Exploiting machine learning strategies and RSSI for localization in wireless sensor networks: A survey

被引：0
作者：
Ahmadi, Hanen [1 ,2 ]
Bouallegue, Ridha [1 ]
机构：
[1] Univ Tunis El Manar, Univ Carthage, Supcom, InnovCOM, Tunis, Tunisia
[2] Univ Carthage, ELEDIA InnovCOM, ELEDIA Res Ctr, Tunis, Tunisia
来源：
2017 13TH INTERNATIONAL WIRELESS COMMUNICATIONS AND MOBILE COMPUTING CONFERENCE (IWCMC) | 2017年
关键词：
Localization; WSN; Machine learning; RSSI; Fingerprinting; anchor-based; INDOOR LOCALIZATION;
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：
In recent years, there has been a growing interest in the wireless sensor networks (WSN) for a variety of applications such as the localization and real time positioning. Different approaches based on artificial intelligence are applied to solve common issues in WSN and improve network performance. This paper addresses a survey on machine learning techniques for localization in WSNs using Received Signal Strength Indicator.
引用
收藏
页码：1150 / 1154
页数：5
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