Flávio Henry Ferreira

This research investigates Cyber-Physical Systems (CPS) and Collective Decision-Making (CDM) methods to optimize resource, data, and energy allocation in Integrated Networks for Intelligent Work Machines (IWMs) in remote areas. Unmanned Aerial Vehicles (UAVs), pivotal in the Internet of Flying Things (IoFT), are increasingly integrated with B5G and 6G technologies to enhance network coverage and meet growing user equipment (UE) demands, especially during emergencies. While existing studies address UAV deployment in 5G and B5G, gaps remain in CPS-based resource management for UAV networks. This proposal explores UAVs as CPS agents to balance global network efficiency with user-specific demands, addressing key challenges: organizing decentralized UAV systems for extreme coverage, developing Space-Air-Ground Integrated Networks (SAGINs) for IWMs, and comparing CPS-based resource allocation with traditional methods. Hypotheses suggest CDM will enable UAVs to prioritize high-demand users while ensuring network efficiency. Despite the complexity of optimizing UAVs in heterogeneous networks, this study anticipates achieving satisfactory results with low computational costs in decentralized systems. By leveraging the cooperative nature of CPS and CDM, the research aims to deliver superior user-centric outcomes and insights for network planners, building on prior studies in Multi-UAV Sensor Networks and 5G Emergency Planning to propose CPS-based SAGINs for next-generation applications.