On Uncertainty in Context-Aware Computing: Appealing to High-Level and Same-Level Context for Low-Level Context Verification

Padovitz A., Loke S. W., Zaslavsky A., On Uncertainty in Context-Aware Computing: Appealing to High-Level and Same-Level Context for Low-Level Context Verification, in S. K. Mostefaoui et al (eds.) International Workshop on Ubiquitous Computing, 6th International Conference on Enterprise Information Systems (ICEIS) , 2004, INSTICC Press, Portugal, pp. 62 – 72

In context-aware systems factors that promote uncertainty are:

• Unsatisfactory combination of attribute types to infer
• Intrinsic ambiguity between two or more situations that impedes a straightforward reasoning about the correct contex
• Inherent inaccuracy and unreliability of many type of low-level sensors, which may lead to contradicting or substantially different reasoning about context (focus of this paper).

A context-aware system needs to resolve discrepancies as well as high-level context ambiguities that result from the contradicting sensor readings. Sensors sometimes yield to different or contradicting results when systems deal with sensors that are inherently innaccurate.The authors approach is:

We suggest a general high-level, logical approach that makes use of existing context reasoning and acquisition techniques that enables a context-aware system to resolve context ambiguities and optimize sensor-reading values. Our approach is the following: in order to verify a given sensor reading (i.e. low-level contextual information) such as location or light, we use other sensor readings and inferences upon such sensor readings.

We present a system prototype that filters sensed location readings according to a logical scheme using high-level contextual situations. We also present a simulation, used for critically assessing the logical filtering approach.

Logical filtering improves in general location error. However the degree of success of such an approach is dependent on the suitability of the system’s contextual configuration. The environment must somehow be controlled.

Related other positivist approaches like: Towards Reasoning About Context in the Presence of Uncertainty.
References to read:
Glassey R., Ferguson I., Modeling Location for Pervasive Environments, First UK-UbiNet Workshop, London, UK, 2003

Mäntyjärvi J., Seppänen T., Adapting Applications in Mobile Terminals Using Fuzzy Context Information, Mobile Human-Computer Interaction: 4th International Symposium, Mobile HCI 2002, Pisa, Italy, September 18-20, 2002

M. Satyanarayanan. “Coping with Uncertainty,” IEEE Pervasive Computing, vol. 02, no. 3, p. 2, July-September, 2003.

Relation to my thesis: This papers provides a positivist (quantitative/engineering) approach to my research topic on how to handle uncertainties that emerge when systems try to become aware at runtime and are indecisive in reasoning about the true situation. The difference with my approach is that they try to computationally (probability-based solution) decrease uncertainty while I think this is not enough and communicating about the system state/discrepeancier is necessary to disambiguate uncertain situations (or at least supporting disambiguation). Moreover, context is more about accurately detecting a location.