Big data is one of the enabling technologies of the vision of Industry 4.0. Technological evolution is able to generate an increasing number of data. From the web, to social networks, from mobile devices to sensors, data is conveyed through the most disparate products of technology, whether physical or virtual. However, the transition from big data to smart data providing insights about information and issues that matter is not simple and obvious to achieve. The greater the amount of data and the more heterogeneous they are, the more complex their processing will be. The data, once collected, is processed by complex analytics algorithms and, to do this, considerable storage units and computing power are needed.In this paper, we describe our approach and experience at the Italian national agency ENEA in architecting a big data-driven software architecture for public street lighting. Such a software architecture is called ENEA PELL smart city platform (in brief, PELL SCP) and it is intended to collect, represent, control, predict, and possibly optimize the behaviour of public street lighting plants. In particular, we provide an overview of the analytics features that are being developed in collaboration with the University of Bergamo (Italy) to analyze electric energy data as collected by the PELL SCP.
Architecting a big data-driven software architecture for smart street lighting
Moretti F.;Blaso L.
2023-01-01
Abstract
Big data is one of the enabling technologies of the vision of Industry 4.0. Technological evolution is able to generate an increasing number of data. From the web, to social networks, from mobile devices to sensors, data is conveyed through the most disparate products of technology, whether physical or virtual. However, the transition from big data to smart data providing insights about information and issues that matter is not simple and obvious to achieve. The greater the amount of data and the more heterogeneous they are, the more complex their processing will be. The data, once collected, is processed by complex analytics algorithms and, to do this, considerable storage units and computing power are needed.In this paper, we describe our approach and experience at the Italian national agency ENEA in architecting a big data-driven software architecture for public street lighting. Such a software architecture is called ENEA PELL smart city platform (in brief, PELL SCP) and it is intended to collect, represent, control, predict, and possibly optimize the behaviour of public street lighting plants. In particular, we provide an overview of the analytics features that are being developed in collaboration with the University of Bergamo (Italy) to analyze electric energy data as collected by the PELL SCP.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
