Highly Reliable Energy-Saving MAC for Wireless Body Sensor Networks in Healthcare Systems
Highly Reliable Energy-Saving MAC for Wireless Body Sensor Networks in Healthcare Systems
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DOI：
10.1109/jsac.2009.090516
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发表时间：
2009-05-01
影响因子：
16.4
通讯作者：
Verikoukis, Christos
中科院分区：
文献类型：
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作者：
Otal, Begonya;Alonso, Luis;Verikoukis, Christos
医疗保健系统中的无线身体传感器网络（BSNs）在相互冲突的要求下运行。这些是维持所需的可靠性和数据传输的消息延迟，同时最大限度地延长单个身体传感器的电池寿命。在此过程中，必须考虑整个系统的特性，包括物理层、介质访问控制层（MAC）和应用层。本文的目的是为BSNs开发一个新的MAC模型，以满足现实医疗环境下所有这些特定的严格要求。为此，提出了一种新的跨层模糊规则调度算法和能量感知无线电激活策略。其主要思想是在每个身体传感器中集成一个模糊逻辑系统，以独立的方式处理多个不同性质的跨层输入变量。通过自主感知当前状态，身体传感器能够要求“无碰撞”时隙，只要它们认为这是严格要求的（例如，高系统数据包延迟或低身体传感器剩余电池寿命）。类似地，如果有一个坏的通道链接，它们可能会拒绝传输，从而允许另一个身体传感器这样做。这将提高系统的整体性能。通过计算机模拟，根据特定医疗保健场景下的服务质量和能源消耗对所提出的MAC模型进行了评估。
Wireless Body Sensor Networks (BSNs) in healthcare systems operate under conflicting requirements. These are the maintenance of the desired reliability and message latency of data transmissions, while simultaneously maximizing battery lifetime of individual body sensors. In doing so, the characteristics of the entire system, including physical, medium access control (MAC), and application layers have to be considered. The aim of this paper is to develop a new MAC model for BSNs to fulfill all these specific rigorous requirements under realistic medical settings. For that purpose, a novel cross-layer fuzzy-rule scheduling algorithm and energy-aware radio activation policies are introduced. The main idea is to integrate a fuzzy-logic system in each body sensor to deal with multiple cross-layer input variables of diverse nature in an independent manner. By being autonomously aware of their current condition, body sensors are able to demand a "collision-free" time slot, whenever they consider it strictly required (e.g. high system packet delay or low body sensor residual battery lifetime). Similarly, they may refuse to transmit, if there is a bad channel link, thus permitting another body sensor to do so. This results in improving the system overall performance. The proposed MAC model is evaluated by computer simulations in terms of quality of service and energy consumption under specific healthcare scenarios.