当前位置: X-MOL 学术Phys. Commun. › 论文详情
Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)
An effective metaheuristic based node localization technique for wireless sensor networks enabled indoor communication
Physical Communication ( IF 2.0 ) Pub Date : 2021-07-06 , DOI: 10.1016/j.phycom.2021.101411
Pudi Sekhar 1 , E. Laxmi Lydia 2 , Mohamed Elhoseny 3, 4 , Marwan Al-Akaidi 3 , Mahmoud M. Selim 5 , K. Shankar 6
Affiliation  

Recently, wireless sensor network (WSN) enabled indoor communication provides an effective and flexible method for local area networks mainly in large buildings or in a group of several buildings. Node localization can be considered as a major process which helps to calculate the coordinate points of the unknown nodes with the assistance of known (anchor) nodes. Earlier studies have considered node localization problem as an NP hard problem. Several metaheuristics techniques are employed for resolving the localization problem in WSN that extremely decreases the localization error. This paper designs an effective metaheuristic-based Group Teaching Optimization Algorithm for Node Localization (GTOA-NL) technique for WSN. The goal of the GTOA-NL technique is to determine the position of the unknown nodes by the use of anchor nodes in the WSN with minimum localization error and maximum localization accuracy. The presented GTOA is stimulated from the group teaching strategy and it can be used for optimization process with no loss of generality. In order to guarantee the effective node localization performance of the presented GTOA-NL model, an extensive set of simulations were performed to highlight the supremacy of the GTOA-NL model. The obtained results have ensured the superior performance of the GTOA-NL model over the other compared methods under varying number of anchor nodes, ranging error, and transmission range.



中文翻译:

一种有效的基于元启发式的无线传感器网络节点定位技术支持室内通信

最近,支持无线传感器网络 (WSN) 的室内通信为主要位于大型建筑物或由多个建筑物组成的组中的局域网提供了一种有效且灵活的方法。节点定位可以被认为是在已知(锚)节点的帮助下帮助计算未知节点坐标点的主要过程。早期的研究已经将节点定位问题视为一个 NP 难题。几种元启发式技术被用来解决 WSN 中的定位问题,极大地减少了定位误差。本文设计了一种有效的基于元启发式的组教学优化算法,用于 WSN 节点定位(GTOA-NL)技术。GTOA-NL技术的目标是通过使用WSN中的锚节点以最小的定位误差和最大的定位精度确定未知节点的位置。所提出的 GTOA 是从小组教学策略中激发出来的,它可以在不失一般性的情况下用于优化过程。为了保证所提出的 GTOA-NL 模型的有效节点定位性能,进行了大量的模拟以突出 GTOA-NL 模型的优越性。获得的结果确保了 GTOA-NL 模型在不同锚节点数量、测距误差和传输范围下优于其他比较方法的性能。所提出的 GTOA 是从小组教学策略中激发出来的,它可以在不失一般性的情况下用于优化过程。为了保证所提出的 GTOA-NL 模型的有效节点定位性能,进行了大量的模拟以突出 GTOA-NL 模型的优越性。获得的结果确保了 GTOA-NL 模型在不同锚节点数量、测距误差和传输范围下优于其他比较方法的性能。所提出的 GTOA 是从小组教学策略中激发出来的,它可以在不失一般性的情况下用于优化过程。为了保证所提出的 GTOA-NL 模型的有效节点定位性能,进行了大量的模拟以突出 GTOA-NL 模型的优越性。获得的结果确保了 GTOA-NL 模型在不同锚节点数量、测距误差和传输范围下优于其他比较方法的性能。

更新日期:2021-07-18
down
wechat
bug