# Empowering WBANs: Enhanced Energy Efficiency Through Cluster-Based Routing and Swarm Optimization
## 1 Introduction
However, difficulties and system costs can grow when considering two-hop neighbour data. Some requirements must be met for these WBANs to continue functioning. The WBAN development process's primary goals are innovative battery and energy management technologies and flexible packaging. The issue of energy consumption is addressed in this work. We present a biological data collection protocol to decrease network energy utilization. The suggested method is an innovative technique based on the shortcomings of current methods. Well-designed routes are one way to increase the utilization of energy. Cluster-based routing is in charge of creating network routes. Improving energy efficiency, extending network life, and avoiding network split have become a significant WBAN routing protocol analysis focus. Based on the previous evaluation, this work suggests an energy-saving biological data acquisition procedure for accurate data transfer in WBANs. The proposed method guarantees accurate and effective biological data transmission, balances network power consumption, and extends the network life cycle. Figure 1 depicts the WBAN logical topology. The WBAN comprises a receiver point and several SNs. This study presents a novel approach to energy efficiency in wireless body area networks (WBANs) using a hybrid swarm intelligence method called the adaptive binary bird swarm optimization algorithm and the modified snake swarm optimization algorithm (ABBSOA-MSSOA). The low battery capacity of wearable sensors makes energy consumption a critical challenge in WBANs. The scalability and flexibility of traditional energy optimization strategies might be challenging in dynamic contexts. To address these constraints, this research presents a new approach to energy management and routing in WBANs by combining two swarm optimization techniques, MSSOA and ABCSOA, into a single framework. The adaptive binary decision-making introduced by ABBSOA improves the algorithm's capacity to strike a good balance between the exploration and exploitation stages. This helps it optimize binary judgments, which helps with energy efficiency, including finding the best routes to go or which states to activate sensors. In contrast, MSSOA eliminates the possibility of routing deadlocks and guarantees improved pathfinding in WBANs by optimizing clustering and routing choices using behaviour inspired by snakes. Combining these two methods in the ABBSOA-MSSOA framework produces an improved optimization model for energy-efficient routing. The proposed hybrid approach optimizes routing and clustering choices, considerably increasing the energy efficiency of WBANs and extending the network's lifetime. Additionally, the model's adaptability makes it ideal for large-scale, dynamic WBAN settings, where conventional algorithms often underperform. Future tactics for optimizing WBAN energy could take a page out of this study's novel swarm intelligence approach. Figure 1: Logical topology of wireless body area network. The research proposes an innovative hybrid optimization methodology called ABBSOA-MSSOA to address WBAN energy consumption challenges. This technique maintains a healthy balance between discovery and exploitation in routing and clustering by combining the ABBSOA and the MSSOA. ABBSOA optimizes binary decisions like sensor activation and route selection to maximize energy efficiency. MSSOA adjusts clustering configurations and routes based on snake behaviour to enhance energy distribution, minimize routing deadlocks, and optimize energy distribution. Due to its versatility in various WBAN topologies, the two-layered optimization strategy is better than single-method approaches.