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Edge-based Interoperable Service-driven Information Distribution for Intelligent Pervasive Services
Stockholms universitet, Samhällsvetenskapliga fakulteten, Institutionen för data- och systemvetenskap.
Stockholms universitet, Samhällsvetenskapliga fakulteten, Institutionen för data- och systemvetenskap.
Stockholms universitet, Samhällsvetenskapliga fakulteten, Institutionen för data- och systemvetenskap.
Stockholms universitet, Samhällsvetenskapliga fakulteten, Institutionen för data- och systemvetenskap.
(Engelska)Ingår i: Pervasive and Mobile Computing, ISSN 1574-1192, E-ISSN 1873-1589Artikel i tidskrift (Refereegranskat) Submitted
Nationell ämneskategori
Systemvetenskap, informationssystem och informatik
Forskningsämne
data- och systemvetenskap
Identifikatorer
URN: urn:nbn:se:su:diva-142339OAI: oai:DiVA.org:su-142339DiVA: diva2:1092190
Tillgänglig från: 2017-05-01 Skapad: 2017-05-01 Senast uppdaterad: 2017-05-11Bibliografiskt granskad
Ingår i avhandling
1. Data-Centric Network of Things: A Method for Exploiting the Massive Amount of Heterogeneous Data of Internet of Things in Support of Services
Öppna denna publikation i ny flik eller fönster >>Data-Centric Network of Things: A Method for Exploiting the Massive Amount of Heterogeneous Data of Internet of Things in Support of Services
2017 (Engelska)Doktorsavhandling, sammanläggning (Övrigt vetenskapligt)
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.

Ort, förlag, år, upplaga, sidor
Stockholm: Department of Computer and Systems Sciences, Stockholm University, 2017. 44 s.
Serie
Report Series / Department of Computer & Systems Sciences, ISSN 1101-8526 ; 17-006
Nyckelord
Internet of Things, Big Data, Artificial Intelligence, Data Supply, Distributed System
Nationell ämneskategori
Datorsystem
Forskningsämne
data- och systemvetenskap
Identifikatorer
urn:nbn:se:su:diva-142342 (URN)978-91-7649-840-8 (ISBN)978-91-7649-841-5 (ISBN)
Disputation
2017-06-13, Lilla hörsalen, NOD-huset, Borgarfjordsgatan 12, Kista, 13:00 (Engelska)
Opponent
Handledare
Tillgänglig från: 2017-05-19 Skapad: 2017-05-01 Senast uppdaterad: 2017-05-22Bibliografiskt granskad

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Bin, XiaoRahim, RahmaniYuhong, LiKanter, Theo
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Pervasive and Mobile Computing
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