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Geometric Monitoring in Action: a Systems Perspective for the Internet of Things

机译:实际中的几何监视:物联网的系统视角

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Applications for IoT often continuously monitor sensor values and react if the network-wide aggregate exceeds a threshold. Previous work on Geometric monitoring (GM) has promised a several-fold reduction in communication but been limited to analytic or high-level simulation results. In this paper, we build and evaluate a full system design for GM on resource-constrained devices. In particular, we provide an algorithmic implementation for commodity IoT hardware and a detailed study regarding duty cycle reduction and energy savings. Our results, both from full-system simulations and a publicly available testbed, show that GM indeed provides several-fold energy savings in communication. We see up to 3x and 11x reduction in duty-cycle when monitoring the variance and average temperature of a real-world data set, but the results fall short compared to the reduction in communication (4.3x and 44x, respectively). Hence, we investigate the energy overhead imposed by the network stack and the communication pattern of the algorithm and summarize our findings. These insights may enable the design of protocols that will unlock more of the potential of GM and similar algorithms for IoT deployments.
机译:物联网的应用程序通常会不断监视传感器的值,如果全网范围的聚合超过阈值,则会做出反应。先前有关几何监视(GM)的工作已承诺将通信减少几倍,但仅限于分析或高级仿真结果。在本文中,我们在资源受限的设备上构建并评估了GM的完整系统设计。特别是,我们为商品IoT硬件提供了一种算法实现,并提供了有关减少占空比和节能的详细研究。我们从全系统仿真和可公开获得的测试台获得的结果表明,通用汽车确实在通信方面节省了几倍的能源。监控实际数据集的方差和平均温度时,我们看到占空比分别降低了3倍和11倍,但与通信量的减少(分别为4.3倍和44倍)相比,结果不足。因此,我们研究了网络堆栈和算法的通信模式带来的能量开销,并总结了我们的发现。这些见解可以使协议设计成为可能,从而为物联网部署释放更多的GM和类似算法的潜力。

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