The University of Southampton
University of Southampton Institutional Repository

Interoperable & efficient: linked data for the internet of things

Interoperable & efficient: linked data for the internet of things
Interoperable & efficient: linked data for the internet of things
Two requirements to utilise the large source of time-series sensor data from the Internet of Things are interoperability and efficient access. We present a Linked Data solution that increases interoperability through the use and referencing of common identifiers and ontologies for integration. From our study of the shape of Internet of Things data, we show how we can improve access within the resource constraints of Lightweight Computers, compact machines deployed in close proximity to sensors, by storing time-series data succinctly as rows and producing Linked Data ‘just-in-time’. We examine our approach within two scenarios: a distributed meteorological analytics system and a smart home hub. We show with established benchmarks that in comparison to storing the data in a traditional Linked Data store, our approach provides gains in both storage efficiency and query performance from over 3 times to over three orders of magnitude on Lightweight Computers. Finally, we reflect how pushing computing to edge networks with our infrastructure can affect privacy, data ownership and data locality.
161-175
Siow, Eugene
01f33f70-e412-467c-aab2-5509d58d1b94
Tiropanis, Thanassis
d06654bd-5513-407b-9acd-6f9b9c5009d8
Hall, Wendy
11f7f8db-854c-4481-b1ae-721a51d8790c
Siow, Eugene
01f33f70-e412-467c-aab2-5509d58d1b94
Tiropanis, Thanassis
d06654bd-5513-407b-9acd-6f9b9c5009d8
Hall, Wendy
11f7f8db-854c-4481-b1ae-721a51d8790c

Siow, Eugene, Tiropanis, Thanassis and Hall, Wendy (2016) Interoperable & efficient: linked data for the internet of things. INSCI 2016, 3rd International conference on Internet Science, Florence, Italy. 12 - 14 Sep 2016. pp. 161-175 . (doi:10.1007/978-3-319-45982-0_15).

Record type: Conference or Workshop Item (Paper)

Abstract

Two requirements to utilise the large source of time-series sensor data from the Internet of Things are interoperability and efficient access. We present a Linked Data solution that increases interoperability through the use and referencing of common identifiers and ontologies for integration. From our study of the shape of Internet of Things data, we show how we can improve access within the resource constraints of Lightweight Computers, compact machines deployed in close proximity to sensors, by storing time-series data succinctly as rows and producing Linked Data ‘just-in-time’. We examine our approach within two scenarios: a distributed meteorological analytics system and a smart home hub. We show with established benchmarks that in comparison to storing the data in a traditional Linked Data store, our approach provides gains in both storage efficiency and query performance from over 3 times to over three orders of magnitude on Lightweight Computers. Finally, we reflect how pushing computing to edge networks with our infrastructure can affect privacy, data ownership and data locality.

Text
interoperable&efficient.pdf - Accepted Manuscript
Download (859kB)

More information

Accepted/In Press date: May 2016
e-pub ahead of print date: 13 September 2016
Venue - Dates: INSCI 2016, 3rd International conference on Internet Science, Florence, Italy, 2016-09-12 - 2016-09-14
Organisations: Web & Internet Science

Identifiers

Local EPrints ID: 397474
URI: http://eprints.soton.ac.uk/id/eprint/397474
PURE UUID: 27579bce-5e51-4101-a68c-897409c466e8
ORCID for Eugene Siow: ORCID iD orcid.org/0000-0002-3593-2436
ORCID for Thanassis Tiropanis: ORCID iD orcid.org/0000-0002-6195-2852
ORCID for Wendy Hall: ORCID iD orcid.org/0000-0003-4327-7811

Catalogue record

Date deposited: 01 Jul 2016 13:29
Last modified: 15 Mar 2024 03:31

Export record

Altmetrics

Contributors

Author: Eugene Siow ORCID iD
Author: Thanassis Tiropanis ORCID iD
Author: Wendy Hall ORCID iD

Download statistics

Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.

View more statistics

Atom RSS 1.0 RSS 2.0

Contact ePrints Soton: eprints@soton.ac.uk

ePrints Soton supports OAI 2.0 with a base URL of http://eprints.soton.ac.uk/cgi/oai2

This repository has been built using EPrints software, developed at the University of Southampton, but available to everyone to use.

We use cookies to ensure that we give you the best experience on our website. If you continue without changing your settings, we will assume that you are happy to receive cookies on the University of Southampton website.

×