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Distortion Analysis for Real-Time Data Gathering of Spatially-temporally Correlated Fields in Sensor Networks

机译:传感器网络中空间时间相关领域的实时数据收集的失真分析

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This paper presents a framework for analyzing spatiotemporal distortion associated with data gathering algorithms in sensor networks. We assume that the underlying data field being sensed is spatially and -temporally correlated and the sink performs real-time reconstruction of the field. Unlike existing work, our analysis framework incorporates realistic network/routing parameters, such as congestion and limited transmission range. Our numerical simulations show that even for a simple collision-free transmission scheme and regular grid deployment, the distortion analyses yield starkly different results when network parameters are considered. The analytical method presented in this paper can be easily extended to consider multidimensional deployments, different transmission schemes, nodes distributions, and reconstruction methods.
机译:本文介绍了分析传感器网络中的数据收集算法的时空失真的框架。我们假设被感测的底层数据字段在空间上和 - 常规相关,并且水槽执行该字段的实时重建。与现有工作不同,我们的分析框架包含了现实网络/路由参数,例如拥塞和传输范围。我们的数值模拟表明,即使是为了简单的碰撞传输方案和常规网格部署,当考虑网络参数时,失真分析会产生显着不同的结果。本文提出的分析方法可以很容易地扩展以考虑多维部署,不同的传输方案,节点分布和重建方法。

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