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 distribu...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.展开更多
基金supported by the National Natural Science Foundation of China(Nos.61171107 and 61271257)General program of science and technology development project of Beijing Municipal Education Commission(Nos.KM201510012005,SQKM201610012008,and SQKM201710012006)+1 种基金the research project of the China Scholarship Council(Nos.201509970037 and 201609970003)Beijing Institute of Fashion Technology College of Special Plan Young Top-notch Talent Project(No.BIFTBJ201803)
文摘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.
