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Geolocation-Centric Information Platform for Resilient Spatio-temporal Content Management
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- TSUKAMOTO Kazuya
- Department of Computer Science and Electronics, Kyushu Institute of Technology (KIT)
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- TAMURA Hitomi
- Department of Information Electronics, Fukuoka Institute of Technology (FIT)
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- TAENAKA Yuzo
- Graduate School of Science and Technology, Nara Institute of Science and Technology (NAIST)
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- NOBAYASHI Daiki
- Department of Electrical Engineering and Electronics, Faculty of Engineering, KIT
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- YAMAMOTO Hiroshi
- Department of Information Science and Engineering, Ritsumeikan University
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- IKENAGA Takeshi
- Department of Electrical Engineering and Electronics, Faculty of Engineering, KIT
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- LEE Myung
- Department of Electrical and Computer Engineering, City University of New York (CCNY)
Bibliographic Information
- Published
- 2021-03-01
- Resource Type
- journal article
- DOI
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- 10.1587/transcom.2020nvi0003
- Publisher
- The Institute of Electronics, Information and Communication Engineers
Search this article
Description
<p>In IoT era, the growth of data variety is driven by cross-domain data fusion. In this paper, we advocate that “local production for local consumption (LPLC) paradigm” can be an innovative approach in cross-domain data fusion, and propose a new framework, geolocation-centric information platform (GCIP) that can produce and deliver diverse spatio-temporal content (STC). In the GCIP, (1) infrastructure-based geographic hierarchy edge network and (2) adhoc-based STC retention system are interplayed to provide both of geolocation-awareness and resiliency. Then, we discussed the concepts and the technical challenges of the GCIP. Finally, we implemented a proof-of-concepts of GCIP and demonstrated its efficacy through practical experiments on campus IPv6 network and simulation experiments.</p>
Journal
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- IEICE Transactions on Communications
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IEICE Transactions on Communications E104.B (3), 199-209, 2021-03-01
The Institute of Electronics, Information and Communication Engineers
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Details 詳細情報について
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- CRID
- 1390005822566363264
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- NII Article ID
- 130007993114
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- NII Book ID
- AA10826261
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- ISSN
- 17451345
- 09168516
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- HANDLE
- 10061/14707
- 10228/00008119
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- Text Lang
- en
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- Article Type
- journal article
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- Data Source
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- JaLC
- IRDB
- Crossref
- CiNii Articles
- KAKEN
- OpenAIRE
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- Abstract License Flag
- Disallowed
