In recent years, a hopeful model of hybrid networks based on light fidelity (LiFi) and wireless fidelity (Wi-Fi) named hybrid LiFi-Wi-Fi networks (HLWNets) has been introduced. To address this issue, an innovative app...
详细信息
In recent years, a hopeful model of hybrid networks based on light fidelity (LiFi) and wireless fidelity (Wi-Fi) named hybrid LiFi-Wi-Fi networks (HLWNets) has been introduced. To address this issue, an innovative approach sewing training inspiredoptimisation_transfer learning (STIO_TL) is introduced for AP selection in the handover process. Initially, a system model of HLWNets is developed and the mobility-aware optical RWP is designed for the handover process. In the handover process, location is predicted every time using the deep recurrent neural network (DRNN). Afterwards, the AP selection is done by the proposed STIO_TL and is processed by several parameters. The proposed STIO_TL is the integration of the sewing circle inspired optimisation algorithm (STBO) and training-based optimisationalgorithm (CIOA). Additionally, the effectiveness of the proposed STIO_TL is evaluated based on the evaluation metrics, like delay, handover occurrence, energy efficiency, and network throughput of 0.111 mS, 6.086, 0.099 Mbits/joules and 0.913 Mbps respectively.
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