A new routing protocol for wireless body area networks based on Q learning and grey wolf optimization

In this paper, a new routing protocol is proposed based on the Q-learning and Grey Wolf Optimization, applicable to Wireless Body Area Networks. The new protocol is designed to improve the routing proficiency with the help of the variables of the adaptive learning approach and metaheuristic optimization. We provide simulations that show the effectiveness of our approach to enhance reasonable network performance in terms of energy usage, end-to-end delay, networks’ lifespan, and many others, The Gray Wolf Optimization (GWO) algorithm was used. For several reasons, including less consumption in ensuring the delivery of complete power data, and the network's longevity is comparable to previously used algorithms such as Particle Swarm Optimization (pso) and Ant Lion Optimizer (ALO), in which more energy is spent, approximately twice what is spent in (GWO). 7.5 is spent on the rest of the algorithms used, and 4.2 is spent on the algorithm used in the article, as the algorithms start from the first round at a rate of 2100 and the GWO starts at a rate of 1200 and produces 4050 rounds, as we will explain later in the practical part.