Multi-Layer Perceptron Decomposition Architecture for Mobile IoT Indoor Positioning
| Author | Çakan E.; Şahin A.; Nakip M.; Rodoplu V. |
|---|---|
| Title | Multi-Layer Perceptron Decomposition Architecture for Mobile IoT Indoor Positioning |
| Journal | 7th IEEE World Forum on the Internet of Things |
| Year | 2021 |
| Status | Published |
| DOI | 10.1109/WF-IoT51360.2021.9595282 |
| Abstract | <p>We develop a Multi-Layer Perceptron (MLP) Decomposition<br /> architecture for mobile Internet Things (IoT) indoor<br /> positioning. We demonstrate the performance of our architecture<br /> on an indoor system that utilizes ultra-wideband (UWB) positioning.<br /> Our architecture outperforms the following benchmark<br /> processing techniques on the same data: MLP, Linear Regression,<br /> Ridge Regression, Support Vector Regression, and the Least<br /> Squares Method for indoor positioning. The results show that our<br /> architecture can significantly advance the positioning accuracy<br /> of indoor positioning systems and enable indoor applications<br /> such as navigation, proximity marketing, asset tracking, collision<br /> avoidance, and social distancing.</p> |
| Publisher | IEEE |
| CONFERENCE_MLP_Decomposition_WFIoT_2021.pdf |