Hybrid FGWO Based FLCs Modeling for Performance Enhancement in Wireless Body Area Networks

Abstract

The progression over wireless technologies paves the way for the emergence of wireless body area networks (WBAN) towards several motivating applications. Specifically, in terms of health concern applications, both the performance and reliability is regarded as the essential elements of WBANs. Many of the soft computational methodologies employed the manual modeling of fuzzy logic controllers (FLCs) by evolutionary algorithms in WBAN. This existing model encodes the entire control parameters of “FLCs” membership functions. This leads to the degradation of network performance by maximizing the latency. In order to rectify this issue, here we propose a hybrid firefly grey wolf optimizer (hybrid FGWO) approach for the optimal modeling of “FLC”. The major goal behind our proposed work relays on the optimal selection of control parameters from the “FLCs” with hybrid FGWO. The modeling of “FLCs” is carried out with CLFB (cross-layer fuzzy logic dependent back-off controller) mechanism to control the frequent access of channels. The efficiency of the “FLCs” model is enhanced by utilizing the coding technique known as unrestricted coding scheme. The performance of our hybrid FGWO approach is contrasted with three conventional “EAs”. Two major modeling goals are established whereas, the initial goal aims for the modeling of “FLCs” on particular configuration of network and the second goal aims on the modeling of “FLCs” over multiple network configurations. The “FLCs” modeled by means of our proposed hybrid FGWO approach exhibits its performance in terms of throughput, latency and packet delivery ratio with some of the challenging algorithms.