A Distributed Architecture for Reasoning about a Higher-Level Context

This paper presents a distributed architecture for reasoning about a higher-level context as an abstraction of a dynamic real-world situation. Reasoning about a higher-level context entails dealing with data acquired from sensors, which can be inexact, incomplete, and/or uncertain. Inexact sensing arises mostly due to the inherent limitation of sensors to precisely capture a real world phenomenon. Incompleteness is caused by the absence of a mechanism to capture certain real-world aspects; and uncertainty stems from the lack of knowledge about the reliability of the sensing sources, such as their sensing range, accuracy, and resolution. The proposed architecture enables the modeling of a context with facts and beliefs; the model is useful for dealing with data from a variety of sensors with different sensing specifications. It will be shown how the architecture enables the application of empirical knowledge of some physical properties of a place (temperature, relative humidity, sound pressure, light intensity and time) to model and reason about a person's whereabouts. Subsequently, depending on the types and reliability of sensors available at any given time, a mobile device could be able to discriminate between various places-corridors, rooms, buildings, and outdoors-with different degrees of uncertainty