BGMM: A Body Gauss-Markov Based Mobility Model for Body Area Networks

Existing mobility models have limitations in their ability to simulate the movement of Wireless Body Area Network (WBAN) since body nodes do not exactly follow either classic mobility models or human contact distributions. In this paper, we propose a new mobility model called Body Gauss–Markov Mobility (BGMM) model, which is oriented specially to WBAN. First, we present the random Gauss-Markov mobility model as the most suitable theoretical basis for developing our new model, as its movement pattern can reveal real human body movements. Next, we examine the transfer of human movement states and derive a simplified mathematical Human Mobility Model (HMM). We then construct the BGMM model by combining the RGMM and HMM models. Finally, we simulate the traces of the new mobility model. We use four direct metrics in our proposed mobility model to evaluate its performance. The simulation results show that the proposed BGMM model performs with respect to the direct mobility metrics and can effectively represent a general WBAN-nodes movement pattern.