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    Map as a Service: A Framework for Visualising and Maximising Information Return from Multi-Modal Wireless Sensor Networks

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    Authors
    Hammoudeh, Mohammad
    Newman, Robert
    Dennett, Christopher
    Mount, Sarah
    Aldabbas, Omar
    Issue Date
    2015-09-11
    
    Metadata
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    Abstract
    This paper presents a distributed information extraction and visualisation service, called the mapping service, for maximising information return from large-scale wireless sensor networks. Such a service would greatly simplify the production of higher-level, information-rich, representations suitable for informing other network services and the delivery of field information visualisations. The mapping service utilises a blend of inductive and deductive models to map sense data accurately using externally available knowledge. It utilises the special characteristics of the application domain to render visualisations in a map format that are a precise reflection of the concrete reality. This service is suitable for visualising an arbitrary number of sense modalities. It is capable of visualising from multiple independent types of the sense data to overcome the limitations of generating visualisations from a single type of sense modality. Furthermore, the mapping service responds dynamically to changes in the environmental conditions, which may affect the visualisation performance by continuously updating the application domain model in a distributed manner. Finally, a distributed self-adaptation function is proposed with the goal of saving more power and generating more accurate data visualisation. We conduct comprehensive experimentation to evaluate the performance of our mapping service and show that it achieves low communication overhead, produces maps of high fidelity, and further minimises the mapping predictive error dynamically through integrating the application domain model in the mapping service.
    Publisher
    MDPI
    Journal
    Sensors
    URI
    http://hdl.handle.net/2436/621534
    DOI
    10.3390/s150922970
    Additional Links
    http://www.mdpi.com/1424-8220/15/9/22970
    Type
    Journal article
    Language
    en
    ISSN
    1424-8220
    ae974a485f413a2113503eed53cd6c53
    10.3390/s150922970
    Scopus Count
    Collections
    Faculty of Science and Engineering

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