RAT Association for Autonomic IoT Systems

The next generation of wireless communications consists of heterogeneous networks that operate over different technologies and standards. Co-existence of multiple technologies aims to enhance the network capacity in order to satisfy the increasing number of connected devices. However, in order to meet high performance, it is important to assign the right device to the right RAT. In this article, we propose a novel distributed approach to solve the RAT selection problem, in a self-organized IoT context. Our scheme is mainly based on tools from MG theory. We show that a stable association between connected IoT devices and available RATs can be met while using a fully distributed algorithm that respects connected devices requirements and RAT constraints as well. The proposed MG assignment allows IoT devices, with limited energy budget, to improve their energy efficiency and to reduce the data transmission cost over the serving RAT at very light signaling overhead. Moreover, extensive simulations show that the MG based approach outperforms the conventional throughput maximization association in terms of energy efficiency and throughput. Our scheme also exhibits some nice load balancing features.