Publication: Ble-based indoor localization with machine learning
| datacite.subject.fos | oecd::Engineering and technology::Electrical engineering, Electronic engineering, Information engineering | |
| dc.contributor.author | Hor, Chia Wei | |
| dc.date.accessioned | 2025-11-27T06:53:40Z | |
| dc.date.available | 2025-11-27T06:53:40Z | |
| dc.date.issued | 2024-08-01 | |
| dc.description.abstract | Indoor localization has become a crucial aspect across various sectors, from enhancing user experiences in retail environments to optimizing logistics in industrial settings. As machine learning technology progresses, the efficacy of indoor localization systems has substantially improved. Despite these advancements, Bluetooth Low Energy (BLE)-based localization systems present ongoing opportunities for further enhancement. This thesis introduces a Temporal Convolutional Network (TCN) as a novel classification model for indoor localization, striking a balance between efficiency and accuracy, thereby positioning it as an ideal solution for indoor localization tasks. The methodology involves segmenting a 10.72 meters by 6.3 meters indoor area into a grid on the (x, y) plane with cells measuring 0.8 meters each. Received Signal Strength Indicator (RSSI) data collected from three strategically placed BLE beacons within this grid were processed in MATLAB, utilizing both moving average and median filters. Subsequently, the data was subjected to machine learning analysis. Comparative evaluations of the TCN model against traditional classifiers, including Decision Trees (DT), Support Vector Machines (SVM), and k-Nearest Neighbors (kNN), underscore the superior performance of the TCN. Notably, TCN outperforms the kNN model by 10% and exceeds the nearest reference-based fingerprinting method by 37.77% in accuracy. These results affirm the effectiveness of the proposed TCN approach in enhancing indoor localization systems. | |
| dc.identifier.uri | https://erepo.usm.my/handle/123456789/23224 | |
| dc.language.iso | en | |
| dc.title | Ble-based indoor localization with machine learning | |
| dc.type | Resource Types::text::thesis::master thesis | |
| dspace.entity.type | Publication | |
| oairecerif.author.affiliation | Universiti Sains Malaysia |