A data distribution model for large-scale context aware systems

Abstract

Very large scale context aware systems are becoming a reality with the emerging paradigms such as machine-to-machine communications, crowdsensing, etc. Scalable data distribution is a critical requirement in such large scale systems for optimal usage of computing and communication resources. In this paper, we present a novel theoretical model for middleware design for such large-scale context aware systems that distributes only relevant data based on its effective utility. We also present extensive experimental results to validate the efficacy of our proposed model.