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A metric for routing in delay-sensitive wireless sensor networks

机译:延迟敏感型无线传感器网络中的路由度量

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We present a new scheme to reduce the end-to-end routing delay in the mission-critical applications of the wireless sensor networks (WSNs) under the duty cycle model. While greedy routing in the synchronized MAC model has been studied extensively, efficient routing in an asynchronous MAC model is considerably different because the wake-up time and availability of a node along the pre-decided path are not synchronized and can be changed by many dynamic factors. The challenge is to catch this dynamic change in time and furthermore, to minimize its impact on routing decisions. We propose a normalized evaluation value ε [0, 1] at each node under the proactive model for all different paths passing through, saving the cost and delay of the reactive information model. Its measurement interprets the existence of the fastest path to the edge of the networks in a certain direction, directing any local advance greedy in the same direction. We provide a new strategy for greedy routing. First, it waits for the appearance of the expected forwarding successor; if this fails, then it will select the backup by the “first-wake-up, first use” policy to avoid a dead wait. We focus on an “everyone” model, in which each node will apply the same generic process in a fully distributed manner in order to achieve a reliable solution. By applying our approach in the networks with a uniform wake-up schedule, we illustrate the substantial improvement of our approach in both analytical and experimental results compared with the best known results.
机译:我们提出一种新的方案,以减少占空比模型下无线传感器网络(WSN)的关键任务应用中的端到端路由延迟。尽管已广泛研究了同步MAC模型中的贪婪路由,但异步MAC模型中的有效路由却大不相同,因为沿预定路径的节点的唤醒时间和可用性未同步,并且可以通过许多动态更改因素。挑战在于及时了解这种动态变化,并最大程度地减小其对路由决策的影响。我们针对所有经过的不同路径,在主动模型下的每个节点处建议归一化评估值ε[0,1],从而节省了被动信息模型的成本和延迟。它的测量解释了在某个方向上到达网络边缘的最快路径的存在,从而将任何本地进阶贪婪都指向同一方向。我们提供了一种贪婪路由的新策略。首先,它等待预期的转发后继者的出现;如果失败,则它将通过“首次唤醒,先使用”策略选择备份,以免造成不必要的等待。我们关注“每个人”模型,其中每个节点将以完全分布式的方式应用相同的通用过程,以实现可靠的解决方案。通过将我们的方法应用于具有统一唤醒时间表的网络中,我们说明了与最著名的结果相比,我们的方法在分析和实验结果上的显着改进。

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