• Fog computing
• Internet of Things
• lightweight monitoring
• peer-to-peer architectures

Infrastructure monitoring will play a crucial enabling role in the orchestration of next-gen Cloud-IoT applications, e.g. in Fog computing. Particularly, monitoring of Fog infrastructures should deal with platform heterogeneity, scarce resource availability at the edge, and high dynamicity all along the Cloud-IoT continuum. This thesis aims at testing, assessing and improving the FogMon monitoring service, a C++ distributed monitoring prototype targeting Fog computing infrastructures that can measure hardware resources at different Fog nodes, end-to-end network QoS between such nodes, and connected IoT devices. To this end, FogMon is thoroughly assessed over federated Cloud-IoT resources against different types of node and link failures within the scope of the "Lightweight Self-adaptive Cloud-IoT Monitoring across Fed4FIRE+ Testbeds" (LiSCIo) experiment, funded by the EU Horizon 2020 project Fed4Fire+. A new improved version of the service, FogMon2.0, is released featuring better monitoring accuracy, greater resiliency to infrastructure failures and self-adaptivity, and a centralized web interface to access experimental data.