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Obstacle mapping in wireless sensor networks via minimum number of measurements

机译：通过最少的测量次数在无线传感器网络中进行障碍物映射

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摘要

In this study, a group of wireless sensors are tasked to trace indoor obstacles without the need to sense them, directly. The authors introduce a novel framework based on compressed sensing theory that allows sensors to map two-dimensional spatial details, non-invasively. By exploiting an alternative projection method which reduces the randomness nature of previous works, the resulting measurement matrix can provide linear samples from an unknown environment more efficiently. It is shown that how sparse representation of the spatial parameters in some domains can be utilised in order to realise obstacle mapping with minimum number of wireless transmissions and receptions. In addition, theoretical analyses along with simulation results illustrate premier performance of their framework. Furthermore, they test their method in different circumstances and show how drawbacks such as walls, bulkheads, and environmental constraints can affect the reconstruction performance. Therefore, they proposed two algorithms (i.e. reducing wall effect and reducing bulkhead effect) in order to decrease the impression of walls and bulkheads which is supported theoretically. Finally, a well-applicable scenario based on their framework is defined and proposing the next best transmitter algorithm remarkable results are achieved.
机译：在这项研究中，一组无线传感器的任务是跟踪室内障碍物，而无需直接感应它们。作者介绍了一种基于压缩感测理论的新颖框架，该框架允许传感器以非侵入方式绘制二维空间细节。通过利用减少前期工作的随机性的替代投影方法，所得的测量矩阵可以更有效地提供来自未知环境的线性样本。示出了如何利用某些域中的空间参数的稀疏表示来实现具有最少数量的无线发送和接收的障碍物映射。此外，理论分析和仿真结果说明了其框架的出色性能。此外，他们在不同的情况下测试了他们的方法，并展示了诸如墙壁，舱壁和环境约束之类的缺点如何影响重建性能。因此，他们提出了两种算法（即减小壁效应和减小舱壁效应），以减少理论上支持的壁和舱壁的印象。最后，基于它们的框架定义了一个很好的应用场景，并提出了下一个最佳的发送器算法，从而获得了显着的结果。

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