# Empowering WBANs: Enhanced Energy Efficiency Through Cluster-Based Routing and Swarm Optimization
## 5 Conclusions  
This study presents a low-power, energy-efficient cluster-based routing approach to dependable biological data gathering in WBAN. Three steps make up the method: routing, CH selection, and cluster creation. The ABBSOA -MSSOA algorithm is used during the cluster development and CH selection phases. In conclusion, utilizing energy-efficient routing, the modified snake swarm optimization algorithm (MSSOA) facilitates intra- and inter-cluster communication. It can ensure reliable and efficient routing of biological data transmission with different priorities, balance the network's power consumption, improve node power utilization efficiency, and ultimately extend the network's life cycle. The proposed protocol outperforms existing MT-MAC, DHCO, ALOC, and M-GWO protocols regarding network performance, lifetime, power efficiency, and reliable data transmission. Based on simulation findings, the suggested approach beats performance parameters such as PDR, AE2E delay, control expenses, cluster construction time, cluster longevity, and network energy usage. Distributed power balancing across nodes also considerably extends the lifetime of the network. Depending on battery charge, hardware discrepancies, and ambient conditions, sensor nodes' initial energy levels might vary in practice. In addition, there is usually some degree of uneven distribution of nodes due to things like human movement, obstructions, and network structure. These differences might cause communication problems and unequal energy use in real-world installations. For future work, we intend to investigate integrating advanced optimization algorithms, such as machine learning-based approaches and hybrid metaheuristic methods, to enhance energy efficiency and scalability in WBANs under dynamic conditions.  
Further study using ML will make the WBAN ABBSOA-MSSOA architecture more adaptive and scalable. Machine learning algorithms provide dynamic routing route predictions and alterations based on environmental changes, user needs, and real-time data trends. Reinforcement learning optimize energy use. One approach to achieve this is to evaluate the network's performance history and respond to changing conditions like node mobility or traffic.  
Federated learning can spread training among WBANs without sharing sensitive patient data. This may enhance prediction models without compromising patient privacy and help diagnose and treat significant medical disorders. Deep learning for sophisticated biosensor data processing is another option. This would help uncover patterns or anomalies that require prompt medical attention. These revisions will tackle scalability issues in large-scale or dynamic WBAN installations and improve real-time decision-making, strengthening the basis for various healthcare applications.  
S.S. conceptualization, methodology, investigation, validation, writing, and visualization. S.B.A.V. Supervision and draft correction. [PERSON] and [PERSON] jointly participated in validating and optimizing overall methodology. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Data Availability Statement: Data are contained within the article.  
The authors declare no conflicts of interest.