Task Offloading and Resource Allocation Using the Echo Tracking Optimization Enabled QoS-Aware Scheduling for MEC-Enabled WBAN Healthcare System

Abstract

In the context of the Internet of Medical Things (IoMT), the rapid expansion of wearable medical devices and healthcare data presents tremendous challenges related to the improved Quality of Service (QoS) and computing task offloading for Smart healthcare systems. Further, the Mobile Edge Computing (MEC)-enabled healthcare systems, which allow computation offloading to edge servers nearby, are attracting great attention as a result of the extraordinary development in Wireless Body Area Network (WBAN) users and applications based on 5G. However, the existing systems in MEC-enabled WBAN-based healthcare systems produce too many control frames while transmitting data, resulting in increased latency, energy wastage, and a lack of flexibility. Therefore, this research aims to design a routing algorithm in WBAN that efficiently allocates resources and consumes less energy utilizing the Echo Tracking Optimization-based MEC-enabled WBAN systems. Specifically, the proposed model provides ultra-reliable data transfer and processing with extremely low latency and energy consumption to meet the demands of healthcare services and applications. More effectively, the proposed approach exploits the Echo Tracking Optimization (ETO) that handles the resource allocation and enhances the QoS by addressing the problem of selection of the target tasks on analyzing the medical criticality, highest relative computing capacity, and energy constraints for effective task offloading. Compared to the other existing techniques, the proposed ETO-QoS aware scheduling effectively lowers latency and energy consumption while increasing throughput and overall WBAN utilization by reporting a delay of 0.102 ms, energy loss of 7.523 J, packet loss of 95, and throughput of 0.723 Kbps outperforming the other existing techniques.