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Toward Accurate Mobile Sensor Network Localization in Noisy Environments

机译：在嘈杂环境中实现精确的移动传感器网络定位

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摘要

The node localization problem in mobile sensor networks has received significant attention. Recently, particle filters adapted from robotics have produced good localization accuracies in conventional settings. In spite of these successes, state-of-the-art solutions suffer significantly when used in challenging indoor and mobile environments characterized by a high degree of radio signal irregularity. New solutions are needed to address these challenges. We propose a fuzzy logic-based approach for mobile node localization in challenging environments. Localization is formulated as a fuzzy multilateration problem. For sparse networks with few available anchors, we propose a fuzzy grid-prediction scheme. The fuzzy logic-based localization scheme is implemented in a simulator and compared to state-of-the-art solutions. Extensive simulation results demonstrate improvements in the localization accuracy from 20 to 40 percent when the radio irregularity is high. A hardware implementation running on Epic motes and transported by iRobot mobile hosts confirms simulation results and extends them to the real world.
机译：移动传感器网络中的节点定位问题已受到广泛关注。最近，采用机器人技术的粒子过滤器在常规设置中已经产生了良好的定位精度。尽管取得了这些成功，但是当用于具有高度无线电信号不规则性的挑战性室内和移动环境中时，最新的解决方案却遭受了巨大的损失。需要新的解决方案来应对这些挑战。我们提出了一种基于模糊逻辑的方法，用于在挑战性环境中进行移动节点定位。本地化被公式化为模糊多边问题。对于稀疏的，锚点很少的网络，我们提出了一种模糊网格预测方案。基于模糊逻辑的定位方案是在模拟器中实现的，并与最新解决方案进行比较。大量的仿真结果表明，当无线电不规则性很高时，定位精度将从20％提高到40％。在ipic节点上运行并由iRobot移动主机传输的硬件实现可确认模拟结果并将其扩展到现实世界。

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