Quality traceability for user-centric context-aware systems in intelligent environments

Sakanga, Nawa, Augusto, Juan Carlos ORCID logoORCID: https://orcid.org/0000-0002-0321-9150, Brodie, Lindsey and Marzano, Lisa ORCID logoORCID: https://orcid.org/0000-0001-9735-3512 (2022) Quality traceability for user-centric context-aware systems in intelligent environments. 2022 IEEE 8th World Forum on Internet of Things (WF-IoT). In: Work-07: Digital Innovation for people well-being: Healthcare and Agriculture perspectives (co-located with IEEE 8th World Forum on Internet of Things), 26 Oct - 11 Nov 2022, Yokohama, Japan. . [Conference or Workshop Item] (Accepted/In press)

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Abstract

Context-awareness is an important component of modern software systems. For example, in Ambient Assisted Living (AAL), the concept of context-awareness empowers users by reducing their dependence on others. Due to this role in healthcare, such systems need to be reliable and usable by their intended users. Our research addresses the development, testing and validation of context-aware systems in an emerging field which currently lacks sufficient systems engineering processes and disciplines. One specific issue being that developers often focus on delivering a system that works at some level, rather than engineering a system that meets a specified set of system requirements and their corresponding qualities. Our research aims to contribute towards improving the delivery of system quality by tracing, developing and linking systems development data for requirements, contexts including sensors, test cases and their results, and user validation tests and their results. We refer to this approach as the “quality traceability of context-aware systems”. In order to support the developer, the quality traceability of context-aware systems introduces a systems development approach tailored to context-aware systems in intelligent environments, an automated system testing tool and system validation process. We have implemented a case study to inform the research. The case study is in healthcare and based on an AAL system used to remotely monitor and manage, in real time, an individual prone to depressive symptoms.

Item Type: Conference or Workshop Item (Paper)
Sustainable Development Goals:
Theme:
Research Areas: A. > School of Science and Technology > Computer Science > Intelligent Environments group
Item ID: 36050
Notes on copyright: © 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
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Depositing User: Juan Augusto
Date Deposited: 23 Sep 2022 10:38
Last Modified: 25 Nov 2022 13:34
URI: https://eprints.mdx.ac.uk/id/eprint/36050

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