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Assuring intelligent ambient assisted living solutions by statistical model checking
Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.ORCID iD: 0000-0002-7663-5497
Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.ORCID iD: 0000-0003-2870-2680
2018 (English)In: Lect. Notes Comput. Sci., Springer Verlag , 2018, p. 457-476Conference paper, Published paper (Refereed)
Abstract [en]

A modern way of enhancing elderly people’s quality of life is by employing various Ambient Assisted Living solutions that facilitate an independent and safe living for their users. This is achieved by integrating computerized functions such as health and home monitoring, fall detection, reminders, etc. Such systems are safety critical, therefore ensuring at design time that they operate correctly, but also in a timely and robust manner is important. Most of the solutions are not analyzed formally at design time, especially if such Ambient Assisted Living functions are integrated within the same design. To address this concern, we propose a framework that relies on an abstract component-based description of the system’s architecture in the Architecture Analysis and Design Language. To ensure scalability of analysis, we transform the AADL models into a network of stochastic timed automata amenable to statistical analysis of various quality-of-service attributes. The architecture that we analyze is developed as part of the project CAMI, co-financed by the European Commission, and consists of a variety of health and home sensors, a data collector, local and cloud processing, as well as an artificial-intelligence-based decision support system. Our contribution paves the way towards achieving design-time assured integrated Ambient Assisted Living solutions, which in turn could reduce verification effort at later stages.

Place, publisher, year, edition, pages
Springer Verlag , 2018. p. 457-476
Series
Lecture Notes in Computer Science, ISSN 0302-9743 ; 11245 LNCS
Keywords [en]
Artificial intelligence, Computer architecture, Decision support systems, Formal methods, Model checking, Network architecture, Quality control, Quality of service, Safety engineering, Stochastic models, Stochastic systems, Ambient assisted living, Architecture analysis, Cloud processing, Component based, Design languages, European Commission, Home monitoring, Statistical model checking, Assisted living
National Category
Computer and Information Sciences
Identifiers
URN: urn:nbn:se:mdh:diva-41449DOI: 10.1007/978-3-030-03421-4_29Scopus ID: 2-s2.0-85056457127ISBN: 9783030034207 (print)OAI: oai:DiVA.org:mdh-41449DiVA, id: diva2:1266706
Conference
5 November 2018 through 9 November 2018
Available from: 2018-11-29 Created: 2018-11-29 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: 2019-04-01Bibliographically approved

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Kunnappilly, AshalathaMarinescu, RalucaSeceleanu, Cristina

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