Intention as a Context: An Activity Intention Model for Adaptable Development of Applications in the Internet of Things

End-users, such as aging people, look for dynamic applications to help them in their daily activities while usually assisted by domain experts (e.g., physicians) in performing these activities. Nowadays, the Internet of Things (IoT) augments applications with a changing context based on the activiti...

Full description

Saved in:
Bibliographic Details
Published in:IEEE access 2021, Vol.9, p.151167-151185
Main Authors: Ponce, Victor, Abdulrazak, Bessam
Format: Article
Language:English
Subjects:
Activity model
Computational modeling
Context
Context modeling
context-aware application
Data models
Domains
Hidden Markov models
intention model
Internet of Things
Optimization
Pattern matching
Physicians
Semantics
Smart buildings
Software development
Subject specialists
Citations: Items that this one cites
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:End-users, such as aging people, look for dynamic applications to help them in their daily activities while usually assisted by domain experts (e.g., physicians) in performing these activities. Nowadays, the Internet of Things (IoT) augments applications with a changing context based on the activities, environments, and services. Unfortunately, most IoT application development tools are restricted to specific scenarios or involve technical challenges. We propose an activity intention model for quick application development, targeting domain experts who are not traditional software developers. Our model is based on the ContextAA micro context-awareness approach with autonomic computing-based components providing pervasive adaptations. Unlike other Internet of Things application definitions, our model promotes elicitation of the activity semantics and provides mechanisms to compute the semantics. We then generate intention as a context (IaaC), which contains a self-described activity intention context with compiled knowledge ready to be assessed in pervasive smart environments for augmented adaptations. Experimental results show a potential resource optimization for dynamic Internet of Things applications in smart homes and smart cities.
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2021.3126000