Hybrid LoRaWAN Localization using Ensemble Learning

被引：15

作者：

Pandangan, Zaeefa A. ^{[1
]}

Talampas, Marc Caesar R. ^{[1
]}

机构：

[1] Univ Philippines Diliman, Elect & Elect Engn Inst, Quezon City, Philippines

来源：

2020 GLOBAL INTERNET OF THINGS SUMMIT (GIOTS) | 2020年

关键词：

IoT; Localization; LPWAN; LoRaWAN; Hybrid; Data Fusion; Fingerprinting; Machine Learning; Ensemble Learning; kNN; Random Forest;

D O I：

10.1109/giots49054.2020.9119520

中图分类号：

TP301 [理论、方法];

学科分类号：

081202 ;

摘要：

Localization is an integral element in any Internet-of-Things (IoT) setup. Low Power Wide Area Network (LPWAN) technology such as LoRaWAN enables long-range communication with low-power consumption, and so, localization methods for outdoor applications have grown increasingly popular. Various ranging and fingerprinting techniques have been studied so far; however, fusing signal strength with time information remain to be an unexplored approach. In this study, an ensemble learning-based outdoor positioning algorithm utilizing hybrid data is designed with the goal of improving accuracy. An open access LoRaWAN dataset that offers both signal strength measurements and nanosecond precise timestamps is used and is split to train, evaluate, and test the algorithm that incorporates k Nearest Neighbors (kNN) method with the Random Forest Regressor (RFR). The kNN-RFR algorithm configured with the best parameters achieved a mean error of 332.63 meters and a median error of 193.63 meters.

引用

页数：6

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