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Link Scheduling Algorithm with Interference Prediction for Multiple Mobile WBANs

机译:多个移动WBAN的具有干扰预测的链路调度算法

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摘要

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.
机译:随着无线人体局域网(WBAN)成为电子医疗保健(e-healthcare)系统中的关键要素,多个移动WBAN的共存成为一个问题。网络性能受到人体不可预测的运动的负面影响。在这种环境下,WBAN间干扰可能是由于附近WBAN的传输范围重叠所致。我们针对多个移动WBAN提出了一种具有干扰预测(LSIP)的链路调度算法,该算法允许多个移动WBAN同时发送,而不会引起WBAN间干扰。在LSIP中,超帧分别包括使用无冲突的载波侦听多路访问的竞争访问阶段(CSMA / CA)和使用时分多路访问(TDMA)的调度阶段,分别用于非干扰节点和干扰节点。对于干扰预测,我们将称为干扰持续时间的参数定义为不同WBAN相互干扰的持续时间。贝叶斯模型用于使用信噪比加噪声比(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算法以可接受的延迟显着提高了分组的传输率和吞吐量。

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