Self-organized Control of Knowledge Generation in Pervasive Computing Systems

Pervasive computing devices (e.g., sensor networks, localization devices, cameras, etc.) are increasingly present in every aspect of our lives. These devices are able to generate enormous amounts of data, from which knowledge about situations and facts occurring in the world can be inferred; inference can also be done by combining data items and generating new (higher-level) ones. Such data and knowledge is of extreme importance for to context-aware and mobile services. However, we are left with the problem that the possibly huge amount of data and knowledge generated can be very hard to be analyzed and made usable in real-time. The core of the problem in today's pervasive environments lies between the ability to extract meaningful (useful) knowledge from the data while making sure the total amount of data does not become overwhelming to the system. This paper focus on this trade-off using (without loss of generality) the W4 model for contextual data as a case study. Starting from the basic mechanism by which the W4 model autonomously generate new knowledge, the paper shows how this can generate knowledge overflow, and propose a method to select---in a self-organizing way---what kinds of knowledge should be generated based on their importance; hence preventing knowledge overflow. Experimental results are reported to support our arguments and proposals.