Research Article
Improving Indoor Localization Based on Artificial Neural Network Technology
@ARTICLE{10.4108/eai.31-10-2018.159633, author={Chi Han Chen and Rung Shiang Cheng}, title={Improving Indoor Localization Based on Artificial Neural Network Technology}, journal={EAI Endorsed Transactions on Internet of Things}, volume={4}, number={16}, publisher={EAI}, journal_a={IOT}, year={2018}, month={10}, keywords={Internet of things, Sensors, Smart devices, Machine-Type Communications, Prototypes and demonstrators, Embedded systems.}, doi={10.4108/eai.31-10-2018.159633} }- Chi Han Chen
Rung Shiang Cheng
Year: 2018
Improving Indoor Localization Based on Artificial Neural Network Technology
IOT
EAI
DOI: 10.4108/eai.31-10-2018.159633
Abstract
Wireless networks are ubiquitous nowadays and hence provide a promising approach for indoor localization. Many algorithms have been proposed for exploiting wireless signals for localization purposes. Among the methods, ANNbased methods have attracted particular attention due to their robustness in complex signal environments. However, their accuracy is still degraded by multi-path effects, signal fluctuations, and so on. Accordingly, this study commences by examining the effects of fluctuations in the received signal strength indicator (RSSI) measurement on the accuracy of an ANN-based localization algorithm. This study list some strategies and illustrate by simulation experiment. Based on the investigation results, a systematic methodology is proposed for improving the localization performance by increasing the number of APs. The feasibility of the proposed method is demonstrated by means of numerical simulations.
Copyright © 2018 Chi Han Chen et al., licensed to EAI. This is an open access article distributed under the terms of the Creative Commons Attribution licence (http://creativecommons.org/licenses/by/3.0/), which permits unlimited use, distribution and reproduction in any medium so long as the original work is properly cited.
