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Dong, Yuji
Publications (2 of 2) Show all publications
Dong, Y., Wan, K. & Yue, Y. (2020). A Semantic-Based Belief Network Construction Approach in IoT. Sensors, 20(20), Article ID E5747.
Open this publication in new window or tab >>A Semantic-Based Belief Network Construction Approach in IoT
2020 (English)In: Sensors, E-ISSN 1424-8220, Vol. 20, no 20, article id E5747Article in journal (Refereed) Published
Abstract [en]

Uncertainty is intrinsic in most of the complex systems, especially when the systems have to interact with the physical environment; therefore, handling uncertainty is critical in the Internet of Things (IoT). In this paper, we propose a semantic-based approach to build the belief network in IoT systems to handle the uncertainties. Semantics is the functionality description of any system component. Semantic Match mechanisms can construct the appropriate structures to compare the consistency between different sources of data based on the same functionality. In the approach, we define the belief property of every system component and develop the related algorithms to update the belief value. Furthermore, the related mechanisms and algorithms for data fusion and fault detection based on the belief property are described to explain how the approach works in the IoT systems. Several simulation experiments are used to evaluate the proposed approach, and the results indicate that the approach can work as expected. More accurate data are fused from the inaccurate devices and the fault in one node is automatically detected.

Place, publisher, year, edition, pages
MDPI, 2020
belief, data fusion, fault detection, internet of things, self adaptation, uncertainty
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
urn:nbn:se:mau:diva-18781 (URN)10.3390/s20205747 (DOI)000585564800001 ()33050402 (PubMedID)2-s2.0-85092439234 (Scopus ID)
Available from: 2020-10-23 Created: 2020-10-23 Last updated: 2024-02-05Bibliographically approved
Vogel, B., Dong, Y., Emruli, B., Davidsson, P. & Spalazzese, R. (2020). What is an Open IoT Platform?: Insights from a Systematic Mapping Study. Future Internet, 12(4)
Open this publication in new window or tab >>What is an Open IoT Platform?: Insights from a Systematic Mapping Study
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2020 (English)In: Future Internet, E-ISSN 1999-5903, Vol. 12, no 4Article in journal (Refereed) Published
Abstract [en]

Today, the Internet of Things (IoT) is mainly associated with vertically integrated systems that often are closed and fragmented in their applicability. To build a better IoT ecosystem, the open IoT platform has become a popular term in the recent years. However, this term is usually used in an intuitive way without clarifying the openness aspects of the platforms. The goal of this paper is to characterize the openness types of IoT platforms and investigate what makes them open. We conducted a systematic mapping study by retrieving data from 718 papers. As a result of applying the inclusion and exclusion criteria, 221 papers were selected for review. We discovered 46 IoT platforms that have been characterized as open, whereas 25 platforms are referred as open by some studies rather than the platforms themselves. We found that the most widely accepted and used open IoT platforms are NodeMCU and ThingSpeak that together hold a share of more than 70% of the declared open IoT platforms in the selected papers. The openness of an IoT platform is interpreted into different openness types. Our study results show that the most common openness type encountered in open IoT platforms is open-source, but also open standards, open APIs, open data and open layers are used in the literature. Finally, we propose a new perspective on how to define openness in the context of IoT platforms by providing several insights from the different stakeholder viewpoints.

Place, publisher, year, edition, pages
Basel, Switzerland: MDPI, 2020
internet of things, IoT, open IoT platforms, openness, open-source, open standards, open API, systematic mapping study
National Category
Computer Sciences Software Engineering
urn:nbn:se:mau:diva-17332 (URN)10.3390/fi12040073 (DOI)000533885000007 ()2-s2.0-85084682502 (Scopus ID)
Available from: 2020-05-18 Created: 2020-05-18 Last updated: 2024-04-04Bibliographically approved

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