IoT-Lite: A Lightweight Semantic Model for the Internet of Things and its Use with Dynamic Semantics

Bermudez-Edo, M, Elsaleh, Tarek, Barnaghi, Payam and Taylor, K (2017) IoT-Lite: A Lightweight Semantic Model for the Internet of Things and its Use with Dynamic Semantics Personal and Ubiquitous Computing.

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Abstract

Over the past few years the semantics community has developed several ontologies to describe concepts and relationships for Internet of Things (IoT) applications. A key problem is that most of the IoT related semantic descriptions are not as widely adopted as expected. One of the main concerns of users and developers is that semantic techniques increase the complexity and processing time and therefore they are unsuitable for dynamic and responsive environments such as the IoT. To address this concern, we propose IoT-Lite, an instantiation of the semantic sensor network (SSN) ontology to describe key IoT concepts allowing interoperability and discovery of sensory data in heterogeneous IoT platforms by a lightweight semantics. We propose 10 rules for good and scalable semantic model design and follow them to create IoT-Lite. We also demonstrate the scalability of IoT-Lite by providing some experimental analysis, and assess IoT-Lite against another solution in terms of round trip time (RTT) performance for query-response times. We have linked IoTLite with Stream Annotation Ontology (SAO), to allow queries over stream data annotations and we have also added dynamic semantics in the form of MathML annotations to IoT-Lite. Dynamic semantics allows the annotation of spatio-temporal values, reducing storage requirements and therefore the response time for queries. Dynamic semantics stores mathematical formulas to recover estimated values when actual values are missing.

Item Type:	Article
Subjects :	Electronic Engineering
Divisions :	Faculty of Engineering and Physical Sciences > Electronic Engineering
Authors :	Name Email ORCID Bermudez-Edo, M UNSPECIFIED UNSPECIFIED Elsaleh, Tarek T.Elsaleh@surrey.ac.uk UNSPECIFIED Barnaghi, Payam P.Barnaghi@surrey.ac.uk UNSPECIFIED Taylor, K UNSPECIFIED UNSPECIFIED
Date :	22 February 2017
Identification Number :	10.1007/s00779-017-1010-8
Copyright Disclaimer :	The final publication is available at Springer via http://link.springer.com/article/10.1007/s00779-017-1010-8
Uncontrolled Keywords :	Internet of Things · Semantics · Linked Sensor Data · Knowledge Management
Related URLs :	Publisher
Depositing User :	Symplectic Elements
Date Deposited :	04 Jan 2017 14:05
Last Modified :	12 Jul 2017 10:22
URI:	http://epubs.surrey.ac.uk/id/eprint/813196

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