Towards an intelligent energy conservation approach for context-aware systems in smart environments

A smart personal space is a context-aware system that recognizes situations using contextual data. A user interacts within the personal space using smart devices that are mobile, and run-on batteries that have limited power. This paper proposes a Power-Constrained Context-Aware System (PCCA) that uses Markov Chain-based pre-classification to predict context change and defer context processing to conserve energy in an intelligent way. A new Markov Chain Module is added that creates a Markov Chain using history information. This enables PCCA to predict context change for the next observation. The results show that PCCA consumes 37% less power than a context-aware system.