It is important for wireless sensor networks (WSNs) to determine whether hubs can be restricted, called localizability recognition. This progression is important for confining hubs, achieving minimal organizations of effort and distinguishing critical in field-based applications. Because of their high calculation and communication costs, centralized graph algorithms are meaningless to an asset-restricted WSN, whereas distributed methodologies can skip a large number of hypothetically locatable hubs in a resource-limited WSN. In this document we are proposing a efficient and successful distributed methodology to solve that address of issue. In addition, we demonstrate our calculation accuracy and analyze the reasons why our calculation will discover increasingly localizable hubs while requiring less established area hubs. The paper introduces a new RSSI-based localization approach triangle as well as centroid placement, utilizing triangle as well as centroid approach to minimize RSSI measurement error. Modelling simulations show that this algorithm can significantly increase the precision of the location compared to the trilateration.

对于无线传感器网络 (WSN) 来说，确定是否可以限制集线器非常重要，称为可定位性识别。这一进展对于限制集线器、实现最小的工作组织和区分基于现场的应用程序的关键非常重要。由于其高计算和通信成本，集中式图算法对资产受限的 WSN 毫无意义，而分布式方法可以跳过资源有限的 WSN 中大量假设可定位的集线器。在本文档中，我们提出了一种有效且成功的分布式方法来解决该问题。此外，我们展示了我们的计算准确性，并分析了为什么我们的计算会发现越来越本地化的枢纽，同时需要较少建立的区域枢纽。本文介绍了一种新的基于RSSI的定位方法三角形以及质心放置，利用三角形和质心方法来最小化RSSI测量误差。建模仿真表明，与三边测量相比，该算法可以显着提高定位精度。