Holzmann Spatial Awareness of Autonomous Embedded Systems

1. Auflage 2009
ISBN: 978-3-8348-9569-1
Verlag: Vieweg & Teubner
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, 206 Seiten, Web PDF

Reihe: Computer Science

Our environment is increasingly pervaded by embedded systems which are integrated into everyday objects, equipped with sensors and actuators, networked among each other and available at any time and in any location. These computationally augmented real-world objects must be supplied with context-awareness and need to share the perceived context-information to enable them to operate more autonomously.

Clemens Holzmann investigates the role of spatial contexts for autonomous embedded systems, in particular the position, direction, and spatial extension of objects with respect to an external reference system or other objects. The author presents concepts for recognizing, representing, and reasoning about qualitative spatial relations and their changes over time, as well as an appropriate architecture which has prototypically been implemented in a flexible software framework. His results show that the proposed concepts are suitable for developing spatially aware applications and that qualitatively abstracted relations can constitute an adequate basis for this purpose.

Zielgruppe

Research

Autoren/Hrsg.

Holzmann, Clemens

Inhaltsverzeichnis

Spatial Awareness.- Representation of Space.- Distributed Spatial Reasoning.- Rule-Based Spatial Awareness.- Zones-of-Influence Framework.- Framework Evaluation.

Leseproben

3 Representation of Space (S. 45-46)

Simple things should be simple. Complex things should be possible. Alan Kay, 1940

In order to deal with space computationally, it has to be represented in a standardized way such that it can be processed by a computer. In this regard, we distinguish between the quantitative and qualitative representation of space, which are discussed in detail in the Sections 3.1 and 3.2, respectively. While the former deals with concrete facts about the spatial properties of an artifact using numerical values (as provided by sensors for example), the latter is concerned with symbolic and human-readable abstractions of spatial relations (i.e. symbols are used for the representation rather than numeric values) which are acquired by a pairwise comparison of quantitative spatial properties. For the representation of an artifact’s spatial properties we propose to use so-called Zones-of-In.uence, which are explicitly defined geographical regions that are relevant for the application. Our focus is on qualitative spatial relationships as well as their changes over time and the application-dependent semantics.

We consider qualitative relationship abstractions to be particularly valuable for implementing services that are distributed among multiple artifacts in physical space. Quantitative spatial properties and qualitative spatial relations – which together constitute an artifact’s spatial context – are exchanged between artifacts in range by means of XML-based self-descriptions, which enables them to reason about both self-determined and received relations, an example structure of self-descriptions, which we used for evaluation purposes in this thesis, is presented in Section 3.3. Section 3.4 eventually sums up findings concerning the quantitative and qualitative representation of spatial properties and relations with regard to their representation and exchange by autonomous embedded systems.

3.1 Quantitative Representation

3.1.1 Spatial Abstraction with Zones-of-Influence

For providing spatial awareness to digital artifacts, we propose a concept referred to as Zones-of-In.uence (ZoI). It builds on initial work published in [FHR+08], where ZoIs are three-dimensional spatial regions associated with digital artifacts, in this work, they serve as an explicit proximity model in that they are used for limiting the interaction between artifacts to those whose ZoI-geometries overlap. In contrast to proximity sensors like Bluetooth signal-strength measurements of nearby artifacts as used in [FHM+04, FHO04a] for example, a Zone-of-In.uence provides added value by allowing to explicitly de.ne the "proximity range" by its shape, the size of the shape as well as its position and direction in space relative to that of the artifact for which it is defined. A similar concept has also been used in [BMK+00, HB00, HHS+02], where geometric containment relations between regions of interest that are associated with physical objects (e.g. a person and the area in front of a screen) are used for location-aware applications (e.g. to display the person’s desktop on that screen).

Über Autor(innen)

Dr. Clemens Holzmann is a research and teaching assistant at the Institute for Pervasive Computing of the Johannes Kepler University of Linz (Austria). His research interests include location awareness and context awareness as well as wireless ad hoc and sensor networks.