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Activity Recognition and User Preference Learning for Automated Configuration of IoT Environments
Malmö University, Faculty of Technology and Society (TS), Department of Computer Science and Media Technology (DVMT). Malmö University, Internet of Things and People (IOTAP).ORCID iD: 0000-0002-8025-4734
Malmö University, Faculty of Technology and Society (TS), Department of Computer Science and Media Technology (DVMT). Malmö University, Internet of Things and People (IOTAP).
Malmö University, Faculty of Technology and Society (TS), Department of Computer Science and Media Technology (DVMT). Malmö University, Internet of Things and People (IOTAP).ORCID iD: 0000-0003-0326-0556
Malmö University, Faculty of Technology and Society (TS), Department of Computer Science and Media Technology (DVMT). Malmö University, Internet of Things and People (IOTAP).ORCID iD: 0000-0003-0998-6585
2020 (English)In: IoT '20: Proceedings of the 10th International Conference on the Internet of Things, New York, United States: ACM Digital Library, 2020, p. 1-8, article id 3Conference paper, Published paper (Refereed)
Abstract [en]

Internet of Things (IoT) environments encompass different types of devices and objects that offer a wide range of services. The dynamicity and uncertainty of those environments, including the mobility of users and devices, make it hard to foresee at design time available devices, objects, and services. For the users to benefit from such environments, they should be proposed services that are relevant to the specific context and can be provided by available things. Moreover, environments should be configured automatically based on users' preferences. To address these challenges, we propose an approach that leverages Artificial Intelligence techniques to recognize users' activities and provides relevant services to support users to perform their activities. Moreover, our approach learns users' preferences and configures their environments accordingly by dynamically forming, enacting, and adapting goal-driven IoT systems. In this paper, we present a conceptual model, a multi-tier architecture, and processes of our approach. Moreover, we report about how we validated the feasibility and evaluated the scalability of the approach through a prototype that we developed and used.

Place, publisher, year, edition, pages
New York, United States: ACM Digital Library, 2020. p. 1-8, article id 3
National Category
Information Systems, Social aspects
Identifiers
URN: urn:nbn:se:mau:diva-36986DOI: 10.1145/3410992.3411003Scopus ID: 2-s2.0-85123041965ISBN: 978-1-4503-8758-3 (print)OAI: oai:DiVA.org:mau-36986DiVA, id: diva2:1504048
Conference
IoT '20: 10th International Conference on the Internet of Things, Malmö Sweden 6-9 October, 2020
Available from: 2020-11-26 Created: 2020-11-26 Last updated: 2024-02-05Bibliographically approved
In thesis
1. Realizing Emergent Configurations in the Internet of Things
Open this publication in new window or tab >>Realizing Emergent Configurations in the Internet of Things
2020 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

The Internet of Things (IoT) is a fast-spreading technology that enables new types of services in several domains such as transportation, health, and building automation. To exploit the potential of the IoT effectively, several challenges have to be tackled, including the following ones that we study in this thesis. First, the proposed IoT visions provide a fragmented picture, leading to a lack of consensus about IoT systems and their constituents. To piece together the fragmented picture of IoT systems, we systematically identified their characteristics by analyzing existing taxonomies. More specifically, we identified seventeen characteristics of IoT systems, and grouped them into two categories, namely, elements and quality aspects of IoT systems. Moreover, we conducted a survey to identify the factors that drive the deployment decisions of IoT systems in practice. A second set of challenges concerns the environment of IoT systems that is often dynamic and uncertain. For instance, due to the mobility of users and things, the set of things available in users' environment might change suddenly. Similarly, the status of IoT systems’ deployment topologies (i.e., the deployment nodes and their interconnections) might change abruptly. Moreover, environmental conditions monitored and controlled through IoT devices, such as ambient temperature and oxygen levels, might fluctuate suddenly. The majority of existing approaches to engineer IoT systems rely on predefined processes to achieve users’ goals. Consequently, such systems have significant shortcomings in coping with dynamic and uncertain environments. To address these challenges, we used the concept of Emergent Configurations (ECs) to engineer goal-driven IoT systems. An EC is an IoT system that consists of a dynamic set of things that cooperate temporarily to achieve a user goal. To realize ECs, we proposed an abstract architectural approach, comprising an architecture and processes, as well as six novel approaches that refine the abstract approach. The developed approaches support users to achieve their goals seamlessly in arbitrary environments by enabling the dynamic formation, deployment, enactment, and self-adaptation of IoT systems. The approaches exploit different techniques and focus on different aspects of ECs. Moreover, to better support users in dynamic and uncertain environments, we investigated the automated configuration of those environments based on users' preferences. 

Place, publisher, year, edition, pages
Malmö: Malmö universitet, 2020. p. 254
Series
Studies in Computer Science ; 12
Keywords
Internet of Things, Emergent Configurations, Goal-driven IoT Systems, Automated Configuration of IoT environments, Software Architectures, Self-adaptive Systems.
National Category
Engineering and Technology
Identifiers
urn:nbn:se:mau:diva-18508 (URN)10.24834/isbn.9789178771226 (DOI)978-91-7877-121-9 (ISBN)978-91-7877-122-6 (ISBN)
Public defence
2020-12-18, Digitalt, 10:00 (English)
Opponent
Supervisors
Projects
Emergent Configurations for IoT Systems – ECOS+
Available from: 2020-10-06 Created: 2020-10-06 Last updated: 2023-12-28Bibliographically approved

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Alkhabbas, FahedAlawadi, SadiSpalazzese, RominaDavidsson, Paul

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