01
Internet of Things
Making dense, low-power IoT deployments reliable and scalable — reliability, scheduling, and lifetime under real constraints.
Home/Research
Research directions
From the physical link up to the learning layer — the questions that shape what the lab builds, and the protocols, simulators, and testbeds we build to answer them.
02
Wireless Networks
Squeezing more range, throughput, and mission-critical reliability out of the wireless edge, including UAV networks.
03
Protocol Performance
Rigorous, reproducible evaluation of IoT protocols — building simulators and testbeds to see what really happens.
04
Congestion Control
Keeping the next-generation Internet stable under load with smarter flow, queue, and buffer management.
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Edge Computing
Moving intelligence to the edge, where latency, energy, and privacy budgets are tight.
06
Deep RL for Networks
Applying deep reinforcement learning to scheduling, routing, and control in computer networks.
How we work
Build it, then prove it
Every claim in the lab has to survive a measurement. Our method runs on three tracks.
Simulators
Custom, reproducible simulators such as 6TiSCH-Sim let us explore protocol behaviour at scale before touching hardware.
Testbeds
A dedicated IoT testbed grounds our results in real radios, real interference, and real energy budgets.
Learning
Deep RL and other ML methods turn hard-won measurements into controllers that adapt scheduling, routing, and control.