M. Tropea

An Energy-Aware Framework for Drone Charging Management in FANETs

Unmanned Aerial Vehicles (UAVs), also known as drones, have rapidly gained interest across a wide range of applications due to their versatility, mobility, and ability to operate in inaccessible or hazardous environments. Their adoption has expanded across a wide variety of application domains, including environmental...

An IoT-Enabled Early Warning Platform for Urban Flood-Risk Monitoring and Mitigation: The IN-FLOOD Case Study

Urban drainage networks are increasingly exposed to critical operating conditions because urbanization limits rainfall infiltration while climate change intensifies extreme precipitation events. This paper presents an IoT-enabled early warning platform developed within the IN-FLOOD project and deployed in the Municipal...

Evaluating SDN Controller Failover with Direct Replication and Shared Data Store Architectures

Software-Defined Networking (SDN) relies on software controllers to program data-plane forwarding, so controller availability is critical: the controller is a single point of failure where High Availability (HA) state synchronization trades recovery speed against synchronization overhead. This paper empirically evaluat...

An in-depth analysis of QKD channel and secret key rate in free space laser optics for next generation secure networks

3D Deep Reinforcement Learning Based UAV Trajectory Planning in Dynamic and Partially Observable Environments

Trajectory planning for unmanned aerial vehicles (UAVs) in dynamic and partially observable environments becomes more complex when extended from two-dimensional to three-dimensional navigation. Although Deep Reinforcement Learning (DRL) methods have shown strong performance in 2D scenarios, their application to 3D spac...

Energy-Aware Persistent Storage Optimization for Mobile Embedded Edge IoT Platforms

Experimental results show that by using an optimal buffer size, the edge node reduces SPI bus usage for reading and writing data to the SD card from 38.0% to 3.1%, which significantly minimizes delays caused by frequent SD card access and also lowers the edge node’s energy consumption.