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A Novel Integrated Architecture for Ambient Assisted Living Systems
Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
University Politehnica of Bucharest, Romania.
University Politehnica of Bucharest, Romania.
University Politehnica of Bucharest, Romania.
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2017 (English)In: The 41st IEEE Computer Society International Conference on Computers, Software & Applications COMPSAC 2017, 2017, Vol. 1, p. 465-472, article id 8029645Conference paper, Published paper (Refereed)
Abstract [en]

The increase in life expectancy and the slumping birth rates across the world result in lengthening the average age of the society. This change in demography has many consequences, the major being the insufficient number of caregivers. Therefore, we are in need of techniques that will assist the elderly in their daily life, while preventing their social isolation. The recent developments in Ambient Intelligence (AmI) and Information and Communication Technologies (ICT) have facilitated a technological revolution in the field of Ambient Assisted Living (AAL). At present, there are many technologies on the market that support the independent life of older adults, requiring less assistance from family and caregivers, yet most of them offer isolated services, such as health monitoring, supervised exercises, reminders etc. There are only very few architectures that support the seamless integration of various functionalities and none of them incorporates user preferences or are formally analyzed for their functionality and quality-of-service attributes which is needed in order to ensure safe mitigations of potential critical scenarios. In this paper, we propose a novel architectural solution that seamlessly integrates necessary functions of an AAL system, based on user preferences. To enable a first level of the architecture's analysis, we model our system in Architecture Analysis and Design Language (AADL), and carry out its simulation for analyzing the end-to-end data-flow latency, resource budgets and system safety.

Place, publisher, year, edition, pages
2017. Vol. 1, p. 465-472, article id 8029645
Keywords [en]
Ambient Intelligence, Ambient Assisted Living, Architecture Analysis and Design Language.
National Category
Computer Systems
Identifiers
URN: urn:nbn:se:mdh:diva-35500DOI: 10.1109/COMPSAC.2017.28ISI: 000424861400061Scopus ID: 2-s2.0-85031918944OAI: oai:DiVA.org:mdh-35500DiVA, id: diva2:1107564
Conference
The 41st IEEE Computer Society International Conference on Computers, Software & Applications COMPSAC 2017, 04 Jul 2017, Turin, Italy
Projects
CAMI - Artificially intelligent ecosystem for self-management and sustainable quality of life in AAL (Ambient Assisted Living)Available from: 2017-06-09 Created: 2017-06-09 Last updated: 2019-03-14Bibliographically approved
In thesis
1. Formally Assured Intelligent Systems for Enhanced Ambient Assisted Living Support
Open this publication in new window or tab >>Formally Assured Intelligent Systems for Enhanced Ambient Assisted Living Support
2019 (English)Licentiate thesis, comprehensive summary (Other academic)
Abstract [en]

Ambient Assisted Living (AAL) solutions are aimed to assist the elderly in their independent and safe living. During the last decade, the AAL field has witnessed a significant development due to advancements in Information and Communication Technologies, Ubiquitous Computing and Internet of Things. However, a closer look at the existing AAL solutions shows that these improvements are used mostly to deliver one or a few functions mainly of the same type (e.g. health monitoring functions). There are comparatively fewer initiatives that integrate different kinds of AAL functionalities, such as fall detection, reminders, fire alarms, etc., besides health monitoring, into a common framework, with intelligent decision-making that can thereby offer enhanced reasoning by combining multiple events. 

 

To address this shortage, in this thesis, we propose two different categories of AAL architecture frameworks onto which different functionalities, chosen based on user preferences, can be integrated. One of them follows a centralized approach, using an intelligent Decision Support System (DSS), and the other, follows a truly distributed approach, involving multiple intelligent agents. The centralized architecture is our initial choice, due to its ease of development by combining multiple functionalities with a centralized DSS that can assess the dependency between multiple events in real time. While easy to develop, our centralized solution suffers from the well-known single point of failure, which we remove by adding a redundant DSS. Nevertheless, the scalability, flexibility, multiple user accesses, and potential self-healing capability of the centralized solution are hard to achieve, therefore we also propose a distributed, agent-based architecture as a second solution, to provide the community with two different AAL solutions that can be applied depending on needs and available resources. Both solutions are to be used in safety-critical applications, therefore their design-time assurance, that is, providing a guarantee that they meet functional requirements and deliver the needed quality-of-service, is beneficial. 

 

Our first solution is a generic architecture that follows the design of many commercial AAL solutions with sensors, a data collector, DSS, security and privacy, database (DB) systems, user interfaces (UI), and cloud computing support. We represent this architecture in the Architecture Analysis and Design Language (AADL) via a set of component patterns that we propose. The advantage of using patterns is that they are easily re-usable when building specific AAL architectures. Our patterns describe the behavior of the components in the Behavioral Annex of AADL, and the error behavior in AADL's Error Annex. We also show various instantiations of our generic model that can be developed based on user requirements. To formally assure these solutions against functional, timing and reliability requirements, we show how we can employ exhaustive model checking using the state-of-art model checker, UPPAAL, and also statistical model-checking techniques with UPPAAL SMC, an extension of the UPPAAL model checker for stochastic systems, which can be employed in cases when exhaustive verification does not scale. The second proposed architecture is an agent-based architecture for AAL systems, where agents are intelligent entities capable of communicating with each other in order to decide on an action to take. Therefore, the decision support is now distributed among agents and can be used by multiple users distributed across multiple locations. Due to the fact that this solution requires describing agents and their interaction, the existing core AADL does not suffice as an architectural framework. Hence, we propose an extension to the core AADL language - The Agent Annex, with formal semantics as Stochastic Transition Systems, which allows us to specify probabilistic, non-deterministic and real-time AAL system behaviors. In order to formally assure our multi-agent system, we employ the state-of-art probabilistic model checker PRISM, which allows us to perform probabilistic yet exhaustive verification.

 

As a final contribution, we also present a small-scale validation of an architecture of the first category, with end users from three countries (Romania, Poland, Denmark). This work has been carried out with partners from the mentioned countries. 

 

Our work in this thesis paves the way towards the development of user-centered, intelligent ambient assisted living solutions with ensured quality of service.

Place, publisher, year, edition, pages
Västerås: Mälardalen University, 2019
Series
Mälardalen University Press Licentiate Theses, ISSN 1651-9256 ; 278
National Category
Embedded Systems
Research subject
Computer Science
Identifiers
urn:nbn:se:mdh:diva-42922 (URN)978-91-7485-425-1 (ISBN)
Presentation
2019-04-15, Milos, Mälardalens högskola, Västerås, 13:30 (English)
Opponent
Supervisors
Available from: 2019-03-19 Created: 2019-03-14 Last updated: 2019-03-19Bibliographically approved

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Kunnappilly, AshalathaSeceleanu, Cristina

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