Please use this identifier to cite or link to this item: http://hdl.handle.net/2440/108664
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Type: Conference paper
Title: ThingsNavi: finding most-related things via multi-dimensional modeling of human-thing interactions
Author: Yao, L.
Sheng, Q.
Falkner, N.
Ngu, A.
Citation: Proceedings of the 11th International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services, 2014 / pp.20-29
Publisher: ICST
Publisher Place: online
Issue Date: 2014
ISBN: 9781631900396
Conference Name: 11th International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services (MobiQuitous) (02 Dec 2014 - 05 Dec 2014 : London, UK)
Statement of
Responsibility: 
Lina Yao, Quan Z. Sheng, Nickolas Falkner, Anne Ngu
Abstract: With the fast emerging Internet of Things (IoT), effectively and efficiently searching and selecting the most related things of a user’s interest is becoming a crucial challenge. In the IoT era, human interactions with things are taking place at a new level in ubiquitous computing. These interactions initiated by humans are not completely random and carry rich contextual information. In this paper, we propose a things searching approach based on a hypergraph, called ThingsNavi, where given a target thing, other related things can be found by fully exploiting human-thing interactions in terms of multi-dimensional, contextual information (e.g., spatial information, temporal information, user identity). In particular, we construct a unified hypergraph to represent the rich structural and contextual information in human-thing interactions. We formulate the correlated things search as a ranking problem on top of this hypergraph, in which the information of target things can be propagated through the structure of the hypergraph. We evaluate our approach by using real-world datasets and the experimental results demonstrate its effectiveness.
Keywords: Internet of Things, things discovery; hypergraph; ranking
Rights: Copyright © 2014
RMID: 0030025174
DOI: 10.4108/icst.mobiquitous.2014.258007
Published version: https://dl.acm.org/citation.cfm?id=2692987
Appears in Collections:Computer Science publications

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