on the road. CAVs are also able to communicate with each other and roadside infrastructure via vehicle-to-vehicle and vehicle-to-infrastructure communications, respectively, sharing information on the vehicles’ states, signal phase and timing (SPaT) information, enabling CAVs to make decisions in a collaborative manner. As a typical scenario, ramp control attracts wide attention due to the concerns of safety and mobility in the merging area. In particular, if the line-of-the-sight is blocked (because of grade separation), then neither mainline vehicles nor on-ramp vehicles may well adapt their own dynamics to perform smoothed merging maneuvers. This may lead to speed fluctuations or even shockwave propagating upstream traffic along the corridor, thus potentially increasing the traffic delays and excessive energy consumption. In this project, the research team proposed a hierarchical ramp merging system that not only allowed microscopic cooperative maneuvers for connected and automated electric vehicles on the ramp to merge into mainline traffic flow, but also had controllability of ramp inflow rate, which enabled macroscopic traffic flow control. A centralized optimal control-based approach was proposed to both smooth the merging flow and improve the system-wide mobility of the network. Linear quadratic trackers in both finite horizon and receding horizon forms were developed to solve the optimization problem in terms of path planning and sequence determination, and a microscopic electric vehicle (EV) energy consumption model was applied to estimate the energy consumption. The simulation results confirmed that under the regulated inflow rate, the proposed system was able to avoid potential traffic congestion and improve the mobility (in terms of average speed) as much as 115%, compared to the conventional ramp metering and the ramp without any control approach. Interestingly, for EVs (connected and automated EVs in this study), the improved mobility may not necessarily result in the reduction of energy consumption. The “sweet spot” of average speed ranges from 27–34 mph for the EV models in this study.View the NCST Project Webpag