Due to the increasing number of IoT devices, the amount of data gathered nowadays is rather large and continuously growing. The availability of new sensors presented in IoT devices and open data platforms provides new possibilities for innovative applications and use-cases. However, the dependence on data for the provision of services creates the necessity of assuring the quality of data to ensure the viability of the services. In order to support the evaluation of the valuable information, this chapter shows the development of a series of metrics that have been defined as indicators of the quality of data in a quantifiable, fast, reliable, and human-understandable way. The metrics are based on sound statistical indicators. Statistical analysis, machine learning algorithms, and contextual information are some of the methods to create quality indicators. The developed framework is also suitable for deciding between different datasets that hold similar information, since until now with no way of rapidly discovering which one is best in terms of quality had been developed. These metrics have been applied to real scenarios which have been smart parking and environmental sensing for smart buildings, and in both cases, the methods have been representative for the quality of the data.
Part of the book: Data Integrity and Quality
This chapter studies the fluid flow within pipes subjected to thermal asymmetrical boundary conditions. The phenomenon at hand takes place in many real-world industrial situations, such as solar thermal devices, aerial pipelines. A steady-state analysis of laminar forced-convection heat transfer for an incompressible Newtonian fluid is studied. The fluid is considered to flow through a straight round pipe provided with straight fins. For the case studied, axial heat conduction in the fluid has been considered and the effects of the forced convection have been considered to be dominant. A known uniform temperature field is applied at the upper external surface of the assembly. The 3D assembly has been created combining cylindrical and Cartesian coordinates. The governing differential equation system is solved numerically through suitable discretization in a set of different finite volume elements. The results are shown through the thermal profiles in respect of longitudinal and radial-azimuthal coordinates and the problem characteristic length. To facilitate the resolution of this phenomenon, an open computing platform called HEATT©, based on this model, has been developed, and it is also shown here. The platform is now being built and is expected to be freely available at the end of year 2022.
Part of the book: Pipeline Engineering