Hoseinitabatabaei, SA;
Fathy, Y;
Barnaghi, P;
Wang, C;
Tafazolli, R;
(2018)
A Novel Indexing Method for Scalable IoT Source Lookup.
IEEE Internet of Things Journal
, 5
(3)
pp. 2037-2054.
10.1109/JIOT.2018.2821264.
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Abstract
When dealing with a large number of devices, the existing indexing solutions for the discovery of IoT sources often fall short to provide an adequate scalability. This is due to the high computational complexity and communication overhead that is required to create and maintain the indices of the IoT sources particularly when their attributes are dynamic. This paper presents a novel approach for indexing distributed IoT sources and paves the way to design a data discovery service to search and gain access to their data. The proposed method creates concise references to IoT sources by using Gaussian Mixture Models (GMM). Furthermore, a summary update mechanism is introduced to tackle the change of sources availability and mitigate the overhead of updating the indices frequently. The proposed approach is benchmarked against a standard centralized indexing and discovery solution. The results show that the proposed solution reduces the communication overhead required for indexing by three orders of magnitude while depending on IoT network architecture it may slightly increase the discovery time.
| Type: | Article |
|---|---|
| Title: | A Novel Indexing Method for Scalable IoT Source Lookup |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1109/JIOT.2018.2821264 |
| Publisher version: | http://doi.org/10.1109/JIOT.2018.2821264 |
| Language: | English |
| Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
| Keywords: | Internet of Things, Source Indexing, Probabilistic Model, Gaussian Mixture Model, Distributed Discovery |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10057069 |
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