Demo: efficient multi-objective optimization of network problems on wireless testbeds

A large amount of research focuses on experimentally optimizing performance of wireless solutions. Finding the optimal performance settings typically requires investigating all possible combinations of design parameters, and as a result the number of required experiments increases exponentially for each considered design parameter. However, Efficient Global Optimization (EGO) algorithms overcome this limitation and arrive at the global optimum performance in a very short time compared to the exhaustive search technique. In this demo, we apply the SUrrogate MOdeling (SUMO) toolbox, an efficient implementation of EGO algorithms, in order to improve the experimentation time of a realistic wireless conference solution. By tuning a speaker's transmit power and channel parameters, the SUMO toolbox searches for an improved listeners' audio quality having minimum transmission exposure. Moreover, the SUMO experiment is compared to an exhaustive search experiment and it is found that SUMO reached 99.51\% of the global optimum performance while requiring 10 times less experiments.