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https://github.com/signetlabdei/

Internet of things

Network protocol design, spatio-temporal compression and data mining from various application domains, machine learning based data collection and processing schemes. Our current activities include the study of biometric signals from wearable IoT devices and wireless sensing.

5G Cellular Networks

Research on 5G cellular nets is ongoing to achieve unprecendently experienced bitrates, exploiting massive MIMO technology, machine learning for self-adapting audio/video streaming and intelligent network policies for energy harvesting base stations and devices.

Underwater Networks

We are involved in several research activities for underwater mobile networking. There, our focus is centered around the design of networking protocol (channel access, routing, data gathering, etc). Various experimental activities are also on the way, including simulation, lab and field test of network protocols and at-sea testing.

Smart Energy Grids

Our focus is centered around the use of Distributed Energy Sources (DES) in electricity grids. This includes the joint optimization of control, communication algorithms and market rules (energy trading and pricing). Our study also encompasses PLC communication standards and network elements equipped with energy harvesting capabilities.

Microfluidics

Droplet microfluidics refers to manipulation and control of little amount of fluids flowing into channels of micro-size scale. We are concerned with understanding microfluidic flow dynamics and their propagation characteristics. These concepts are then used to devise networking algorithms to route droplets in a controlled manner through complex microfluidic networks.

Human Sensing

Human sensing technology is being massively introduced in our everyday life. Example are smartphone applications gathering inertial signals, assisted living products and intelligent white goods. The general idea is to measure human data / posture / vital signs or gestures to feed applications. Our group is especially interested in data mining in the contexts of physiological signals and motion tracking.

mmWave networks

The millimeter wave bands offer orders of magnitude more spectrum than conventional wireless frequencies but, on the other hand, suffer from increased path loss, severe channel intermittency and inability to penetrate through solid materials. To overcome these issues, further studies are required in a way to perform the transmission in future mobile networks.