Middleware Support for Quality of Context in Pervasive Context-Aware Systems
PERCOMW '07 Proceedings of the Fifth IEEE International Conference on Pervasive Computing and Communications Workshops
Quality Aware Context Information Aggregation System for Pervasive Environments
WAINA '09 Proceedings of the 2009 International Conference on Advanced Information Networking and Applications Workshops
Modeling and Measuring Quality of Context Information in Pervasive Environments
AINA '10 Proceedings of the 2010 24th IEEE International Conference on Advanced Information Networking and Applications
Building ubiquitous QoC-aware applications through model-driven software engineering
Science of Computer Programming
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It is hard to believe that the internet is now in its adolescent stage. Our information age is replete with communication capable, intelligent, sensor equipped devices. Social networks, web services, and global information repositories make a wealth of information available instantly. There exist endless possibilities for creating useable knowledge. However, the importance of information quality becomes more urgent as information quantity increases. Integrity of knowledge directly affects its value and usefulness. Deciphering integrity can be difficult without the appropriate middleware quality measures in place. Since, context middleware separates applications from the concerns of quality enhanced context-sensing data and reasoning, it also bears responsibility for maintaining expected information quality. To accomplish this, the context middleware must aggregate quality factors as it senses raw data from heterogeneous sources and reasons to infer new knowledge. Additionally, middleware must periodically monitor its quality performance and adapt to meet requirements. In this work, we: i) propose a specification for information quality along with new middleware quality measures that reflect middleware success in adapting to maintain specified requirements; ii) describe the operation of conceptual components supporting quality maintenance and adaptable context reasoning, a refinement to our previous conceptual architecture for hybrid context reasoning; and iii) identify quality propagation challenges created by hybrid high level context reasoning; offering solutions to both issues of context quality aggregation and propagation.