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Understanding Compressed Sensing Inspired Approaches for Path Reconstruction in Wireless Sensor Networks

机译:了解无线传感器网络中用于路径重构的压缩感知启发方法

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摘要

Abstract:Understanding per-packet routing dynamics in deployed and complex wireless sensor networks (WSNs) has become increasingly important for many essential tasks such as network performance analysis, operation optimization, system maintenance, and network diagnosis. In this paper, we study routing path recovery for data collection in multi-hop WSNs at the sink using a very small and fixed path measurement carried in each packet. We analyze the two recent compressed sensing (CS) inspired approaches called RTR and CSPR. We evaluate RTR versus CSPR as well as other state-of-the-art approaches including MNT and Pathfinder via simulations. Our work provides insights into the better understanding of the profound impacts of different CS-inspired approaches on their respective path reconstruction performance and the resource requirement on sensor nodes. The evaluation results show that the RTR significantly outperforms CSPR, MNT and Pathfinder.
机译:摘要:对于许多基本任务(例如网络性能分析,操作优化,系统维护和网络诊断),了解已部署的复杂无线传感器网络(WSN)中的按包路由动态已变得越来越重要。在本文中,我们使用每个数据包中携带的非常小且固定的路径测量值,研究了汇点处多跳WSN中数据收集的路由路径恢复。我们分析了两种最新的压缩感知(CS)启发方法,称为RTR和CSPR。我们通过仿真评估RTR与CSPR以及其他最先进的方法,包括MNT和Pathfinder。我们的工作为更好地理解不同的CS启发方法对其各自的路径重构性能和传感器节点的资源需求所产生的深远影响提供了见识。评估结果表明,RTR明显优于CSPR,MNT和Pathfinder。

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