ItemConference Paper

A Comparative Benchmark of Deep Learning and Classical Models for BLE-Based Indoor Localization

Title
A Comparative Benchmark of Deep Learning and Classical Models for BLE-Based Indoor Localization
Creator
Narayanan, Dhanvanth; Rajoriya, Meenakshi Malviya; Rohini, V.
Description
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.
Source
Proceedings of the 2025 1st International Conference on Advances in Engineering and Computing Technologies for Sustainable Development, AECTSD 2025;
Date
01-01-2025
Publisher
Institute of Electrical and Electronics Engineers Inc.
Subject
Bluetooth Low Energy (BLE); Deep Learning; DNN; Indoor Positioning; K-Nearest Neighbors (KNN); Machine Learning; Performance Benchmark; Random Forest; RSSI
Coverage
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
Rights
Restricted Access; Hardcopy may be available in the library
Relation
ISBN: 979-833158156-5;
Format
online
Language
English
Type
Conference paper

Citation

Narayanan, Dhanvanth; Rajoriya, Meenakshi Malviya; Rohini, V., “A Comparative Benchmark of Deep Learning and Classical Models for BLE-Based Indoor Localization,” CHRIST (Deemed To Be University) Institutional Repository, accessed June 18, 2026, https://archives.christuniversity.in/items/show/25741.

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