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Enabling distributed intelligence assisted Future Internet of Things Controller (FITC)
Stockholm University, Faculty of Social Sciences, Department of Computer and Systems Sciences.
Stockholm University, Faculty of Social Sciences, Department of Computer and Systems Sciences.
2018 (English)In: Applied Computing and Informatics, ISSN 1578-4487, E-ISSN 2210-8327, Vol. 14, no 1, p. 73-87Article in journal (Refereed) Published
Abstract [en]

The unprecedented prevalence of ubiquitous sensing will revolutionise the Future Internet where state-of-the-art Internet-of-Things (IoT) is believed to play the pivotal role. In the fast forwarding IoT paradigm, hundreds of billions of things are estimated to be deployed which would give rise to an enormous amount of data. Cloud computing has been the prevailing choice for controlling the connected things and the data, and providing intelligence based on the data. But response time and network load are on the higher side for cloud based solutions. Recently, edge computing is gaining growing attention to overcome this by employing rule-based intelligence. However, requirements of rules do not scale well with the proliferation of things. At the same time, rules fail in uncertain events and only offer pre-assumed intelligence. To counter this, this paper proposes a novel idea of leveraging the belief-network with the edge computing to utilize as an IoT edge-controller the aim of which is to offer low-level intelligence for IoT applications. This low-level intelligence along with cloud-based intelligence form the distributed intelligence in the IoT realm. Furthermore, a learning approach similar to reinforcement learning has been proposed. The approach, i.e. enabling a Future IoT Controller (FITC) has been verified with a simulated SmartHome scenario which proves the feasibility of the low-level intelligence in terms of reducing rules domination, faster response time and prediction through learning experiences at the edge.

Place, publisher, year, edition, pages
2018. Vol. 14, no 1, p. 73-87
Keywords [en]
Future Internet, Internet of Things, edge computing, distributed intelligence, belief-network
National Category
Information Systems
Research subject
Computer and Systems Sciences
Identifiers
URN: urn:nbn:se:su:diva-149262DOI: 10.1016/j.aci.2017.05.001OAI: oai:DiVA.org:su-149262DiVA, id: diva2:1159978
Available from: 2017-11-24 Created: 2017-11-24 Last updated: 2018-01-13Bibliographically approved
In thesis
1. Distributed Intelligence-Assisted Autonomic Context-Information Management: A context-based approach to handling vast amounts of heterogeneous IoT data
Open this publication in new window or tab >>Distributed Intelligence-Assisted Autonomic Context-Information Management: A context-based approach to handling vast amounts of heterogeneous IoT data
2018 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

As an implication of rapid growth in Internet-of-Things (IoT) data, current focus has shifted towards utilizing and analysing the data in order to make sense of the data. The aim of which is to make instantaneous, automated, and informed decisions that will drive the future IoT. This corresponds to extracting and applying knowledge from IoT data which brings both a substantial challenge and high value. Context plays an important role in reaping value from data, and is capable of countering the IoT data challenges. The management of heterogeneous contextualized data is infeasible and insufficient with the existing solutions which mandates new solutions. Research until now has mostly concentrated on providing cloud-based IoT solutions; among other issues, this promotes real-time and faster decision-making issues. In view of this, this dissertation undertakes a study of a context-based approach entitled Distributed intelligence-assisted Autonomic Context Information Management (DACIM), the purpose of which is to efficiently (i) utilize and (ii) analyse IoT data.

To address the challenges and solutions with respect to enabling DACIM, the dissertation starts with proposing a logical-clustering approach for proper IoT data utilization. The environment that the number of Things immerse changes rapidly and becomes dynamic. To this end, self-organization has been supported by proposing self-* algorithms that resulted in 10 organized Things per second and high accuracy rate for Things joining. IoT contextualized data further requires scalable dissemination which has been addressed by a Publish/Subscribe model, and it has been shown that high publication rate and faster subscription matching are realisable. The dissertation ends with the proposal of a new approach which assists distribution of intelligence with regard to analysing context information to alleviate intelligence of things. The approach allows to bring few of the application of knowledge from the cloud to the edge; where edge based solution has been facilitated with intelligence that enables faster responses and reduced dependency on the rules by leveraging artificial intelligence techniques. To infer knowledge for different IoT applications closer to the Things, a multi-modal reasoner has been proposed which demonstrates faster response. The evaluations of the designed and developed DACIM gives promising results, which are distributed over seven publications; from this, it can be concluded that it is feasible to realize a distributed intelligence-assisted context-based approach that contribute towards autonomic context information management in the ever-expanding IoT realm.

Place, publisher, year, edition, pages
Stockholm: Department of Computer and Systems Sciences, Stockholm University, 2018. p. 103
Series
Report Series / Department of Computer & Systems Sciences, ISSN 1101-8526 ; 18-001
Keywords
Internet of Things, Context information, Intelligence, Edge computing, Distributed computing
National Category
Computer Systems
Research subject
Computer and Systems Sciences
Identifiers
urn:nbn:se:su:diva-149513 (URN)978-91-7797-087-3 (ISBN)978-91-7797-088-0 (ISBN)
Public defence
2018-01-24, Lilla Hörsalen. Nod building, Borgarfjordsgatan 12, Kista, 13:00 (English)
Opponent
Supervisors
Note

At the time of the doctoral defense, the following paper was unpublished and had a status as follows: Paper 7: Submitted.

Available from: 2017-12-29 Created: 2017-12-04 Last updated: 2017-12-28Bibliographically approved

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