On Data Management in Pervasive Computing Environments

Abstract

This paper presents a framework to address new data management challenges introduced by data-intensive, pervasive computing environments. These challenges include a spatio-temporal variation of data and data source availability, lack of a global catalog and schema, and no guarantee of reconnection among peers due to the serendipitous nature of the environment. An important aspect of our solution is to treat devices as semi-autonomous peers guided in their interactions by profiles and context. The profiles are grounded in a semantically rich language and represent information about users, devices and data described in terms of “beliefs”, “desires”, and “intentions”. We present a prototype implementation of this framework over combined Bluetooth and Ad-Hoc 802.11 networks, and present experimental and simulation results that validate our approach and measure system performance.