Building RSSI-based Indoor Positioning Fingerprint Maps using Android-based Coordination
| dc.contributor.author | Nakpaen, Lapat | |
| dc.contributor.author | Wongsekleo, Prab | |
| dc.contributor.author | Cherntanomwong, Panarat | |
| dc.contributor.author | Pattiyanon, Charnon | |
| dc.date.accessioned | 2026-08-06T10:43:13Z | |
| dc.date.available | 2026-08-06T10:43:13Z | |
| dc.date.issued | 2024-01-01 | |
| dc.description.abstract | Indoor positioning systems (IPS) have emerged as a critical technology for location-based applications. Developing IPS system is challenging since technologies for outdoor positioning seem to be limited in indoor environment. Fingerprinting is a technique to build an offline map and compare the current location with it. While fingerprinting remains a popular technique for indoor positioning, its reliance on extensive manual data collection is a significant challenge. These data points can be the Received Signal Strength Indicator (RSSI) of the Wi-Fi signal or signals from the triangulation of Bluetooth/cellular beacons. However, the conventional grid-based fingerprint technique is facing challenges when the target area is being large. This research proposes an automated approach to gathering Wi-Fi RSSI data for building indoor positioning maps using the Android-based triangulated coordination. Our method demonstrates a substantial reduction in data collection time (79%) compared to traditional grid-based techniques. The resulting dataset effectively supports machine learning models for indoor positioning, achieving a Mean Distance Error (MDE) of less than 2 meters different. | |
| dc.identifier.citation | 19th International Joint Symposium on Artificial Intelligence and Natural Language Processing Isai Nlp 2024, 2024 | |
| dc.identifier.doi | 10.1109/iSAI-NLP64410.2024.10799385 | |
| dc.identifier.other | 2-s2.0-85216548424 | |
| dc.identifier.uri | https://dspace.kmitl.ac.th/handle/123456789/14881 | |
| dc.source | 19th International Joint Symposium on Artificial Intelligence and Natural Language Processing Isai Nlp 2024 | |
| dc.subject | and Android Application | |
| dc.subject | Indoor Positioning System (IPS) | |
| dc.subject | Received Signal Strength Indicator (RSSI) | |
| dc.subject | Triangulated Coordination | |
| dc.title | Building RSSI-based Indoor Positioning Fingerprint Maps using Android-based Coordination | |
| dc.type | Conference Paper |