In this thesis, we investigate the impact of channels that have a bursty nature in a cognitive radio network scenario. Our goal is to design a general channel usage model that can handle bursty primary user channel usage. The proposed model describes idle periods with a discrete platoon arrival process and describes busy periods with a discrete phase type distribution. The performance of the proposed model is compared with two more traditionally encountered channel usage models in three different secondary user access schemes. First, we design a reactive access scheme to show the poor performance results an in- vestigator can potentially obtain when ignoring bursty data traffic. We have also analyzed two proactive secondary network access schemes. Numerical results show that the achiev- able utilization and interference probability of the network are affected when traditional channel models are used in a bursty PU channel.

In this thesis, we investigate the impact of channels that have a bursty nature in a cognitive radio network scenario. Our goal is to design a general channel usage model that can handle bursty primary user channel usage. The proposed model describes idle periods with a discrete platoon arrival process and describes busy periods with a discrete phase type distribution. The performance of the proposed model is compared with two more traditionally encountered channel usage models in three different secondary user access schemes. First, we design a reactive access scheme to show the poor performance results an in- vestigator can potentially obtain when ignoring bursty data traffic. We have also analyzed two proactive secondary network access schemes. Numerical results show that the achiev- able utilization and interference probability of the network are affected when traditional channel models are used in a bursty PU channel.

The need of a working infrastructure in a city also requires an understanding of how the traffic flows. It is known that increasing number of drivers prolong the travel time and has an environmental effect in larger cities. It also makes it more difficult for commuters and delivery firms to estimate their travel time. To estimate the traffic flow the traffic department can arrange cameras along popular roads and redirect the traffic, but this is a costly method and difficult to implement. Another approach is to apply theories from physics wave theory and mathematics to model the traffic flow; in this way it is less costly and possible to predict the traffic flow as well. This report studies the application of wave theory and expresses the traffic flow as a modified linear differential equation. First is an analytical solution derived to find a feasible solution. Then a numerical approach is done with Taylor expansions and Crank-Nicolson’s method. All is performed in Matlab and compared against measured values of speed and flow retrieved from Swedish traffic department over a 24 hours traffic day. The analysis is performed on a highway stretch outside Stockholm with no entries, exits or curves. By dividing the interval of the highway into shorter equal distances the modified linear traffic model is expressed in a system of equations. The comparison between actual values and calculated values of the traffic density is done with a nominal average difference. The results reveal that the numbers of intervals don’t improve the average difference. As for the small constant that is applied to make the linear model stable is higher than initially considered.

Design and management decisions require an accurate prediction of the performance of the network. Network performance estimation techniques require accurate network traffic models. In this thesis we are concerned with the modelling of network traffic for the wireless mesh network (WMN) environment. Queueing theory has been used in the past to model the WMN environment and we found in this study that queueing theory was used in two main methods to model WMNs. The first method is to consider each node in the network in terms of the number of hops it is away from the gateway. Each node is then considered as a queueing station and the parameters for the station is derived from the number of hops each node is away from the gateway. These topologies can be very limiting in terms of the number of physical topologies they can model due to the fact that their parameters are only dependent on the number of hop-counts each node is away from the gateway. The second method is to consider a fixed topology with no gateways. This method simplifies analysis but once again is very limiting. In this dissertation we propose a queueing based network traffic model that uses a connection matrix to define the topology of the network. We then derive the parameters for our model from the connection matrix. The connection matrix allows us to model a wider variety of topologies without modifying our model. We verify our model by comparing results from our model to results from a discrete event simulator and we validate our model by comparing results from our model to results from models previously proposed by other authors. By comparing results from our model to results of other models we show that our model is indeed capable of modelling a wider variety of topologies. / Thesis (MIng (Computer and Electronic Engineering))--North-West University, Potchefstroom Campus, 2013.

Design and management decisions require an accurate prediction of the performance of the network. Network performance estimation techniques require accurate network traffic models. In this thesis we are concerned with the modelling of network traffic for the wireless mesh network (WMN) environment. Queueing theory has been used in the past to model the WMN environment and we found in this study that queueing theory was used in two main methods to model WMNs. The first method is to consider each node in the network in terms of the number of hops it is away from the gateway. Each node is then considered as a queueing station and the parameters for the station is derived from the number of hops each node is away from the gateway. These topologies can be very limiting in terms of the number of physical topologies they can model due to the fact that their parameters are only dependent on the number of hop-counts each node is away from the gateway. The second method is to consider a fixed topology with no gateways. This method simplifies analysis but once again is very limiting. In this dissertation we propose a queueing based network traffic model that uses a connection matrix to define the topology of the network. We then derive the parameters for our model from the connection matrix. The connection matrix allows us to model a wider variety of topologies without modifying our model. We verify our model by comparing results from our model to results from a discrete event simulator and we validate our model by comparing results from our model to results from models previously proposed by other authors. By comparing results from our model to results of other models we show that our model is indeed capable of modelling a wider variety of topologies. / Thesis (MIng (Computer and Electronic Engineering))--North-West University, Potchefstroom Campus, 2013.

