{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,21]],"date-time":"2026-02-21T10:07:30Z","timestamp":1771668450252,"version":"3.50.1"},"reference-count":91,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2024,12,19]],"date-time":"2024-12-19T00:00:00Z","timestamp":1734566400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Comput. Sci."],"abstract":"<jats:p>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. 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