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General Information
    • ISSN: 2010-3751
    • Frequency: Bimonthly (2012-2016); Quarterly (Since 2017)
    • DOI: 10.18178/IJFCC
    • Editor-in-Chief: Prof. Mohamed Othman
    • Executive Editor: Ms. Cherry L. Chan
    • Abstracting/ Indexing: Google Scholar,  Crossref, Electronic Journals LibraryEI (INSPEC, IET), etc.
    • E-mail:  ijfcc@ejournal.net 
Editor-in-chief
Prof. Mohamed Othman
Department of Communication Technology and Network Universiti Putra Malaysia, Malaysia
It is my honor to be the editor-in-chief of IJFCC. The journal publishes good papers in the field of future computer and communication. Hopefully, IJFCC will become a recognized journal among the readers in the filed of future computer and communication.
IJFCC 2018 Vol.7(4): 79-85 ISSN: 2010-3751
DOI: 10.18178/ijfcc.2018.7.4.525

Intelligent RF-Based Indoor Localization through RSSI of LoRa Communication Technology

Sittikorn Muekdang and Wimol San-Um
Abstract—Requirements on high-quality indoor localization approaches and the increase in ubiquitous computing and context-dependent have led to an emphasis on a continuous search for promising localization technologies and techniques. Typical RF-Based localization technologies such as Cellular, RFID, Bluetooth, Wi-Fi, Zigbee, and UWB have been widespread studied over the past decades. Recently, LoRa communication technology has suggested as a potential alternative to those of exiting wireless communication standards with low power consumption and low implementation costs. This paper therefore presents an indoor localization technique through the use of Received Signal Strength Indicator (RSSI) of LoRa Technology. The LoRa chip from SEMTECH was implemented on a compact board with built-in antenna. The Arduino microcontroller was employed as a core processor with a step-down switching regulator. Five sets of LoRa nodes were implemented and four of which were utilized as statistic nodes, radiating a signal power from 5- meter high from the floor. The receiving node is placed in a particular coordinate on the floor. The RSS values were employed as inputs for Artificial Neural Network (ANN) for estimation of the coordinate of the receiving node. The accuracy was approximately 95%. The result provides satisfactory accuracy with cost-effective and low-power operation as for an alternative for large scales deployments of indoor localization.

Index Terms—Indoor localization, LoRa technology, received signal strength indicator, artificial neural network.

The authors are with Center of Excellence in Intelligent Systems Integration (CoE-ISI) of Engineering, Thai-Nichi Institute of Technology (TNI), Thailand, 10250 (e-mail: mu.sittikorn_st@tni.ac.th, wimol@tni.ac.th).

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Cite: Sittikorn Muekdang and Wimol San-Um, "Intelligent RF-Based Indoor Localization through RSSI of LoRa Communication Technology," International Journal of Future Computer and Communication vol. 7, no.4, pp. 79-85, 2018.

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