Automated generation of geometrically-precise and semantically-informed virtual geographic environnements populated with spatially-reasoning agents

La Géo-Simulation Multi-Agent (GSMA) est un paradigme de modélisation et de simulation de phénomènes dynamiques dans une variété de domaines d'applications tels que le domaine du transport, le domaine des télécommunications, le domaine environnemental, etc. La GSMA est utilisée pour étudier et analyser des phénomènes qui mettent en jeu un grand nombre d'acteurs simulés (implémentés par des agents) qui évoluent et interagissent avec une représentation explicite de l'espace qu'on appelle Environnement Géographique Virtuel (EGV). Afin de pouvoir interagir avec son environnement géographique qui peut être dynamique, complexe et étendu (à grande échelle), un agent doit d'abord disposer d'une représentation détaillée de ce dernier. Les EGV classiques se limitent généralement à une représentation géométrique du monde réel laissant de côté les informations topologiques et sémantiques qui le caractérisent. Ceci a pour conséquence d'une part de produire des simulations multi-agents non plausibles, et, d'autre part, de réduire les capacités de raisonnement spatial des agents situés. La planification de chemin est un exemple typique de raisonnement spatial dont un agent pourrait avoir besoin dans une GSMA. Les approches classiques de planification de chemin se limitent à calculer un chemin qui lie deux positions situées dans l'espace et qui soit sans obstacle. Ces approches ne prennent pas en compte les caractéristiques de l'environnement (topologiques et sémantiques), ni celles des agents (types et capacités). Les agents situés ne possèdent donc pas de moyens leur permettant d'acquérir les connaissances nécessaires sur l'environnement virtuel pour pouvoir prendre une décision spatiale informée. Pour répondre à ces limites, nous proposons une nouvelle approche pour générer automatiquement des Environnements Géographiques Virtuels Informés (EGVI) en utilisant les données fournies par les Systèmes d'Information Géographique (SIG) enrichies par des informations sémantiques pour produire des GSMA précises et plus réalistes. De plus, nous présentons un algorithme de planification hiérarchique de chemin qui tire avantage de la description enrichie et optimisée de l'EGVI pour fournir aux agents un chemin qui tient compte à la fois des caractéristiques de leur environnement virtuel et de leurs types et capacités. Finalement, nous proposons une approche pour la gestion des connaissances sur l'environnement virtuel qui vise à supporter la prise de décision informée et le raisonnement spatial des agents situés

Using Visual Analytics to Discover Bot Traffic

With the advance of technology, the Internet has become a medium tool used for many malicious activities. The presence of bot traffic has increased greatly that causes significant problems for businesses and organisations, such as spam bots, scraper bots, distributed denial of service bots and adaptive bots that aim to exploit the vulnerabilities of a website. Discriminating bot traffic against legitimate flash crowds remains an open challenge to date.In order to address the above issues and enhance security awareness, this thesis proposes an interactive visual analytics system for discovering bot traffic. The system provides an interactive visualisation, with details on demand capabilities, which enables knowledge discovery from very large datasets. It enables an analyst to understand comprehensive details without being constrained by large datasets. The system has a dashboard view to represent legitimate and bot traffic by adopting Quadtree data structure and Voronoi diagrams. The main contribution of this thesis is a novel visual analytics system that is capable of discovering bot traffic.This research conducted a literature review in order to gain systematic understanding of the research area. Furthermore, the research was conducted by utilising experiment and simulation approaches. The experiment was conducted by capturing website traffic, identifying browser fingerprints, simulating bot attacks and analysing mouse dynamics, such as movements and events, of participants. Data were captured as the participants performed a list of tasks, such as responding to the banner. The data collection is transparent to the participants and only requires JavaScript to be activated on the client side. This study involved 10 participants who are familiar with the Internet. To analyse the data, Weka 3.6.10 was used to perform classification based on a training dataset. The test dataset of all participants was evaluated using a built-in decision tree algorithm. The results of classifying the test dataset were promising, and the model was able to identify ten participants and six simulated bot attacks with an accuracy of 86.67%. Finally, the visual analytics design was formulated in order to assist an analyst to discover bot presence

