# HCLE: Hybrid Clustering Approach for Extending WBAN Lifetime
[PERSON]  
2 Department of Computer Science, College of Computing and Informatics, University of Sharjah, Sharjah 22722, United Arab Emirates; [EMAIL_ADDRESS] (M.A.S.) [PERSON]  
2 Department of Computer and Network Engineering, College of Information Technology, United Arab Emirates University, Al Ain 15551, United Arab Emirates; [EMAIL_ADDRESS] (M.A.S.) [PERSON]  
2 Department of Computer and Network Engineering, College of Information Technology, United Arab Emirates University, Al Ain 15551, United Arab Emirates; [EMAIL_ADDRESS] (M.A.S.) [PERSON]  
2 Department of Computer and Network Engineering, College of Information Technology, United Arab Emirates University, Al Ain 15551, United Arab Emirates; [EMAIL_ADDRESS] (M.A.S.) [PERSON]  
2 Department of Computer and Network Engineering, College of Information Technology, United Arab Emirates University, Al Ain 15551, United Arab Emaries; [EMAIL_ADDRESS] (M.A.S.) [PERSON]  
2 Department of Computer and Network Engineering, College of Information Technology, United Arab Emirates University, Al Ain 15551, United Arab Emirates; [EMAIL_ADDRESS] (M.A.S.) [PERSON] physical hospital visits pose challenges. Finding cost-effective and convenient ways to deliver healthcare services is essential to ensure that individuals receive the care they require [3; 4; 5]. Technological advancements have played an essential role in addressing these challenges. A notable innovation is the integration of remote care providers or hospitals, which allow the delivery of healthcare services from a distance [5; 6]. In this context, WBANs have emerged as a significant trend that offers promising opportunities for remote healthcare monitoring [5; 6; 7; 8; 9]. WBANs are considered a subset of wireless sensor networks (WSNs) and also represent the next generation of personal area networks (PANs) [5; 10; 11; 12; 13; 14; 15]. Additionally, WBANs may contain various types of physiological, kinematic, and ambient sensors. These sensors capture and measure different parameters related to the human body and the environment, providing valuable insights into an individual's health status [7; 16; 17; 18]. In WBANs, small sensor nodes (edge nodes) are strategically placed within a range of 1-2 m [12; 13] from the central node, known as the sink node or coordinator node. The sink node has enhanced resource capabilities to handle data from all sensor nodes and transmit them to the cloud for remote analysis. WBANs may have implanted or wearable sensors [11; 12; 13; 15; 19]. Implanted sensors are designed to be placed inside the human body, while wearable sensors are externally attached to the body, allowing for non-invasive monitoring of physiological and environmental data. The applications of WBANs span medical and non-medical domains [11; 12; 13; 15; 19]. In the medical field, WBANs facilitate real-time health monitoring, remote patient management, and early detection of medical emergencies. For instance, WBANs can monitor vital signs like heart rate, blood pressure, and body temperature, allowing health professionals to remotely assess a patient's health status and provide timely interventions. In non-medical applications, WBANs contribute to various sectors such as monitoring circadian rhythms, supporting battlefield healthcare for military personnel, and enhancing interactive gaming experiences by capturing real-time physiological responses [10; 15; 16; 18; 20; 21]. Implementing WBANs poses several challenges, with a primary concern of energy efficiency [11; 12; 13; 15; 19]. To address these challenges, various routing protocols have been proposed; however, most existing protocols designed for WSNs are not directly applicable to WBANs due to unique constraints in WBANs. WBANs operate with limitations on the number of sensor nodes and the distances between nodes placed on the human body. Consequently, for instance, finding multiple sensors capable of performing the same task, which is applicable in WSNs, becomes constrained in WBANs.