• 5g RAN Slicing
• Concurrent Transmissions
• LoRaWAN
• Massive alarms
• Optimization
• Resource Allocation

This thesis addresses resource allocation strategies in two crucial technological domains, with a primary focus on enhancing smart transportation through 5th Generation (5G) technology and ensuring safety in factories using LoRaWAN. The initial contribution revolves around the design of a dedicated 5G Radio Access Network (RAN) slice tailored for the Autonomous Tram (AT) use case in smart transportation. Collaborating with Thales Italia, Florence, the research aims to deliver a service to tram lines in Florence. Numerical simulations were performed leveraging a customized version of a 5G-air-simulator, particularly the uplink segment, establishing an optimal BW required for the Vehicle to Infrastructure (V2I) communication
infrastructure.

The subsequent phase delves into the Industrial Internet of Things (IIoT) context, specifically addressing Factories at Major Accident Risk (FMAR). Focusing on LoRa concurrent transmissions, the research is conducted through experimental analyses on the performance of LoRa synchronous transmission, with an ultimate goal of designing a reliable and low-latency LoRaWAN solution exploiting the downlink features of
LoRaWAN standard for End Device coordination.

The final contribution centers on the development of an optimization framework for time-slotted LoRaWAN transmission within the FMAR scenario. This involves a robust theoretical analysis, validated through extensive simulations. The primary focus is on optimizing slot probabilities for End Devices using slotted ALOHA. The proposed scheme efficiently manages the influx of massive alarms within IoT systems, enhancing overall communication reliability and effectiveness.

This comprehensive work contributes significantly to advancing resource allocation strategies in both smart transportation and industrial safety applications.