Semantic Stream Management Framework for Data Consistency in Smart Spaces
Oluwaseun Bamgboye, Xiaodong Liu, Peter Cruickshank · 2019
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.