Modeling and Performance Evaluation of Body Area Network in Mobile Edge Computing Paradigm

—Body Area Network (BAN) is composed of various sensors and some actuators which are attached on body or even implanted under skin. The data is gathered from sensors in different periods of time and transferred to the servers or doctors for interpretation. Sometimes after processing, control signals comeback to the origin for actuation activities. This application is delay-sensitive and its performance evaluation is critical concern. In this paper, we focus on performance evaluation of BAN technology in a typical hospital. We also employ the Mobile Edge Computing (MEC) paradigm. The idea of MEC is using small data centers in the proximity of mobile base stations. As a result, user requests will fulfill in shorter time. The motto of MEC is local problems should be solved locally. MEC is known as base and key technology of 5G and IoT. In this paper, we extract the servicing steps of BAN application in a hospital. Markov Reward Models (MRM) are presented for each steps. The models are solved by SHARPE software package. Mean response delay and request blocking probability are calculated under different situation. Moreover, the numerical results are verified by Discrete-Event Simulation