Research Output

Semantic Stream Management Framework for Data Consistency in Smart Spaces

  Semantic technology can provide a bridge between
smart applications and Internet of Things (IoT) to enable
possible integration and interoperability of data produced by
heterogeneous devices. In IoT, data quality plays an important
role when it comes to interfacing sensor readings with real-time
applications at the basic atomic level. Popular techniques of machine
learning and point-based calibrations are inadequate due
to inability to perform semantic reasoning and interoperability
on sensor streams even in real time. In this paper, a layered
software framework based on semantic technologies is developed
to maintain the consistency of data streams produced by physical
sensors that interprets measurements as numeric values. The
framework shows how semantic modelling and reasoning can
be applied to validate the consistency of data streams while
placing emphasis on the temporal characteristics of the stream.
The evaluation of the approach involves analysing the effects of
different Resource Description Format(RDF) data serializations
on the response times of the reasoning engine and throughput
of continuous semantic stream query execution. The outcome
of experiments indicates the semantic framework as a promising
approach for stream validation in Smart Spaces and other related
IoT domains.


Bamgboye, O., Liu, X., & Cruickshank, P. (2019). Semantic Stream Management Framework for Data Consistency in Smart Spaces.



Ontology, C-SPARQL, Latency, Throughput, Sensor, Data Stream, Smart Space

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