Energy-efficient synchronization for body sensor network in the metaverse: an optimized connectivity approach

Abstract

Abstract Wireless body sensor networks (WBSNs), or wireless body area networks (WBANs), represent an advanced class of sensor networks where small sensor nodes are either implanted within or attached to the human body. When these networks are integrated with the Internet of Things (IoT) in the healthcare sector, they are collectively referred to as the Internet of Medical Things (IoMT). Body sensor networks (BSNs) have become a pivotal technology in modern healthcare systems, enabling continuous and real-time monitoring of critical patient health metrics such as blood pressure, heart rate, body temperature, body motion, and movement. WBSNs face numerous major challenges that impact their efficiency and performance. Energy consumption remains a critical issue, as sensor nodes operate on limited power sources, leading to reduced network longevity. Signal path loss, caused by body interference and environmental factors, weakens communication reliability. Additionally, dual synchronization is vital for maintaining seamless communication, but managing synchronization between multiple nodes increases complexity and energy demand. Data latency can occur due to transmission delays, impacting real-time monitoring. Scalability challenges arise as networks expand, straining energy and communication resources, while security and privacy concerns persist due to the sensitive nature of medical data, requiring robust protection mechanisms. These issues are the focus of ongoing research aimed at enhancing WBSN performance in healthcare applications. In this paper, we introduce a novel routing protocol, energy-efficient synchronization for body sensor networks (EESBSN), aimed at overcoming these challenges. The proposed EESBSN protocol incorporates a dual synchronization mechanism designed to minimize signal path loss and prevent rapid energy depletion in sensor nodes. Furthermore, it leverages multi-path communication strategies to optimize energy efficiency and extend the operational life span of the network. By utilizing intelligent transponder node selection, EESBSN ensures an even distribution of energy consumption across the network, thereby enhancing its overall stability. Simulation results demonstrate that EESBSN significantly outperforms existing protocols such as CRPBA, H-SAMER, HCEL, SEBA, WHOOPH, and TSFIS-GWO in terms of network stability and performance. These findings highlight the potential of EESBSN to enhance the effectiveness and reliability of BSN-based healthcare systems.

Keywords

• Body sensor
• Sensors in healthcare
• Metaverse
• Energy efficiency
• Path loss
• Dual sink