Multi AGV Communication Failure Tolerant Industrial Supervisory System

Hoje em dia, em muitos ambientes industriais que utilizam vários robots, existe o problema de controlar o tráfego. Para se controlar o tráfego é preciso planear caminhos seguros, evitar os chamados deadlocks e estar imune a falhas de rede. O objetivo deste projeto consiste em implementar um sistema supervisor que controle esse tráfego, ou seja, seja capaz de detetar as falhas de rede, detetar desvios nas rotas dos robôs e replanear se necessário. O sistema de planeamento de trajetórias é o TEA*, um algoritmo A* mas que entra com a noção de tempo.The use of multi AGV implies an optimisation of traffic control. Several approaches focus on a trajectory planning method that guarantees an efficient and safe coordination of multi AGV. However, many fail to detect, treat and prevent the possible failure and delay in the communication between the AGV and the control platform. These faults can result in possible deadlock situations and collisions. In environments where communication faults are common, we might face a decrease of efficiency. Therefore, the aim of this project is to implement a supervisory system that controls the traffic of a fleet of AGV by being able to detect communication faults, delays in the communication, deviations in the routes of the robots and re-plan trajectories if necessary. For this purpose, the algorithm TEA*, an A* based algorithm (a graph search algorithm) with time notion, will be used to keep the efficiency and allow time optimisations

Developing an advanced collision risk model for autonomous vehicles

Aiming at improving road safety, car manufacturers and researchers are verging upon autonomous vehicles. In recent years, collision prediction methods of autonomous vehicles have begun incorporating contextual information such as information about the traffic environment and the relative motion of other traffic participants but still fail to anticipate traffic scenarios of high complexity. During the past two decades, the problem of real-time collision prediction has also been investigated by traffic engineers. In the traffic engineering approach, a collision occurrence can potentially be predicted in real-time based on available data on traffic dynamics such as the average speed and flow of vehicles on a road segment. This thesis attempts to integrate vehicle-level collision prediction approaches for autonomous vehicles with network-level collision prediction, as studied by traffic engineers. [Continues.

Pedestrian Mobility Mining with Movement Patterns

In street-based mobility mining, pedestrian volume estimation receives increasing attention, as it provides important applications such as billboard evaluation, attraction ranking and emergency support systems. In practice, empirical measurements are sparse due to budget limitations and constrained mounting options. Therefore, estimation of pedestrian quantity is required to perform pedestrian mobility analysis at unobserved locations. Accurate pedestrian mobility analysis is difficult to achieve due to the non-random path selection of individual pedestrians (resulting from motivated movement behaviour), causing the pedestrian volumes to distribute non-uniformly among the traffic network. Existing approaches (pedestrian simulations and data mining methods) are hard to adjust to sensor measurements or require more expensive input data (e.g. high fidelity floor plans or total number of pedestrians in the site) and are thus unfeasible. In order to achieve a mobility model that encodes pedestrian volumes accurately, we propose two methods under the regression framework which overcome the limitations of existing methods. Namely, these two methods incorporate not just topological information and episodic sensor readings, but also prior knowledge on movement preferences and movement patterns. The first one is based on Least Squares Regression (LSR). The advantage of this method is the easy inclusion of route choice heuristics and robustness towards contradicting measurements. The second method is Gaussian Process Regression (GPR). The advantages of this method are the possibilities to include expert knowledge on pedestrian movement and to estimate the uncertainty in predicting the unknown frequencies. Furthermore the kernel matrix of the pedestrian frequencies returned by the method supports sensor placement decisions. Major benefits of the regression approach are (1) seamless integration of expert data and (2) simple reproduction of sensor measurements. Further advantages are (3) invariance of the results against traffic network homeomorphism and (4) the computational complexity depends not on the number of modeled pedestrians but on the traffic network complexity. We compare our novel approaches to state-of-the-art pedestrian simulation (Generalized Centrifugal Force Model) as well as existing Data Mining methods for traffic volume estimation (Spatial k-Nearest Neighbour) and commonly used graph kernels for the Gaussian Process Regression (Squared Exponential, Regularized Laplacian and Diffusion Kernel) in terms of prediction performance (measured with mean absolute error). Our methods showed significantly lower error rates. Since pattern knowledge is not easy to obtain, we present algorithms for pattern acquisition and analysis from Episodic Movement Data. The proposed analysis of Episodic Movement Data involve spatio-temporal aggregation of visits and flows, cluster analyses and dependency models. For pedestrian mobility data collection we further developed and successfully applied the recently evolved Bluetooth tracking technology. The introduced methods are combined to a system for pedestrian mobility analysis which comprises three layers. The Sensor Layer (1) monitors geo-coded sensor recordings on people’s presence and hands this episodic movement data in as input to the next layer. By use of standardized Open Geographic Consortium (OGC) compliant interfaces for data collection, we support seamless integration of various sensor technologies depending on the application requirements. The Query Layer (2) interacts with the user, who could ask for analyses within a given region and a certain time interval. Results are returned to the user in OGC conform Geography Markup Language (GML) format. The user query triggers the (3) Analysis Layer which utilizes the mobility model for pedestrian volume estimation. The proposed approach is promising for location performance evaluation and attractor identification. Thus, it was successfully applied to numerous industrial applications: Zurich central train station, the zoo of Duisburg (Germany) and a football stadium (Stade des Costières Nîmes, France)