The Context Awareness Challenges for PIS

Kirsch-Pinheiro, Manuele

doi:10.1007/978-3-031-18176-4_3

Cited by 2 publications

(1 citation statement)

References 55 publications

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“…The brain of a smart environment is a context-aware system. The system receives contextual information, analyzes it, and assigns it an activity label (Alegre et al, 2016;Hashemi and Sadeghi-Niaraki, 2016;Kirsch-Pinheiro, 2023). A context-aware system communicates with sensors and services in a smart environment and processes the context using machine-learning techniques (Ogbuabor et al, 2022).…”

Section: Evidence In Literaturementioning

confidence: 99%

Augmenting context with power information for green context-awareness in smart environments

Mahmud,

Hussain

2024

Front. Comput. Sci.

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The increase in the use of smart devices has led to the realization of the Internet of Everything (IoE). The heart of an IoE environment is a Context-Aware System that facilitates service discovery, delivery, and adaptation based on context classification. The context has been defined in a domain-dependent way, traditionally. The classical models of context have been focused on rich context and lack Cost of Context (CoC) that can be used for decision support. The authors present a philosophy-inspired mathematical model of context that includes confidence in activity classification of context, the actions performed, and the power information. Since a single recurring activity can lead to distinct actions performed at different times, it is better to record the actions. The power information includes the power consumed in the complete context processing and is a quality attribute of the context. Power consumption is a useful metric as CoC and is suitable for power-constrained context awareness. To demonstrate the effectiveness of the proposed work, example contexts are described, and the context model is presented mathematically in this study. The context is aggregated with power information, and actions and confidence on the classification outcome lead to the concept of situational context. The results show that the context gathered through sensor data and deduced through remote services can be made more rich with CoC parameters.

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Section: Evidence In Literaturementioning

confidence: 99%

Augmenting context with power information for green context-awareness in smart environments

Mahmud,

Hussain

2024

Front. Comput. Sci.

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

Mahmud,

Hussain,

Karamat

2024

Front. Comput. Sci.

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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.

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The Context Awareness Challenges for PIS

Cited by 2 publications

References 55 publications

Augmenting context with power information for green context-awareness in smart environments

Augmenting context with power information for green context-awareness in smart environments

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

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