A novel routing protocol based on grey wolf optimization and Q learning for wireless body area network.

• The paper is dedicated to proposed a cluster-based routing protocol for WBAN. • The paper utilizes the benefits of machine learning. • A novel approach, MGWOQL, is proposed for cluster head selection and updation. • The multi-objective function is designed to select the optimal cluster head node. • The result shows better network longevity, residual energy, and path loss. Recently, Wireless Body Area Networks (WBAN) have been developed to advance Internet-of-Things (IoT) that play an essential role in biomedical applications. While deploying these applications practically, there may arise associated issues. Among all the available problems, the primary concern is energy utilization among these resource-limited sensors during data communication. These sensors continuously sense the signal and send messages to other nodes. There is a need to optimize the energy utilization in WBAN. This paper proposes a cluster-based routing protocol for WBAN with the benefits of machine learning to predict energy wastage. A Modified Grey Wolf Optimization with Q-Learning (MGWOQL) is proposed for cluster head selection and updating. The proposed protocol used different objective functions to minimize the energy utilization of clusters by selecting the optimal cluster head (CH). The simulation was performed on the MATLAB platform under different conditions. The result analysis shows its efficiency in terms of energy for WBAN. [ABSTRACT FROM AUTHOR]