The Industry 4.0 and smart city solutions are impossible to be implemented without using IoT devices. There can be several problems in acquiring data from these IoT devices, problems that can lead to missing values. Without a complete set of data, the automation of processes is not possible or is not satisfying enough. The aim of this paper is to introduce a new algorithm that can be used to fill in the missing values of signals sent by IoT devices. In order to do that, we introduce Shepard local approximation operators in Riesz MV-algebras for one variable function and we structure the set of possible values of the IoT devices signals as Riesz MV-algebra. Based on these local approximation operators we define a new algorithm and we test it to prove that it can be used to fill in the missing values of signals sent by IoT devices.

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In this article we continue the study started in [8] to use Riesz MValgebras for IoT devices signals processing. The Shepard local approximation operators introduced in [8] were defines such that to approximate single variable functions. In real industrial usage, the signals coming from IoT devices may be influenced by mode than a parameter, and thus we introduce generalized Shepard local approximation operators that can approximate multi-dimensional functions and some numerical experiments are considered.

In this paper we introduce the concepts of vectorial MV-algebra, vectorial complete MV-algebra, modulus of continuity and continuous function with values in a vectorial complete MV-algebra. The main result of this paper is a Weierstrass type approximation theorem for functions taking values on vectorial complete MV-algebras. As examples, we construct such a structure on the RGB model (this represents the colours model of a pixel on the screen) and on the space of fuzzy subsets of a set.

The main objective of this paper is to investigate how a failure in the functioning of a normal electrical system represented by a three-phase asynchronous motor will modify the voltages and currents present in the system and if it is possible to design a system that is able to automatically detect the fault, based on the use of modern data acquisition system and powerful computer processing capabilities. The detection of faulty signals is realised using Feedforward Artificial Neural Networks.