Le Thien T T, Moh Sangman
Department of Computer Engineering, Chosun University, 309 Pilmun-daero, Dong-gu, Gwangju 61452, Korea.
Sensors (Basel). 2017 Sep 28;17(10):2231. doi: 10.3390/s17102231.
As wireless body area networks (WBANs) become a key element in electronic healthcare (e-healthcare) systems, the coexistence of multiple mobile WBANs is becoming an issue. The network performance is negatively affected by the unpredictable movement of the human body. In such an environment, inter-WBAN interference can be caused by the overlapping transmission range of nearby WBANs. We propose a link scheduling algorithm with interference prediction (LSIP) for multiple mobile WBANs, which allows multiple mobile WBANs to transmit at the same time without causing inter-WBAN interference. In the LSIP, a superframe includes the contention access phase using carrier sense multiple access with collision avoidance (CSMA/CA) and the scheduled phase using time division multiple access (TDMA) for non-interfering nodes and interfering nodes, respectively. For interference prediction, we define a parameter called interference duration as the duration during which disparate WBANs interfere with each other. The Bayesian model is used to estimate and classify the interference using a signal to interference plus noise ratio (SINR) and the number of neighboring WBANs. The simulation results show that the proposed LSIP algorithm improves the packet delivery ratio and throughput significantly with acceptable delay.
随着无线体域网（WBAN）成为电子医疗保健（e-医疗保健）系统的关键要素，多个移动WBAN的共存正成为一个问题。人体不可预测的运动会对网络性能产生负面影响。在这样的环境中，附近WBAN重叠的传输范围可能会导致WBAN间干扰。我们提出了一种针对多个移动WBAN的具有干扰预测功能的链路调度算法（LSIP），该算法允许多个移动WBAN同时传输而不会造成WBAN间干扰。在LSIP中，一个超帧包括使用带有冲突避免的载波侦听多路访问（CSMA/CA）的竞争接入阶段，以及分别针对无干扰节点和干扰节点使用时分多址（TDMA）的调度阶段。对于干扰预测，我们定义了一个名为干扰持续时间的参数，即不同WBAN相互干扰的持续时间。贝叶斯模型用于利用信干噪比（SINR）和相邻WBAN的数量来估计和分类干扰。仿真结果表明，所提出的LSIP算法在可接受的延迟下显著提高了数据包传输率和吞吐量。
