RSSI-based Localization in Wireless Sensor Networks Using Regression Tree

被引：0

作者：

Ahmadi, Hanen ^{[1
,2
]}

Bouallegue, Ridha ^{[1
]}

机构：

[1] Univ Carthage, SupCom, InnovCom, Tunis, Tunisia

[2] Univ Tunis El Manar, Ecole Natl Ingn Tunis, Tunis, Tunisia

来源：

2015 INTERNATIONAL WIRELESS COMMUNICATIONS & MOBILE COMPUTING CONFERENCE (IWCMC) | 2015年

关键词：

WSN; Localization; Machine Learning; RSSI; Least Square Regression Tree;

D O I：

暂无

中图分类号：

TP3 [计算技术、计算机技术];

学科分类号：

0812 ;

摘要：

Wireless sensor networks are different from other networks; therefore it is necessary to use innovative techniques to solve some issues. Localization is a significant area of research in wireless sensor networks due to its various applications. This paper proposes and evaluates a Received Signal Strength-based localization algorithm using Regression Tree by comparing its performance with Least Squares Support Vector Regression and Multi Layers Perceptron Neural Network. The evaluation considers the localization error and the complexity of the algorithm. Simulations show that Regression Tree method is simple and efficient, even when using a small number of anchor nodes.

引用

页码：1548 / 1553

页数：6

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