Scalable high-capacity high-fan-out optical networks for constrained environments

The investigations carried out as part of the dissertation address the architecture and application of optical access networks pertaining to high-capacity and high fan-out applications such as in-flight entertainment (IFE) and video-gaming environment. High-capacity and high-fan-out optical networks have a multitude of applications such as expo-centers, train area networks (TAN), video gaming competitions and other applications that require large number of connected users. For the purpose of keeping the scope of the dissertation within limit however, we have concentrated this work on IFE systems. IFE systems present unique challenges at physical and application layers alike. In-flight entertainment (IFE) systems have been a part of passengers' experience for a while now. Currently available systems can be considered a bare-bone at best due to lack of adequate performance and support infrastructure. According to electronic arts (EA), one of the largest developers of video games in the world, an increase in demand for electronically distributed video games will exceed boxed games in just a matter of few years. This also shows a shifting trend towards the electronic distribution of video game content as opposed to physical distribution. Against the same backdrop, the dissertation project involved defining a novel system architecture and capacity based on the requirements for development of novel physical layer architecture utilizing optical networks for high-speed and high-fan-out distribution of content. At the physical layer of the stacked communication model a novel high-fan-out optical network was proposed and simulated for high data-rates. Having defined the physical layer, protocol stack was identified through rigorous observations and data traffic analysis from a large set of traffic traces obtained from various sources in order to understand the distribution and behavior of video game related traffic compared with regular internet traffic. Data requirements were laid down based on analysis keeping in mind that bandwidth requirements are increasing at a tremendous pace and that the network should be able to support future high-definition and 3D gaming as well. Based on the data analysis, analytical models and latency analysis models were also developed for bandwidth allocation in the high-fan-out network architectures. Analytical modeling gives an insight into the performance of the technique as a function of incoming traffic whereas latency analysis exposes the delay factors involved in running the technique over time. "State-full bandwidth allocation" (SBA) was proposed as part of the network layer design for upstream transmission. The novel technique involves keeping state information from previous states for future allocation. The results show that the proposed high-fan-out high-capacity physical layer architecture can be used to distribute video-gaming related content. Also, latency analysis and design and development of a novel SBA algorithm were carried out. Results were quiet promising, in that; a large number of users can be supported on the same single channel network. SBA criteria can be applied to multi-channel networks such as the physical architecture proposed / simulated and investigated in this project. In summary, the project involved design of a novel physical layer; network layer and protocol stack of the communication model and verification by simulations and mathematical modeling while adhering to application layer requirements

Modeling the relationship between network operators and venue owners in public Wi-Fi deployment using non-cooperative game theory

Wireless data demands keep rising at a fast rate. In 2016, Cisco measured a global mobile data traffic volume of 7.2 Exabytes per month and projected a growth to 49 Exabytes per month in 2021. Wi-Fi plays an important role in this as well. Up to 60% of the total mobile traffic was off-loaded via Wi-Fi (and femtocells) in 2016. This is further expected to increase to 63% in 2021. In this publication, we look into the roll-out of public Wi-Fi networks, public meaning in a public or semi-public place (pubs, restaurants, sport stadiums, etc.). More concretely we look into the collaboration between two parties, a technical party and a venue owner, for the roll-out of a new Wi-Fi network. The technical party is interested in reducing load on its mobile network and generating additional direct revenues, while the venue owner wants to improve the attractiveness of the venue and consequentially generate additional indirect revenues. Three Wi-Fi pricing models are considered: entirely free, slow access with ads or fast access via paid access (freemium), and paid access only (premium). The technical party prefers a premium model with high direct revenues, the venue owner a free/freemium model which is attractive to its customers, meaning both parties have conflicting interests. This conflict has been modeled using non-cooperative game theory incorporating detailed cost and revenue models for all three Wi-Fi pricing models. The initial outcome of the game is a premium Wi-Fi network, which is not the optimal solution from an outsider's perspective as a freemium network yields highest total payoffs. By introducing an additional compensation scheme which corresponds with negotiation in real life, the outcome of the game is steered toward a freemium solution

DxNAT - Deep Neural Networks for Explaining Non-Recurring Traffic Congestion

Non-recurring traffic congestion is caused by temporary disruptions, such as accidents, sports games, adverse weather, etc. We use data related to real-time traffic speed, jam factors (a traffic congestion indicator), and events collected over a year from Nashville, TN to train a multi-layered deep neural network. The traffic dataset contains over 900 million data records. The network is thereafter used to classify the real-time data and identify anomalous operations. Compared with traditional approaches of using statistical or machine learning techniques, our model reaches an accuracy of 98.73 percent when identifying traffic congestion caused by football games. Our approach first encodes the traffic across a region as a scaled image. After that the image data from different timestamps is fused with event- and time-related data. Then a crossover operator is used as a data augmentation method to generate training datasets with more balanced classes. Finally, we use the receiver operating characteristic (ROC) analysis to tune the sensitivity of the classifier. We present the analysis of the training time and the inference time separately

A Study of Truck Platooning Incentives Using a Congestion Game

We introduce an atomic congestion game with two types of agents, cars and trucks, to model the traffic flow on a road over various time intervals of the day. Cars maximize their utility by finding a trade-off between the time they choose to use the road, the average velocity of the flow at that time, and the dynamic congestion tax that they pay for using the road. In addition to these terms, the trucks have an incentive for using the road at the same time as their peers because they have platooning capabilities, which allow them to save fuel. The dynamics and equilibria of this game-theoretic model for the interaction between car traffic and truck platooning incentives are investigated. We use traffic data from Stockholm to validate parts of the modeling assumptions and extract reasonable parameters for the simulations. We use joint strategy fictitious play and average strategy fictitious play to learn a pure strategy Nash equilibrium of this game. We perform a comprehensive simulation study to understand the influence of various factors, such as the drivers' value of time and the percentage of the trucks that are equipped with platooning devices, on the properties of the Nash equilibrium.Comment: Updated Introduction; Improved Literature Revie

From Social Simulation to Integrative System Design

As the recent financial crisis showed, today there is a strong need to gain "ecological perspective" of all relevant interactions in socio-economic-techno-environmental systems. For this, we suggested to set-up a network of Centers for integrative systems design, which shall be able to run all potentially relevant scenarios, identify causality chains, explore feedback and cascading effects for a number of model variants, and determine the reliability of their implications (given the validity of the underlying models). They will be able to detect possible negative side effect of policy decisions, before they occur. The Centers belonging to this network of Integrative Systems Design Centers would be focused on a particular field, but they would be part of an attempt to eventually cover all relevant areas of society and economy and integrate them within a "Living Earth Simulator". The results of all research activities of such Centers would be turned into informative input for political Decision Arenas. For example, Crisis Observatories (for financial instabilities, shortages of resources, environmental change, conflict, spreading of diseases, etc.) would be connected with such Decision Arenas for the purpose of visualization, in order to make complex interdependencies understandable to scientists, decision-makers, and the general public.Comment: 34 pages, Visioneer White Paper, see http://www.visioneer.ethz.c