The assessments presented in the document aim to provide guidance on the use of 5G technology and advanced data recovery and analysis techniques from IoT devices in the energy sector. Specifically, connectivity and data traffic management aspects were investigated through connectivity analysis in different application scenarios where an energy system can be deployed and the achievable performance can be verified using the Network Slicing approach, depending on the type and quantity of data traffic to manage. These analyses were conducted by developing an emulative-simulation platform to study the complete behaviour of a TLC network described in the main levels of the OSI (Open Systems Interconnection) stack, in terms of BER, throughput, packet loss, latency and jitter. In addition to the processing system for describing the radio-backhaul segment, developed with the ns3 software, a network emulator was created using the mininet software, for making assessments of for assessing the edge-core segment, with particular reference to fog-type architectures. The issue of retrieving and managing data associated with or produced by energy-system-related IoT was also addressed, using the IoTSE (Internet of Things Search Engine) paradigm. Two activities were carried out in this context. The first is an experimental study on the correlation between energy data and data from other contexts, using databases published in open format such as the TIM Big Data Challenge.

The second activity is the retrieval and analysis of banners associated with devices connected to the Internet and with unprotected access, which can be used mainly to identify and assess vulnerabilities. Using Shodan as an IoT crawler, which provides the raw data, a Big Data type platform was set up for the acquisition, indexing and analysis of the information found and some preliminary processing aimed at ICS (Industrial Control Systems) devices was carried out, as these devices are likely to be of greater interest for the energy sector.