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Performance improvement of connectivity-based localization using iterative learning

机译:使用迭代学习提高基于连接的本地化的性能

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Resource constraints of wireless ad-hoc and senior networks prohibit range-based localization schemes which squire specialized hardware for high location accuracy. On he other hand, cost effective range-free schemes which depend only on connectivity information offer lower accuracy and grant heir applicability only to large-scale networks. This paper propose an efficient localization scheme which applies received signal strength (RSS) measurements to improve the localization accuracy of range-free schemes without any extra hardware support and to solve the applicability problem. Locations of the lodes are estimated with the proposed iterative location learning algorithm which utilizes both connectivity information and RSS-based distance information between the nodes to get more precise location estimation. To make our proposed scheme applicable or both small and large scale networks, we configure the connectivity information using the available RSS measurements and a predefined RSS threshold. Optimal RSS threshold value that minimizes the error for a particular network to be localized is derived as a function of the total number of nodes and the network size. The accuracy of the proposed scheme is further improved by introducing the use of regulated hop-count values. Experimental results show that our proposed scheme significantly improves the localization accuracy and works well under different network configurations.
机译:无线自组织网络和高级网络的资源限制禁止基于范围的本地化方案,该方案要求专用硬件以实现较高的定位精度。另一方面,仅依赖于连通性信息的具有成本效益的无范围方案提供了较低的准确性,并且仅继承人适用于大规模网络。本文提出了一种有效的定位方案,该方案应用接收信号强度(RSS)测量来提高无范围方案的定位精度,而无需任何额外的硬件支持,并解决了适用性问题。利用所提出的迭代位置学习算法来估计矿点的位置,该算法利用节点之间的连通性信息和基于RSS的距离信息来获得更精确的位置估计。为了使我们提出的方案适用于小型或大型网络,我们使用可用的RSS测量和预定义的RSS阈值来配置连接信息。根据节点总数和网络大小得出最佳RSS阈值,该阈值使要定位的特定网络的错误最小化。通过引入经过调节的跳数计数值,可以进一步提高所提出方案的准确性。实验结果表明,本文提出的方案可以显着提高定位精度,并且在不同的网络配置下都能很好地工作。

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