A deep learning Lora-based indoor localization technique - Aqqad, Kamilia; Aqqad, Karmel; Khdeir, Naeema; Zabade, Raha; Ghanem, Wasel; Lwas, Ali Kadhim; Al-Rimawi, Ashraf; Habaebi, Mohamed Hadi; Zyoud, Alhareth Mohammed (IEEE, 2026)
Indoor localization is crucial for many applications where Global Positioning System (GPS) signals are frequently poor or unreliable, such as asset tracking and interior navigation. This study uses deep learning algorithms and the benefits of long-range wireless communication offered by Long Range (LoRa) to produce an accurate indoor locating system. The initial step towards constructing a wireless communication network is positioning LoRa-based devices at specified locations. A dataset is produced by merging the signal strength measurements acquired from each of these LoRa devices. Each location in the data set is within an environment, and measurements of the signal strength from LoRa devices related to those locations are included. The collected dataset is used to train the deep learning model. To increase accuracy, techniques like data augmentation and cross-validation were included. The experiment's results demonstrate the effectiveness of the deep learning-based LoRa indoor localization technique. Among the many possible applications for the suggested method are location-based services, asset monitoring, and precise indoor positioning.