A novel flocking inspired algorithm for self-organization and control in heterogeneous wireless networks

This paper presents a new model and algorithm for slef-organization and control of a class of next generation communication networks: hierarchical heterogeneous wireless networks (HHWNs), under real world physical constraints. A nature inspired flocking algorithm (FA) is investigated in this context. Our model is based on the control framework at the physical layer presented previously by the authors, where network robustness is characterized in terms of the system's potential energy, and control mechanisms are designed to minimize potential energy for optimized network performance. We first focus on the modeling of HHWNs under real world physical constraints. Second, we propose a new FA for self-organization and control of the backbone nodes in an HHWN by collecting local information from end users. Our algorithm is examined in our built simulation platform that supports various dynamic scenarios. Experimental results demonstrate that FA outperforms current algorithms for the self-organization and optimization of HHWN under real world physical constraints.