Change search
CiteExportLink to record
Permanent link

Direct link
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Multi-Modal Context-Aware reasoNer (CAN) at the Edge of IoT
Stockholm University, Faculty of Social Sciences, Department of Computer and Systems Sciences.
Stockholm University, Faculty of Social Sciences, Department of Computer and Systems Sciences.
Stockholm University, Faculty of Social Sciences, Department of Computer and Systems Sciences.
2017 (English)In: Procedia Computer Science, ISSN 1877-0509, E-ISSN 1877-0509, Vol. 109, p. 335-342Article in journal (Refereed) Published
Abstract [en]

Future Internet is expected to be driven by prevalence of the Internet of Things (IoT). This prevalence of IoT promises to impact every aspect of human life in the foreseeable future where computing paradigm would witness huge influx of IoT data. Context is gaining growing attention to make sense of the data and it is envisaged that context-aware computing would act as an indispensable enabler for IoT. Contextualizing the collected IoT data enables to reap value from the data and to harvest the knowledge. Reasoning the contextualized data, that is, context information is imperative to the vision of harvesting knowledge. Edge computing is also expected to play a vital role in IoT to reduce dependency on cloud based solution, to achieve faster response, and to provide intelligence closer to the IoT things. The combination of context-awareness and edge solution would be inseparable in the future IoT. Furthermore, IoT vision comprises of different IoT applications controlled by a capable controller at the edge, an edge controller necessitates to counter the challenge of providing knowledge for each of the IoT applications. Therefore, such a controller requires to offer different context-aware reasoning to alleviate the intelligence-of-things. In view of this, this paper proposes a multi-modal context-aware reasoner the aim of which is to provide knowledge at the edge for each IoT application. The context-aware reasoning has been verified with rules-based and Bayesian reasoning for three IoT applications and initial results suggest that it is promising to realize such multimodal reasoning at the edge with low latency.

Place, publisher, year, edition, pages
2017. Vol. 109, p. 335-342
Keywords [en]
Internet of Things (IoT), context-aware, edge computing, multimodal, reasoning
National Category
Information Systems
Research subject
Computer and Systems Sciences
URN: urn:nbn:se:su:diva-144910DOI: 10.1016/j.procs.2017.05.360OAI:, id: diva2:1117557
The 8th International Conference on Ambient Systems, Networks and Technologies (ANT-2017), Madeira, Portugal, May 16-19, 2017
Available from: 2017-06-29 Created: 2017-06-29 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
Report Series / Department of Computer & Systems Sciences, ISSN 1101-8526 ; 18-001
Internet of Things, Context information, Intelligence, Edge computing, Distributed computing
National Category
Computer Systems
Research subject
Computer and Systems Sciences
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)

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

Open Access in DiVA

No full text in DiVA

Other links

Publisher's full text

Search in DiVA

By author/editor
Rahman, HasiburRahmani, RahimKanter, Theo
By organisation
Department of Computer and Systems Sciences
In the same journal
Procedia Computer Science
Information Systems

Search outside of DiVA

GoogleGoogle Scholar


Altmetric score

Total: 76 hits
CiteExportLink to record
Permanent link