Enhanced Network Selection Algorithms for IoT-Home Environments With Hybrid VLC/RF Systems

The impending scarcity of the radio frequency (RF) spectrum has led to a boom in the development of complementary technologies such as visible light communication (VLC). Hybrid systems that complement both technologies have demonstrated numerous advantages in terms of data rate, bandwidth, and security. However, the successful operation of a hybrid VLC/RF network is highly dependent on the network selection process. In this paper, three network selection algorithms are proposed for hybrid VLC/RF systems in an indoor Internet of things (IoT)-home scenario. The proposed algorithms consider the signal-to-noise ratio (SNR), the available capacity of each network, the number of devices connected to each network, and the users' location within the scenario. Each algorithm analyzes the data load requirements of each mobile terminal and IoT device according to these metrics. Based on this evaluation, the network that offers the best benefit in terms of signal quality, capacity, and throughput is selected. The numerical results show that the VLC network presents higher SNR values, especially for users near its access points. In addition, a higher bandwidth, a higher connection capacity, and an improvement in the quality of service are perceived from these positions. The three proposed selection algorithms presented good overall performance of the hybrid system, with the Analytical Algorithm offering the best performance for connected users, superior to the Sequential Algorithm by an average of 15.9%. The network selection algorithms efficiently allocated the maximum number of users for the VLC network, improving overall system performance and reducing the RF network data load.