11th International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services

Research Article

ThingsNavi: Finding Most-Related Things via Multi-Dimensional Modeling of Human-Thing Interactions

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  • @INPROCEEDINGS{10.4108/icst.mobiquitous.2014.258007,
        author={Michael Sheng and Lina Yao and Nickolas Falkner and Anne Ngu},
        title={ThingsNavi: Finding Most-Related Things via Multi-Dimensional Modeling of Human-Thing Interactions},
        proceedings={11th International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services},
        publisher={ICST},
        proceedings_a={MOBIQUITOUS},
        year={2014},
        month={11},
        keywords={internet of things things discovery hypergraph ranking},
        doi={10.4108/icst.mobiquitous.2014.258007}
    }
    
  • Michael Sheng
    Lina Yao
    Nickolas Falkner
    Anne Ngu
    Year: 2014
    ThingsNavi: Finding Most-Related Things via Multi-Dimensional Modeling of Human-Thing Interactions
    MOBIQUITOUS
    ICST
    DOI: 10.4108/icst.mobiquitous.2014.258007
Michael Sheng1,*, Lina Yao1, Nickolas Falkner1, Anne Ngu2
  • 1: The University of Adelaide
  • 2: Texas State University
*Contact email: michael.sheng@adelaide.edu.au

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.