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Search Techniques for the Web of Things: a Taxonomy and Survey

Zhou, Y, De, S, Wang, W and Moessner, K (2016) Search Techniques for the Web of Things: a Taxonomy and Survey Sensors, 16 (5).

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Abstract

The Web of Things aims to make physical world objects and their data accessible through standard Web technologies to enable intelligent applications and sophisticated data analytics. Due to the amount and heterogeneity of the data, it is challenging to perform data analysis directly; especially when the data is captured from a large number of distributed sources. However, the size and scope of the data can be reduced and narrowed down with search techniques, so that only the most relevant and useful data items are selected according to the application requirements. Search is fundamental to the Web of Things while challenging by nature in this context, e.g., mobility of the objects, opportunistic presence and sensing, continuous data streams with changing spatial and temporal properties, efficient indexing for historical and real time data. The research community has developed numerous techniques and methods to tackle these problems as reported by a large body of literature in the last few years. A comprehensive investigation of the current and past studies is necessary to gain a clear view of the research landscape and to identify promising future directions. This survey reviews the state-of-the-art search methods for the Web of Things, which are classified according to three different viewpoints: basic principles, data/knowledge representation, and contents being searched. Experiences and lessons learned from the existing work and some EU research projects related to Web of Things are discussed, and an outlook to the future research is presented.

Item Type: Article
Subjects : subj_Electronic_Engineering
Divisions : Faculty of Engineering and Physical Sciences > Electronic Engineering
Authors :
AuthorsEmailORCID
Zhou, YUNSPECIFIEDUNSPECIFIED
De, SUNSPECIFIEDUNSPECIFIED
Wang, WUNSPECIFIEDUNSPECIFIED
Moessner, KUNSPECIFIEDUNSPECIFIED
Date : 27 April 2016
Identification Number : 10.3390/s16050600
Copyright Disclaimer : © 2016 by the authors; licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons by Attribution (CC-BY) license (http://creativecommons.org/licenses/by/4.0/).
Depositing User : Symplectic Elements
Date Deposited : 26 Apr 2016 11:18
Last Modified : 28 Apr 2016 11:04
URI: http://epubs.surrey.ac.uk/id/eprint/810528

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