TY - JOUR
T1 - Comparative study of supervised learning and metaheuristic algorithms for the development of bluetooth-based indoor localization mechanisms
AU - Lovon-Melgarejo, Jesus
AU - Castillo-Cara, Manuel
AU - Huarcaya-Canal, Oscar
AU - Orozco-Barbosa, Luis
AU - Garcia-Varea, Ismael
N1 - Publisher Copyright:
© 2013 IEEE.
Copyright:
Copyright 2019 Elsevier B.V., All rights reserved.
PY - 2019
Y1 - 2019
N2 - The development of the Internet of Things (IoT) benefits from 1) the connections between devices equipped with multiple sensors; 2) wireless networks and; 3) processing and analysis of the gathered data. The growing interest in the use of IoT technologies has led to the development of numerous diverse applications, many of which are based on the knowledge of the end user's location and profile. This paper investigates the characterization of Bluetooth signals behavior using 12 different supervised learning algorithms as a first step toward the development of fingerprint-based localization mechanisms. We then explore the use of metaheuristics to determine the best radio power transmission setting evaluated in terms of accuracy and mean error of the localization mechanism. We further tune-up the supervised algorithm hyperparameters. A comparative evaluation of the 12 supervised learning and two metaheuristics algorithms under two different system parameter settings provide valuable insights into the use and capabilities of the various algorithms on the development of indoor localization mechanisms.
AB - The development of the Internet of Things (IoT) benefits from 1) the connections between devices equipped with multiple sensors; 2) wireless networks and; 3) processing and analysis of the gathered data. The growing interest in the use of IoT technologies has led to the development of numerous diverse applications, many of which are based on the knowledge of the end user's location and profile. This paper investigates the characterization of Bluetooth signals behavior using 12 different supervised learning algorithms as a first step toward the development of fingerprint-based localization mechanisms. We then explore the use of metaheuristics to determine the best radio power transmission setting evaluated in terms of accuracy and mean error of the localization mechanism. We further tune-up the supervised algorithm hyperparameters. A comparative evaluation of the 12 supervised learning and two metaheuristics algorithms under two different system parameter settings provide valuable insights into the use and capabilities of the various algorithms on the development of indoor localization mechanisms.
KW - benchmark
KW - Bluetooth
KW - classification model
KW - fingerprinting
KW - Indoor positioning
KW - metaheuristic optimization algorithms
KW - multipath fading
KW - received signal strength indication
KW - signal processing
KW - transmission power
UR - http://www.scopus.com/inward/record.url?scp=85062689258&partnerID=8YFLogxK
U2 - 10.1109/ACCESS.2019.2899736
DO - 10.1109/ACCESS.2019.2899736
M3 - Artículo (Contribución a Revista)
AN - SCOPUS:85062689258
SN - 2169-3536
VL - 7
SP - 26123
EP - 26135
JO - IEEE Access
JF - IEEE Access
M1 - 8642816
ER -