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A deep relation learning method for IoT interoperability enhancement within semantic formalization framework
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: Proceedings of the Second International Conference on Internet of things and Cloud Computing, Association for Computing Machinery (ACM), 2017, article id 149Conference paper, Published paper (Refereed)
Abstract [en]

Internet of Things (IoT) is facing with the interoperability issue due to the massive amount of heterogeneous entities (both physical and virtual entities) constantly generating heterogeneous data objects; semantic formalization has been widely recognized as a basis for the IoT interoperability, by which IoT can acquire the ability to comprehend data and further recognize the logic relations among heterogeneous IoT entities and heterogeneous data objects, thus to establish mutual understanding between each other to support with interoperability. Even semantic-driven track has emphasizes a lot on the logic relations in connection to the service rules and policies for interoperability, it is important that the quantity-driven relations should be also explored with adhering to the framework of semantic formalization. This paper explores a Deep Recursive Auto-encoders formed data relation learner in line with the semantic framework, which supports the data interoperability enhancement in a quantity-driven way based on the logic-driven framework. The learner starts with representing the virtual IoT entities via feature extraction; based on that, learner is trained in a manner of considering the surrounding relations of the targeted entity. As a baseline, a contrast learner with "regular" structure has been proposed which cannot functionally support semantic framework, even though the semantic formalization is indispensable; regardless the limitations in lab environment, the conducted experiments show that the proposed learner performs a bit better than the contrast learner under the same conditions. So that, the proposed method can synergistically enhances the interoperability within a semantic formalization framework.

Place, publisher, year, edition, pages
Association for Computing Machinery (ACM), 2017. article id 149
Keywords [en]
Internet of Things, Big data, Deep recursive neural network, Interoperability, Semantic formalization
National Category
Information Systems
Research subject
Computer and Systems Sciences
Identifiers
URN: urn:nbn:se:su:diva-151301DOI: 10.1145/3018896.3036392ISBN: 978-1-4503-4774-7 (electronic)OAI: oai:DiVA.org:su-151301DiVA, id: diva2:1172553
Conference
Second International Conference on Internet of things and Cloud Computing, Cambridge, United Kingdom, March 22 - 23, 2017
Available from: 2018-01-10 Created: 2018-01-10 Last updated: 2018-06-19Bibliographically approved
In thesis
1. Data-Centric Network of Things: A Method for Exploiting the Massive Amount of Heterogeneous Data of Internet of Things in Support of Services
Open this publication in new window or tab >>Data-Centric Network of Things: A Method for Exploiting the Massive Amount of Heterogeneous Data of Internet of Things in Support of Services
2017 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Internet of things (IoT) generates massive amount of heterogeneous data, which should be efficiently utilized to support services in different domains. Specifically, data need to be supplied to services by understanding the needs of services and by understanding the environment changes, so that necessary data can be provided efficiently but without overfeeding. However, it is still very difficult for IoT to fulfill such data supply with only the existing supports of communication, network, and infrastructure; while the most essential issues are still unaddressed, namely the heterogeneity issue, the recourse coordination issue, and the environments’ dynamicity issue. Thus, this necessitates to specifically study on those issues and to propose a method to utilize the massive amount of heterogeneous data to support services in different domains.

This dissertation presents a novel method, called the data-centric network of things (DNT), which handles heterogeneity, coordinates resources, and understands the changing IoT entity relations in dynamic environments to supply data in support of services. As results, various services based on IoT (e.g., smart cities, smart transport, smart healthcare, smart homes, etc.) are supported by receiving enough necessary data without overfeeding.

The contributions of the DNT to IoT and big data research are: firstly the DNT enables IoT to perceive data, resources, and the relations among IoT entities in dynamic environments. This perceptibility enhances IoT to handle the heterogeneity in different levels. Secondly, the DNT coordinates IoT edge resources to process and disseminate data based on the perceived results. This releases the big data pressure caused by centralized analytics to certain degrees. Thirdly, the DNT manages entity relations for data supply by handling the environment dynamicity. Finally, the DNT supply necessary data to satisfy different service needs, by avoiding either data-hungry or data-overfed status.

Place, publisher, year, edition, pages
Stockholm: Department of Computer and Systems Sciences, Stockholm University, 2017. p. 44
Series
Report Series / Department of Computer & Systems Sciences, ISSN 1101-8526 ; 17-006
Keywords
Internet of Things, Big Data, Artificial Intelligence, Data Supply, Distributed System
National Category
Computer Systems
Research subject
Computer and Systems Sciences
Identifiers
urn:nbn:se:su:diva-142342 (URN)978-91-7649-840-8 (ISBN)978-91-7649-841-5 (ISBN)
Public defence
2017-06-13, Lilla hörsalen, NOD-huset, Borgarfjordsgatan 12, Kista, 13:00 (English)
Opponent
Supervisors
Available from: 2017-05-19 Created: 2017-05-01 Last updated: 2018-06-19Bibliographically approved

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