The study provides initial performance evaluations of the 5G network, considering the definition of appropriate “slices” that meet the requirements of the energy sector. This was achieved through a processing system called the “5G Planning Tool,” developed by FUB in the NS-3 environment, in which the network architecture was divided into multiple levels: the service level, for defining the functions required by the network according to the desired Key Performance Indicators (KPIs) for the specific energy application; the control level, for matching the application’s requirements with the orchestration of physical resources; and the physical level, for identifying all the resources needed for service implementation, based on both high-capacity radio connections (above 6 GHz) and wide coverage (below 6 GHz).

Analyses were conducted on the service capacity of 5G connectivity in terms of maximum throughput and maximum latency for typical energy sector scenarios corresponding to the three different communication types: mMTC, eMBB, and uRLLC. The results of these analyses were then used to verify traffic management policies based on Software Defined Network (SDN). Specifically, the SDN approach was studied to efficiently manage medium access (i.e., resource optimization) in low data traffic scenarios, or for the timely isolation of an important flow (i.e., ensuring QoS) in high data traffic situations. The performance evaluations showed a clear improvement over current LTE technology, indicating that it can meet the connectivity (mMTC), capacity (eMBB), and latency (uRLLC) requirements needed to deliver various innovative energy services.

Finally, initial evaluations of IoT search engines, with a focus on the energy sector, are reported, analyzing relevant case studies. Both proprietary data and public datasets specific to the energy sector were considered to populate the database for an IoT search engine representative of energy applications. The initial indications for the development of the IoT search engine propose a model based on metadata management and processing, using a hybrid approach of Information Retrieval and Machine Learning to extract the correct information from the heterogeneous data typical of IoT.