Controlling Large-Scale Self-Organized Networks with Lightweight Cost for Fast Adaptation to Changing Environments

Self-organization has potential for high scalability, adaptability, flexibility, and robustness, which are vital features for realizing future networks. Convergence of self-organizing control, however, is slow in some practical applications compared to control with conventional deterministic systems using global information. It is therefore important to facilitate convergence of self-organizing controls. In controlled self-organization, which introduces an external controller into self-organizing systems, the network is controlled to guide systems to a desired state. Although existing controlled self-organization schemes could achieve this feature, convergence speed for reaching an optimal or semioptimal solution is still a challenging task. We perform potential-based self-organizing routing and propose an optimal feedback method using a reduced-order model for faster convergence at low cost. Simulation results show that the proposed mechanism improves the convergence speed of potential-field construction (i.e., route construction) by at most 22.6 times with low computational and communication cost.