Automorphisms and a Cartography of the Solution Space for Vacuum Bianchi Cosmologies: The Type III Case

The theory of symmetries of systems of coupled, ordinary differential equations (ODE's) is used to develop a concise algorithm for cartographing the space of solutions to vacuum Bianchi Einstein's Field Equations (EFE). The symmetries used are the well known automorphisms of the Lie algebra for the corresponding isometry group of each Bianchi Type, as well as the scaling and the time eparameterization symmetry. Application of the method to Type III results in: a) the recovery of all known solutions without prior assumption of any extra symmetry, b) the enclosure of the entire unknown part of the solution space into a single, second order ODE in terms of one dependent variable and c) a partial solution to this ODE. It is also worth-mentioning the fact that the solution space is seen to be naturally partitioned into three distinct, disconnected pieces: one consisting of the known Siklos (pp-wave) solution, another occupied by the Type III member of the known Ellis-MacCallum family and the third described by the aforementioned ODE in which an one parameter subfamily of the known Kinnersley geometries resides. Lastly, preliminary results reported show that the unknown part of the solution space for other Bianchi Types is described by a strikingly similar ODE, pointing to a natural operational unification as far as the problem of solving the cosmological EFE's is concerned.Comment: 19 pages, LatTex source file, no figures, accepted in JM

Situation determination with reusable situation specifications

Automatically determining the situation of an ad-hoc group of people and devices within a smart environment is a significant challenge in pervasive computing systems. Current approaches often rely on an environment expert to correlate the situations that occur with the available sensor data, while other machine learning based approaches require long training periods before the system can be used. In both cases, the situations are tailored to the specific environment, and are therefore not transferable to other environments. Furthermore, situations are recognised at a low-level of granularity, which limits the scope of situation-aware applications. This paper presents a novel approach to situation determination that attempts to overcome these issues by providing a reusable library of general situation specications that can be easily extended to create new speficic situations, and immediately deployed without the need of an environment expert. A proposed architecture of an accompanying situation determination middleware is provided, as well as an analysis of a prototype implementation

A model and architecture for situation determination

Automatically determining the situation of an ad-hoc group of people and devices within a smart environment is a significant challenge in pervasive computing systems. Current approaches often rely on an environment expert to correlate the situations that occur with the available sensor data, while other machine learning based approaches require long training periods before the system can be used. Furthermore, situations are commonly recognised at a low-level of granularity, which limits the scope of situation-aware applications. This paper presents a novel approach to situation determination that attempts to overcome these issues by providing a reusable library of general situation specifications that can be easily extended to create new specific situations, and immediately deployed without the need of an environment expert. A proposed architecture of an accompanying situation determination middleware is provided, as well as an analysis of a prototype implementation

Towards dynamic context discovery and composition

Context-awareness has been identified as a key characteristic for pervasive computing systems. As a variety of context-aware environments begin to flourish, pervasive applications shall have to interact different environments well. In this paper we propose extensions to the Strathclyde Context Infrastructure that gives context-aware applications the potential to adapt to unfamiliar environments transparently. We present a vision of a context discovery technique based on automated semantic reasoning about context information and services. The technique will offer higher levels of scalability and of interoperability with new context environments that cannot be achieved with current methods

Situation determination with distributed context histories

Determining the situation within an environment is a key goal of smart environment research. A significant challenge in situation determination is reasoning about openended groups of people and devices that a smart environment may contain. Contemporary solutions are often tailored to the specific environment. In this position paper, we present a novel general situation determination framework, that by viewing people and tools as playing roles in a situation, can easily adapt recognition to incorporate the dynamic structure of a situation over time

On trust and privacy in context-aware systems

Recent advances in networking, handheld computing and sensors technologies have led to the emergence of context-aware systems. The vast amounts of personal information collected by such systems has led to growing concerns about the privacy of their users. Users concerned about their private information are likely to refuse participation in such systems. Therefore, it is quite clear that for any context-aware system to be acceptable by the users, mechanisms for controlling access to personal information are a necessity. According to Alan Westin "privacy is the claim of individuals, groups, or institutions to determine for themselves when, how and to what extent information is communicated to others"1. Within this context we can classify users as either information owners or information receivers. It is also acknowledged that information owners are willing to disclose personal information if this disclosure is potentially beneficial. So, the acceptance of any context-aware system depends on the provision of mechanisms for fine-grained control of the disclosure of personal information incorporating an explicit notion of benefit