A Resource-Aware Preference Model for Context-Aware Systems

Uddin, Ijaz; Rakib, Abdur

doi:10.1007/978-3-319-77818-1_1

Cited by 3 publications

(1 citation statement)

References 17 publications

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“…However, these solutions require significant resources in terms of memory and computation, making them unsuitable for resource-constrained devices. To address this, Uddin & Rakib (2018) proposes a novel technique where users have control over their choices and are explicitly asked to indicate their interests in the knowledge base, enabling efficient resource utilization. While this approach seemed superior.…”

Section: Related Workmentioning

confidence: 99%

Efficient context-aware computing: a systematic model for dynamic working memory updates in context-aware computing

Ali,

Arshad,

Uddin

et al. 2024

PeerJ Computer Science

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The expanding computer landscape leads us toward ubiquitous computing, in which smart gadgets seamlessly provide intelligent services anytime, anywhere. Smartphones and other smart devices with multiple sensors are at the vanguard of this paradigm, enabling context-aware computing. Similar setups are also known as smart spaces. Context-aware systems, primarily deployed on mobile and other resource-constrained wearable devices, use a variety of implementation approaches. Rule-based reasoning, noted for its simplicity, is based on a collection of assertions in working memory and a set of rules that regulate decision-making. However, controlling working memory capacity efficiently is a key challenge, particularly in the context of resource-constrained systems. The paper’s main focus lies in addressing the dynamic working memory challenge in memory-constrained devices by introducing a systematic method for content removal. The initiative intends to improve the creation of intelligent systems for resource-constrained devices, optimize memory utilization, and enhance context-aware computing.

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Section: Related Workmentioning

confidence: 99%

Efficient context-aware computing: a systematic model for dynamic working memory updates in context-aware computing

Ali,

Arshad,

Uddin

et al. 2024

PeerJ Computer Science

Self Cite

View full text Add to dashboard Cite

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An Efficient Rule-Based Distributed Reasoning Framework for Resource-bounded Systems

Rakib

Uddin

2018

Mobile Netw Appl

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Over the last few years, context-aware computing has received a growing amount of attention among the researchers in the IoT and ubiquitous computing community. In principle, context-aware computing transforms a physical environment into a smart space by sensing the surrounding environment and interpreting the situation of the user. This process involves three major steps: context acquisition, context modelling, and context-aware reasoning. Among other approaches, ontologybased context modelling and rule-based context reasoning are widely used techniques to enable semantic interoperability and interpreting user situations. However, implementing rich context-aware applications that perform reasoning on resourcebounded mobile devices is quite challenging. In this paper, we present a context-aware systems development framework for smart spaces, which includes a lightweight efficient rule engine and a wide range of user preferences to reduce the number of rules while inferring personalized contexts. This shows rules can be reduced in order to optimize the inference engine execution speed, and ultimately to reduce total execution time and execution cost.

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