We design an Ordinary Delay Differential Equation model for car to car interaction with switching between four distinct force terms including "free acceleration'', "follow acceleration'', "follow braking'', and aggressive driving''. We calibrate this model by recreating a real experiment on spontaneous formation of traffic jams. Once simulations of our model match those of the experiment we develop a model of both intersections using traffic lights, and intersections using roundabouts. Using our calibrated car interaction model we compare traffic light versus roundabout efficiencies in both flux and fuel consumption. We also use simulation results to extract information relevant to macroscopic traffic models. A relationship between flux and density known as The Fundamental Diagram is derived, and we discuss a technique for comparing microscopic to macroscopic models. / Graduate / 0405 / gmcgrego@uvic.ca

Ground transportation systems are essential for the mobility of people, goods and services. Thus, making sure these systems are resilient to the impact of natural and man-made disasters has become a top priority for engineers and policy makers. One of the major obstacles for increasing the resilience of ground transportation systems is the lack of a measuring framework. Such measuring framework is critical for identifying needs, monitoring changes, assessing improvements, and performing cost-benefit analysis. This research addresses this problem by developing a traffic-based framework for measuring the resilience of ground transportation systems under normal and extreme conditions. The research methodology consisted of: (1) creating a microscopic traffic model of the road under study, (2) simulating different intrusions and interventions, and (3) measuring the resilience of the system under the different scenarios using the framework developed. This research expanded the current definition of infrastructure resilience, which includes the assessment of system performance versus time, to add a third dimension of resilience for ground transportation system's applications, namely: location. This third dimension considers how the system changes along the different locations in the network, which reflects more accurately the continuous behavior of a ground transportation network. The framework was tested in a 24 km segment of Interstate 95 in Virginia, near Washington, D.C. Four hazard conditions were simulated: inadequate base capacity, traffic incidents, work zones, and weather events. Intervention strategies tested include ramp meters and the use of the shoulder lane during extreme events. Public policy was also considered as a powerful intervention strategy. The findings of this research shed light over the current and future resilience of ground transportation systems when subject to multiple hazards, and the effects of implementing potential interventions. / Ph. D.

Variable bit rate (VBR) compressed video traffic, such as live video news, is expected to account for a large portion of traffic in future integrated networks. This real-time video traffic has strict delay and loss requirements, and exhibits burstiness over multiple time scales, thus imposing a challenge on network resource allocation and management. The renegotiated VBR (R-VBR) scheme, dynamically allocating resources to capture the burstiness of VBR traffic, substantially increases network utilization while satisfying any desired quality of service (QoS) requirements. This thesis focuses on the performance evaluation of R-VBR in the context of different R-VBR approaches. The renegotiated deterministic VBR (RED-VBR) scheme, proposed by Dr. H. Zhang et al., is thoroughly investigated in this research using a variety of real-world videos, with both high quality and low quality. A new Virtual-Queue-Based RED-VBR is then developed to reduce the implementation complexity of RED-VBR. Simulation results show that this approach obtains a comparable network performance as RED-VBR: relatively high network utilization and a very low drop rate. A Prediction-Based R-VBR based on a multiresolution learning neural network traffic predictor, developed by Dr. Y. Liang, is studied and the use of binary exponential backoff (BEB) algorithm is introduced to efficiently decrease the renegotiation frequency. Compared with RED-VBR, Prediction-Based R-VBR obtains significantly improved network utilization at a little expense of the drop rate. This work provides evaluations of the advantages and disadvantages of several R-VBR approaches, and thus provides a clearer big picture on the performance of the studied R-VBR approaches, which can be used as the basis to choose an appropriate R-VBR scheme to optimize network utilization while enabling QoS for the application tasks. / Master of Science

In this extended abstract, we propose an Artificial Intelligence-based model dedicated to the representation of a multi-class traffic flow, i.e. a traffic flow in which different vehicle classes (at least cars and trucks) are explicitly represented, with the aim of using it for the development of freeway traffic control schemes based on ramp management. Specifically, the goal of this work is to develop a hybrid modelling technique in which a Machine Learning component and the multi-class version of METANET model are adopted to determine a better estimation and forecasting tool for freeway traffic. The resulting model is specifically devised in order to be included in a Model Predictive Control (MPC) scheme for the required traffic state prediction.

With the upcoming release of the fifth generation of cellular networks, 5G, it is expected to be a lot more connected devices. As a consequence, the importance of the capacity of the networks is increased, especially the radio access network (RAN), which will be changed a lot from previous generations. The best method to ensure a RAN has enough capacity for its targeted area is a simulation, and therefore, there is a demand for such a simulator. The development of a simulator for RAN started last year in another thesis with the possibility to model nodes, links, and set loads. In this thesis, the functionality is extended with the addition of mobility and user equipment (UE) to create load to more accurately simulate the network traffic. Roads are added as an entity to allow mobility, and cells are modeled to determine coverage. Most requested functionality was implemented, with the main difficulties being integrating the new code into the existing code base. The simulator was then evaluated, both the validity of the model and the performance. To improve the simulator for its purpose, even more, functionalities such as geography elements like buildings or mountains blocking the signal should be considered. Another improvement would be to make links and cells less reliable. The simulator is modeling the perfect day scenario at the moment which does not always reflect upon reality.