The Internet of Things (IoT) is a new generation of network that can remotely and intelligently control distributed objects. Due to the large number of objects in the IoT, a high data traffic for the object communications is required, which is mostly routed through wireless links. However, the available spectrum for radio frequency (RF) wireless communications is exhausted so that each user in the IoT can only achieve very low data rate. In order to offer a better service to users, a light fidelity (Li-Fi) and radio frequency (RF) hybrid network is considered, where Li-Fi uses the large spectrum of visible light to achieve a high data rate, and the RF system guarantees a seamless coverage. In this study, a load balancing (LB) algorithm for the Li-Fi/RF hybrid IoT network is proposed based on evolutionary game theory (EGT). A key feature of the proposed algorithm is that users autonomously select the APs and adapt their strategies. Thus, compared with the conventional centralised algorithm, the computation load of the central unit (CU) can be reduced by using the EGT algorithm. Moreover, simulation results show that the proposed algorithm outperforms the conventional centralised algorithms in terms of the user satisfaction.
['Yunlu Wang', 'Xiping Wu', 'Harald Haas']
Distributed load balancing for Internet of Things by using Li-Fi and RF hybrid network