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

Ali, M; Arshad, M; Uddin, I; Binsawad, M; Bin Sawad, A; Sohaib, O

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

Ali, M Arshad, M Uddin, I Binsawad, M Bin Sawad, A Sohaib, O

Permalink

Publisher:: PeerJ
Publication Type:: Journal Article
Citation:: PeerJ Computer Science, 10, pp. e2129-e2129

Open Access

Copyright Clearance Process

Recently Added
In Progress
Open Access

This item is open access.

Adobe PDF

Download Published versionAdobe PDF (3.86 MB)

View on publisher's site

View statistics

Full metadata record

Field	Value	Language
dc.contributor.author	Ali, M
dc.contributor.author	Arshad, M
dc.contributor.author	Uddin, I
dc.contributor.author	Binsawad, M
dc.contributor.author	Bin Sawad, A
dc.contributor.author	Sohaib, O https://orcid.org/0000-0001-9287-5995
dc.date.accessioned	2024-06-13T07:51:59Z
dc.date.available	2024-06-13T07:51:59Z
dc.identifier.citation	PeerJ Computer Science, 10, pp. e2129-e2129
dc.identifier.issn	2376-5992
dc.identifier.uri	http://hdl.handle.net/10453/179517
dc.description.abstract	<jats:p>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.</jats:p>
dc.language	en
dc.publisher	PeerJ
dc.relation	King Abdulaziz University
dc.relation.ispartof	PeerJ Computer Science
dc.relation.isbasedon	10.7717/peerj-cs.2129
dc.rights	info:eu-repo/semantics/openAccess
dc.subject	0806 Information Systems
dc.subject.classification	46 Information and computing sciences
dc.title	Efficient context-aware computing: a systematic model for dynamic working memory updates in context-aware computing
dc.type	Journal Article
utslib.citation.volume	10
utslib.for	0806 Information Systems
pubs.organisational-group	University of Technology Sydney
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
utslib.copyright.status	open_access	*
dc.rights.license	This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0). To view a copy of this license, visit https://creativecommons.org/licenses/by/4.0/
dc.date.updated	2024-06-13T07:51:49Z
pubs.publication-status	Published online
pubs.volume	10

Abstract:

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.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/179517