Emerging privacy challenges and approaches in CAV systems

The growth of Internet-connected devices, Internet-enabled services and Internet of Things systems continues at a rapid pace, and their application to transport systems is heralded as game-changing. Numerous developing CAV (Connected and Autonomous Vehicle) functions, such as traffic planning, optimisation, management, safety-critical and cooperative autonomous driving applications, rely on data from various sources. The efficacy of these functions is highly dependent on the dimensionality, amount and accuracy of the data being shared. It holds, in general, that the greater the amount of data available, the greater the efficacy of the function. However, much of this data is privacy-sensitive, including personal, commercial and research data. Location data and its correlation with identity and temporal data can help infer other personal information, such as home/work locations, age, job, behavioural features, habits, social relationships. This work categorises the emerging privacy challenges and solutions for CAV systems and identifies the knowledge gap for future research, which will minimise and mitigate privacy concerns without hampering the efficacy of the functions

Planning And Control Of Swarm Motion As Continua

In this thesis, new algorithms for formation control of multi agent systems (MAS) based on continuum mechanics principles will be investigated. For this purpose agents of the MAS are treated as particles in a continuum, evolving in an n-D space, whose desired configuration is required to satisfy an admissible deformation function. Considered is a specific class of mappings that is called homogenous where the Jacobian of the mapping is only a function of time and is not spatially varying. The primary objectives of this thesis are to develop the necessary theory and its validation via simulation on a mobile-agent based swarm test bed that includes two primary tasks: 1) homogenous transformation of MAS and 2) deployment of a random distribution of agents on to a desired configuration. Developed will be a framework based on homogenous transformations for the evolution of a MAS in an n-D space (n=1, 2, and 3), under two scenarios: 1) no inter-agent communication (predefined motion plan); and 2) local inter-agent communication. Additionally, homogenous transformations based on communication protocols will be used to deploy an arbitrary distribution of a MAS on to a desired curve. Homogenous transformation with no communication: A homogenous transformation of a MAS, evolving in an space, under zero inter agent communication is first considered. Here the homogenous mapping, is characterized by an n x n Jacobian matrix ( ) and an n x 1 rigid body displacement vector ( ), that are based on positions of n+1 agents of the MAS, called leader agents. The designed Jacobian ( ) and rigid body displacement vector ( ) are passed onto rest of the agents of the MAS, called followers, who will then use that information to update their positions under a pre- iv defined motion plan. Consequently, the motion of MAS will evolve as a homogenous transformation of the initial configuration without explicit communication among agents. Homogenous Transformation under Local Communication: We develop a framework for homogenous transformation of MAS, evolving in , under a local inter agent communication topology. Here we assume that some agents are the leaders, that are transformed homogenously in an n-D space. In addition, every follower agent of the MAS communicates with some local agents to update its position, in order to grasp the homogenous mapping that is prescribed by the leader agents. We show that some distance ratios that are assigned based on initial formation, if preserved, lead to asymptotic convergence of the initial formation to a final formation under a homogenous mapping. Deployment of a Random Distribution on a Desired Manifold: Deployment of agents of a MAS, moving in a plane, on to a desired curve, is a task that is considered as an application of the proposed approach. In particular, a 2-D MAS evolution problem is considered as two 1-D MAS evolution problems, where x or y coordinates of the position of all agents are modeled as points confined to move on a straight line. Then, for every coordinate of MAS evolution, bulk motion is controlled by two agents considered leaders that move independently, with rest of the follower agents motions evolving through each follower agent communicating with two adjacent agents

Evolutionary games on graphs

Game theory is one of the key paradigms behind many scientific disciplines from biology to behavioral sciences to economics. In its evolutionary form and especially when the interacting agents are linked in a specific social network the underlying solution concepts and methods are very similar to those applied in non-equilibrium statistical physics. This review gives a tutorial-type overview of the field for physicists. The first three sections introduce the necessary background in classical and evolutionary game theory from the basic definitions to the most important results. The fourth section surveys the topological complications implied by non-mean-field-type social network structures in general. The last three sections discuss in detail the dynamic behavior of three prominent classes of models: the Prisoner's Dilemma, the Rock-Scissors-Paper game, and Competing Associations. The major theme of the review is in what sense and how the graph structure of interactions can modify and enrich the picture of long term behavioral patterns emerging in evolutionary games.Comment: Review, final version, 133 pages, 65 figure

Endogenous space in the Net era

Libre Software communities are among the most interesting and advanced socio-economic laboratories on the Net. In terms of directions of Regional Science research, this paper addresses a simple question: “Is the socio-economics of digital nets out of scope for Regional Science, or might the latter expand to a cybergeography of digitally enhanced territories ?” As for most simple questions, answers are neither so obvious nor easy. The authors start drafting one in a positive sense, focussing upon a file rouge running across the paper: endogenous spaces woven by socio-economic processes. The drafted answer declines on an Evolutionary Location Theory formulation, together with two computational modelling views. Keywords: Complex networks, Computational modelling, Economics of Internet, Endogenous spaces, Evolutionary location theory, Free or Libre Software, Path dependence, Positionality.

Modeling and Control of Multi-Agent Systems

Biology has brought much enlightenment to the development of human technology, for example, the collective behaviors inspired engineering applications (such as, the unmanned vehicle formation, the satellite alignment etc.), and even the study of network theory. This discipline has made a significant contribution to technology development. As a prospective solution to the current issues, multi-agent control has become a popular research topic in recent decades. The traditional control methods based on the classical models are suffering from high sensitivity to model accuracy, computational complexity, low fault tolerance, and weakness in real-time performance. Therefore, the advantages of multi-agent control are obvious: 1) easy maintenance and expansion of the system by repairing, replacing or adding agents; 2) high fault tolerance and robustness, ability to function properly even when some agents fail; 3) low requirement of distributed controllers, which brings low cost and large flexibility. In this thesis, I investigate problems on modeling and control of multi-agent systems. In particular, I propose a three-dimensional model to simulate collective behavior under high-speed conditions. I design an improved adaptive-velocity strategy and weighted strategy to enhance the performance of the multi-agent system. Moreover, I analyze the performance from the aspects of energy and parameter space. I show how the model works and its advantages compared to existing models. Then, I study the design of distributed controllers for multi-agent systems. Output regulation with input saturation and nonlinear flocking problems are studied with the assumption of a heterogeneous switching topology. The output regulation problem is solved via low gain state feedback and its validity verified by theoretical study. Then, the flocking problem with heterogeneous nonlinear dynamics is solved. A connectivity-preserving algorithm and potential function are designed to ensure the controllability of the multi-agent system through the dynamic process. Overall, this thesis provides examples of how to analyze and manipulate multi-agent systems. It offers promising solutions to solve physical multi-agent modeling and control problems and provides ideas for bio-inspired engineering and artificial intelligent control for multi-agent systems