Supervised learning of link quality estimates in wireless networks

Systems composed of a large number of relatively simple, and resource-constrained devices can be designed to interact and cooperate with each other in order to jointly solve tasks that are outside their own individual capabilities. However, in many applications, the emergence of the collective behavior of these systems will depend on the possibility and quality of communication among the individuals. In the particular case of wireless data communication, a fundamental and challenging problem is the one of estimating and predicting the quality of wireless links.

In this talk, I will describe our work and experiences in using supervised learning based methods to model the complex interplay among the many different factors that affect the quality of a wireless link. Finally, I will discuss application scenarios in which the prediction models are used by network protocols to derive real-time robust estimates of link qualities, and by mobile robots to perform spatial predictions of wireless links for path planning.

CV

Eduardo Feo received his masters degrees in Software Systems Engineering at RWTH Aachen and in Informatics at University of Trento, Italy. Currently he is working as a Ph.D. candidate at the Dalle Molle Institute for Artificial Intelligence in Lugano, Switzerland on the topic Mission Planning in Heterogeneous Networked Swarms. The work is funded by the project SWARMIX - Synergistic Interactions of Swarms of Heterogeneous Agents.

His research interests include

• Combinatorial optimization: NP problems, mathematical programming, meta-heuristics.
• Networking: Sensor Networks, network performance modelling, link quality learning.
• Swarm robotics: task planning/allocation in heterogeneous systems.