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Dynamic modulation scaling enabled multi-hop topology control for time critical wireless sensor networks

机译:动态调制缩放启用了针对时间紧迫的无线传感器网络的多跳拓扑控制

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The previous work on connection driven topology control has shown that it has significant potential to reduce energy consumption of Wireless Sensor Networks (WSNs). Dynamic Modulation Scaling (DMS) which is a technique that manages transmission power levels in order to change the number of bits encoded per symbol has a direct impact on connection driven topology control. In this paper we investigate the transmission scheduling of multi-hop real-time WSNs equipped with DMS enabled radio chips while taking the effect of DMS on topology control into account. To our best knowledge, this is the first paper that addresses this issue. The current work on DMS enabled WSN tend to rely on theoretical DMS models to predict network performance metrics. However, there is little, if any, work that is based upon empirically verified network performance outcomes using DMS especially on its effect on connection driven topology control. This paper fills this gap by using GNU Radio and Software Defined Radio hardware to show how to emulate DMS in low power wireless systems and measure the impact of varying Signal-to-Noise levels, distance and elevation on throughput and delivery rates for different DMS control strategies. Next, we present the Mixed Integer Nonlinear Optimization Problem of minimizing energy consumption of DMS enabled connection driven topology control on real-time WSNs. Lastly, we present two polynomial time heuristics and compare their performance against the optimal solution.
机译:以前有关连接驱动的拓扑控制的工作表明,它在减少无线传感器网络(WSN)的能耗方面具有巨大的潜力。动态调制缩放(DMS)是一种管理传输功率级别以更改每个符号编码的位数的技术,它直接影响连接驱动的拓扑控制。在本文中,我们研究了配备DMS的无线电芯片的多跳实时WSN的传输调度,同时考虑了DMS对拓扑控制的影响。据我们所知,这是解决此问题的第一篇论文。当前在启用DMS的WSN上的工作往往依赖于理论DMS模型来预测网络性能指标。但是,很少有基于DMS经验验证的网络性能结果的工作,如果有的话,尤其是其对连接驱动的拓扑控制的影响。本文通过使用GNU无线电和软件定义的无线电硬件填补了这一空白,展示了如何在低功率无线系统中仿真DMS并测量不同信噪比水平,距离和仰角对不同DMS控制的吞吐量和传输速率的影响策略。接下来,我们提出了混合整数非线性优化问题,该问题使实时WSN上启用DMS的连接驱动拓扑控制的能耗最小。最后,我们给出了两个多项式时间启发式方法,并将它们的性能与最优解进行了比较。

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