MDMH: An improved multi‐attribute decision‐making and highest response ratio next‐based computation offloading approach for wireless body area networks

Abstract

AbstractWireless body area networks (WBANs) collect health‐related vital signs of human body and provide real‐time and continuous healthy and physical recreation services. Mobile edge computing (MEC) and blockchain technology can significantly improve the quality of service, security, and privacy protection in WBANs. In this paper, we propose an improved computation offloading approach integrating multi‐attribute decision‐making and the highest response ratio next (HRRN) algorithm (MDMH) to optimize network performance and resource allocation in MEC‐enabled WBANs. When tasks are waiting in a queue for execution, the developed HRRN algorithm is designed to solve the starvation problem of low‐priority tasks. Moreover, we employ both analytic hierarchy process (AHP) and the multi‐attribute decision‐making method to select the proper MEC server to guarantee better network performance in mobile scenarios. Comparing with other computation offloading approaches, the simulation results show that the proposed MDMH approach can effectively and efficiently reduce the traffic delay with different user priorities, optimize the communication and computing resource usage in WBANs, and achieve various energy saving goals.