Lukito, Yuan and Chrismanto, Antonius Rachmat (2017) Recurrent Neural Networks Model for WiFi-based Indoor Positioning System. In: Proceedings of 2017 International Conference on Smart Cities, Automation & Intelligent Computing Systems (ICON-SONICS 2017), 08 November 207, Yogyakarta.
Full text not available from this repository.Abstract
This research focus on the implementation of recurrent neural networks (RNN) model for indoor positioning system (IPS). Unlike global positioning system (GPS), IPS is used in closed structures such as hospitals, museums, shopping centers, office buildings, and warehouses. Positioning system is a key aspect in IPS. We propose, implemented, and evaluated an RNN model for positioning system. We used Wi-Fi-based IPS dataset from our previous research and made some comparison of RNN model performance with other methods. From the model evaluation results, we can conclude that RNN model is suitable for Wi-Fi-based IPS. It also produces generally higher accuracy compared with multi-layer perceptron model (MLP), Naïve Bayes, J48, and SVM. The RNN model training process still needs some tweaking on the parameters used in training.
| Item Type: | Conference or Workshop Item (Paper) |
|---|---|
| Keywords: | recurrent neural networks; indoor positioning system; wi-fi. |
| Subjects: | 500 Science and Mathematic > 530 Physics > 537 Electricity and Electronics 600 Technology (Applied Sciences) > 600 Technology > 600 Technology 600 Technology (Applied Sciences) > 620 Engineering > 620 Engineering and Applied Operations |
| Divisions: | Universitas Multimedia Nusantara |
| Depositing User: | Administrator UMN Library |
| Date Deposited: | 01 Mar 2018 04:54 |
| Last Modified: | 08 Feb 2022 03:29 |
| URI: | https://kc.umn.ac.id/id/eprint/2793 |
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