WITHDRAWN: Multiobjective Routing Protocol Design in WBAN Using Optimized Fuzzy Clustering and Hybrid Deep Learning for Optimal Path Selection

Abstract

Real-time monitoring of patients' health conditions is now possible because to wireless body area networks (WBANs). In WBANs, nodes are placed inside, on, or around the human body to collect a range of physiological data, such as body temperature, heart rate, and blood pressure. The routing of data packets in WBANs, however, confronts a number of difficulties because of the dynamic nature of the body, including limited power, interference, and movement. Data packet routing can be made more efficient by using a multiobjective routing protocol architecture to handle these problems. In this research work, WBAN's multiobjective routing protocol architecture is made possible by two phases as clustering and optimal path selection.To group nodes based on several objectives, such as energy consumption, transmission delay, and network longevity, the proposed multiobjective routing protocol design in WBAN uses an optimised Fuzzy C-means clustering method. The membership function in Fuzzy C-means clustering is determined using a novel hybrid optimization model HBWBF that combines the Black Widow Optimization (BWO) and the Bacterial Foraging Optimization Algorithm (BFOA). The hybrid deep learning approach for determining the optimal path between clusters combines Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs).The proposed model is implemented in MATLAB. To validate the efficiency of the proposed model, a comparative evaluation is performed.