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

Body Area Network (BAN) is composed of varioussensors and some actuators which are attached on body or evenimplanted under skin. The data is gathered from sensors indifferent periods of time and transferred to the servers or doctorsfor interpretation. Sometimes after processing, control signalscomeback to the origin for actuation activities. This application isdelay-sensitive and its performance evaluation is critical concern.In this paper, we focus on performance evaluation of BANtechnology in a typical hospital. We also employ the Mobile EdgeComputing (MEC) paradigm. The idea of MEC is using smalldata centers in the proximity of mobile base stations. As a result,user requests will fulfill in shorter time. The motto of MEC islocal problems should be solved locally. MEC is known as baseand key technology of 5G and IoT. In this paper, we extract theservicing steps of BAN application in a hospital. Markov RewardModels (MRM) are presented for each steps. The models aresolved by SHARPE software package. Mean response delay andrequest blocking probability are calculated under differentsituation. Moreover, the numerical results are verified byDiscrete-Event Simulation.

Keywords:

Body Area Network , Mobile Edge Computing , Performance Evaluation , Markov Reward Model