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RSSI Fingerprinting Techniques for Indoor Localization Datasets

Conference paper
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Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1192)

Abstract

Indoor localization techniques using Received Signal Strength Indicator (RSSI) is attractive in the Internet of Things domain due to its simplicity and cost-effectiveness. However, there are many different approaches proposed in and there is not a common, widely acceptable solution in the research community. This is mainly due to the limited number of publicly available datasets and that the multi-effect signal phenomenon limits each dataset to its gathering testbed. In this paper, we tested several fingerprinting methods in a publicly available dataset and we compared them against the RSSI regression approach, which is considered as the most prominent one in certain domains, such as indoor and outdoor localization.

Keywords

RSSI Fingerprinting Localization Internet of Things 

Notes

Acknowledgement

This research has been co-financed by the European Union and Greek national funds through the Operational Program Competitiveness, Entrepreneurship and Innovation, under the call RESEARCH-CREATE-INNOVATE (project code: T1EDK-03487).

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Copyright information

© Springer Nature Switzerland AG 2021

Authors and Affiliations

  1. 1.Information Technologies InstituteCenter for Research and Technology HellasThessalonikiGreece

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