Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Conference Paper Faculty Publications- Conference Papers Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Narayanan, Dhanvanth; Rajoriya, Meenakshi Malviya; Rohini, V. Title A name given to the resource A Comparative Benchmark of Deep Learning and Classical Models for BLE-Based Indoor Localization Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2025 Source A related resource from which the described resource is derived Proceedings of the 2025 1st International Conference on Advances in Engineering and Computing Technologies for Sustainable Development, AECTSD 2025; Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/AECTSD65988.2025.11410887" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/AECTSD65988.2025.11410887</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105036564268?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105036564268?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Narayanan D., Christ University, Department Of Computer Science, Bengaluru, India; Rajoriya M.M., Christ University, Department Of Computer Science, Bengaluru, India; Rohini V., Christ University, Department Of Computer Science, Bengaluru, India Description An account of the resource Bluetooth Low Energy (BLE)-based indoor positioning has gained attention as a cost-effective solution for environments where GPS signals are unreliable. Despite advances in ML and DL techniques, few standardized benchmarks exist for comparing models under uniform conditions. This study evaluates seven models - K-Nearest Neighbor, Random Forest, Deep Neural Network, 1D CNN, Long Short-Term Memory, Bi-LSTM, and Transformer - on a publicly available dataset collected across multiple building floors. A preprocessing pipeline was applied to address missing values, refine RSSI signals, and generate temporal features. Performance was assessed using both accuracy metrics (MAE, RMSE) and efficiency metrics such as processing time, and model size. Results show that KNN, Random Forest, and DNN consistently outperformed complex sequential and attention-based models, achieving RMSE as low as 1.297 m. These findings suggest that simpler architectures align more effectively with BLE RSSI data than deeper models. This study establishes a benchmark that can support future work in developing efficient, lightweight, and generalizable indoor positioning systems. 2025 IEEE. Subject The topic of the resource Bluetooth Low Energy (BLE); Deep Learning; DNN; Indoor Positioning; K-Nearest Neighbors (KNN); Machine Learning; Performance Benchmark; Random Forest; RSSI Publisher An entity responsible for making the resource available Institute of Electrical and Electronics Engineers Inc. Relation A related resource ISBN: 979-833158156-5; Language A language of the resource English Type The nature or genre of the resource Conference paper Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online