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首页> 外文会议>International Conference on Big Data Computing and Communications >WiFi-ZigBee Coexistence Based on Collision Avoidance for Wireless Body Area Network
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WiFi-ZigBee Coexistence Based on Collision Avoidance for Wireless Body Area Network

机译：基于防撞的WiFi-ZigBee共存的无线人体局域网

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ZigBee sensor nodes of a Wireless Body Area network (WBAN) are implanted in or equipped on a person's body. These nodes are employed to transmit important physiological or motion parameters. This wearable healthy telemonitoring system makes the guardians out of the complex wired connection, as well as brings convenience to hospitals and patients for long-term remote monitoring. However, while enjoying the convenience, we also observe that the interference between WiFi and ZigBee signals will lead to sensitive medical data transmission failure, which may affect the timeliness of communication from sensors to the sink. In this paper, we propose a method that ensures high performance of ZigBee signal transmission in spite of the presence of strong interference from WiFi with no additional ZigBee or WiFi hardware devices. In our approach the Hidden Markov Model is applied to train and learn ZigBee and WiFi channel status. When the prediction of status is "ZigBee-WiFi conflict", WiFi transmissions are suppressed by simulated collisions so as to satisfy the precedence of ZigBee communication. Thus it ensures the defined link throughput and packet delivery ratio for WBAN communication. Some experimental results have shown the effectiveness of our method.
机译：无线人体局域网（WBAN）的ZigBee传感器节点被植入人体中或装备在人体上。这些节点用于传输重要的生理或运动参数。这种可穿戴的健康远程监控系统使监护人摆脱了复杂的有线连接，并为医院和患者提供了长期远程监控的便利。但是，在享受便利的同时，我们还观察到WiFi和ZigBee信号之间的干扰将导致敏感的医学数据传输失败，这可能会影响从传感器到接收器的通信及时性。在本文中，我们提出了一种方法，即使存在来自WiFi的强烈干扰，也无需额外的ZigBee或WiFi硬件设备，仍可确保ZigBee信号传输的高性能。在我们的方法中，采用隐马尔可夫模型来训练和学习ZigBee和WiFi信道状态。当状态预测为“ ZigBee-WiFi冲突”时，WiFi传输将通过模拟冲突进行抑制，从而满足ZigBee通信的优先级。因此，它确保了WBAN通信的定义的链路吞吐量和数据包传输率。一些实验结果表明了我们方法的有效性。

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