the fact that the used scenarios are substantially complex for a single controller.

Results
=======

In this section, we initially discuss the results on our attempts to evolve a single controller for all the tasks, followed by an analyses of the set of experiments which are performed using the real-robots. Finally, we showcase the results obtained with the IPM scheme running on an emulated network of 100 nodes.\
**Evolving a Single Robot Controller**  A total of ten runs per scenario were performed to evolve a single controller using (1+1)-Online EA running on a single robot. The controller was allowed to evolve for 200 iterations in scenario $S_1$ while the same was 400 for the other two scenarios. In scenario $S_1$, the controllers which learned to move straight towards the light or avoid the obstacle, easily evolved during the first half of the total of 200 evaluations in all the ten runs of the same experiment. It was only after an average $145^{th}$ evaluation count that controllers which learnt partial phototaxis together with obstacle avoidance emerged. These controllers were of inferior quality in the sense that they followed a curved path while moving towards the light source, instead of the preferred 