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Research

Energy-vs-Performance adaptive wireless acoustic sensor networks

Distributed algorithms allow wireless acoustic sensor networks (WASNs) to divide the computational load of signal processing tasks, such as speech enhancement, among the sensor nodes.

However, current algorithms focus on performance optimality, oblivious to the energy constraints that battery-powered sensor nodes usually face. To extend the lifetime of the network, nodes should be able to dynamically scale down their energy consumption when decreases in performance are tolerated. In this doctoral project we study the relationship between energy and performance in wireless acoustic networks, particularly in a speech enhancement application.

The goal is to propose strategies that introduce exibility to adjust the energy consumption and the desired performance. It is believed that the envisioned approach will result in energy and hence network lifetime improvements with at least one order of magnitude for WASNs.

Biography

Publications