Room-Level Indoor Localization with Artificial Neural Networks

Autor:	Mehmet Önder Efe, Ahmet Serdar Karadeniz
Rok vydání:	2019
Předmět:	Artificial neural network business.industry Computer science Reliability (computer networking) SIGNAL (programming language) Stability (learning theory) Process (computing) Tracking system Sigmoid function computer.software_genre Home automation Data mining business computer
Zdroj:	Pattern Recognition and Artificial Intelligence ISBN: 9783030375478 MedPRAI
DOI:	10.1007/978-3-030-37548-5_1
Popis:	Indoor localization system determines the location of the users or some assets in indoor environments. There are important applications of indoor localization including smart home systems, indoor navigation and tracking systems. In this work, a reliable neural network model is developed for localizing users in room level. Model is based on Wi-Fi signals received by the users’ devices at different rooms from various Wi-Fi access points. A neural network with two hidden layers with sigmoid activation functions is trained with back-propagation optimizing collected signal data. Some of the signals are set to 0 during the training process, which gives significant stability to the model under the conditions where some of the data required for prediction are not available. An additional dataset is collected for the evaluation in addition to the existing datasets. Performance of the model on the existing datasets as well as the new collected dataset is discussed and evaluated. Results are promising in terms of reliability and accuracy.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::bf692cd9b171751210e613442806dabf https://doi.org/10.1007/978-3-030-37548-5_1 Zobrazit plný text záznamu