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

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 inter...

Full description

Saved in:
Bibliographic Details
Published in:Mobile networks and applications 2019-02, Vol.24 (1), p.82-99
Main Authors: Rakib, Abdur, Uddin, Ijaz
Format: Article
Language:English
Subjects:
Algorithms
Ambient intelligence
Artificial intelligence
Communications Engineering
Computer Communication Networks
Computer science
Computers
Context
Electrical Engineering
Electronic devices
Engineering
Internet of Things
Interoperability
IT in Business
Knowledge
Modelling
Networks
Ontology
Reasoning
Semantic web
Semantics
Sensors
Smartphones
Social research
Ubiquitous computing
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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, ontology-based 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 resource-bounded 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.
ISSN:1383-469X
1572-8153
DOI:10.1007/s11036-018-1140-x