工业无线传感器执行器网络的灵活重传策略

Ryan Brummet, Dolvara Gunatilaka, Dhruv Vyas, O. Chipara, Chenyang Lu
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引用次数: 23

摘要

实时、可靠的通信是工业无线传感器-执行器网络的关键。为此,研究人员提出了各种各样的传输调度技术。然而,这些方法通常采用以链路为中心的策略,为流的每个链路分配固定数量的重传。这种方法缺乏灵活性是有问题的,因为故障不会在各个链接之间统一发生,而且链接质量会随着时间的推移而变化。本文提出了一种以流为中心的策略,在运行时灵活动态地重新分配多跳流链路之间的重传。在捕获工业环境常见无线特性的两种故障模型下,确定实现用户指定可靠性级别所需的重传次数的方法补充了这一贡献。我们使用经验评估和跟踪驱动的模拟来证明以流量为中心的策略的有效性。试验台实验表明,以流为中心的策略具有灵活性,可以提供比以链路为中心的策略更高的可靠性。跟踪驱动实验比较了两种可靠性模型下以链路为中心和以流为中心的策略。结果表明,当两种方法配置为达到相同的可靠性水平时,以流为中心的方法将中位实时容量提高了1.42倍,将端到端响应时间减少了2.63倍。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Flexible Retransmission Policy for Industrial Wireless Sensor Actuator Networks
Real-time and reliable communication is essential for industrial wireless sensor-actuator networks. To this end, researchers have proposed a wide range of transmission scheduling techniques. However, these methods usually employ a link-centric policy which allocates a fixed number of retransmissions for each link of a flow. The lack of flexibility of this approach is problematic because failures do not occur uniformly across links and link quality changes over time. In this paper, we propose a flow-centric policy to flexibly and dynamically reallocate retransmissions among the links of a multi-hop flow at runtime. This contribution is complemented by a method for determining the number of retransmissions necessary to achieve a user-specified reliability level under two failures models that capture the common wireless properties of industrial environments. We demonstrate the effectiveness of flow centric policies using empirical evaluations and trace-driven simulations. Testbed experiments indicate a flow-centric policy can provide higher reliability than a link-centric policy because of its flexibility. Trace-driven experiments compare link-centric and flow-centric policies under the two reliability models. Results indicate that when the two approaches are configured to achieve the same reliability level, a flow-centric approach increases the median real-time capacity by as much as 1.42 times and reduces the end-to-end response times by as much as 2.63 times.
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