**5. Conclusions**

The proliferation of datasets thanks to the new paradigm of the Internet of Things, is populating repositories and open data platforms with data that could be of great use for the scientific community and the technologists to catalyze the growth of scientific knowledge and to make proliferate the creation of new technological solutions. Although all data has value, a point has been reached in which it is necessary to rapidly recognize the quality of a dataset, or a data stream, ideally on an only manner.

In this chapter, several concepts have been combined in order to measure the quality of data from IoT-based real-time streams (tested on real-world) sensor systems.

Three sets of quality assurance methods, descriptive, analytic and geometrical have been developed that can be used as levels of a given evaluation, or independently depending on the nature of the datasets to be evaluated.

It has been shown that the metrics can be an standard on the calculation of data quality and the majority can be applied independently on the problem context. At the same time, basic concepts that must be present in any system in which the quality of the data is to be guaranteed have been reviewed. Furthermore, it has been shown how it is possible to obtain quality metrics when knowledge about the data is limited.

The applications of this technology are linked to the proliferation of open data portals. There exist many initiatives and organizations that are working towards publishing data as open. The main funding body for engineering and physical sciences research in the UK, the Engineering and Physical Sciences Research Council (EPSRC) is supporting the management and provision of access to research data. They claim that *publicly funded research data should generally be made as widely and*

*Quality of Information within Internet of Things Data DOI: http://dx.doi.org/10.5772/intechopen.95844*

*freely available as possible in a timely and responsible manner*<sup>2</sup> . Other initiatives are the EU Open Data Portal<sup>3</sup> at European level or the national-level ones such as Open Data Aarhus<sup>4</sup> . In that sense, the selection of data sources becomes more complicated given the great amount of data that researchers and practitioners have access to. Our system provides an easy, understandable and quick way to make an informed decision for choosing between several data sources based on data quality.

As future work, we are considering several technologies in order to make our metrics available to researchers and businesses. We consider that they have the potential to become a standard for measuring data quality.
