Hybrid Learning for Mobile Ad-Hoc Distancing/Positioning Using Bluetooth Low Energy

• Published in: IEEE internet of things journal Vol. 10; no. 14; pp. 12293 - 12307
• Main Authors: Ho, Yik Him, Liu, Yunfei, Zhang, Caiqi, Sartayeva, Yerkezhan, Chan, Henry C. B.
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 15-07-2023; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Accuracy; Blended learning; Bluetooth; Bluetooth low energy (BLE); COVID-19; Data models; Data processing; Estimation; Genetic algorithms; Hybrid learning; Iterative algorithms; Iterative methods; Machine learning; mobile ad-hoc distancing (MAD)/positioning (MAP); Propagation losses; Smartphones; social distancing; Supervised learning; Unsupervised learning
• ISSN: 2327-4662; 2327-4662
• DOI: 10.1109/JIOT.2023.3247299

With the advent of Bluetooth low-energy (BLE)-enabled smartphones, there has been considerable interest in investigating BLE-based distancing/positioning methods (e.g., for social distancing applications). In this article, we present a novel hybrid learning method to support mobile ad-hoc distancing (MAD)/positioning (MAP) using BLE-enabled smartphones. Compared to traditional BLE-based distancing/positioning methods, the hybrid learning method provides the following unique features and contributions. First, it combines unsupervised learning, supervised learning, and genetic algorithms (GAs) for enhancing distance estimation accuracy. Second, unsupervised learning is employed to identify three pseudo channels/clusters for enhanced RSSI data processing. Third, its underlying mechanism is based on a new pattern-inspired approach to enhance the machine learning process. Fourth, it provides a flagging mechanism to alert users if a predicted distance is accurate or not. Fifth, it provides a model aggregation scheme with an innovative 2-D GA to aggregate the distance estimation results of different machine learning models. As an application of hybrid learning for distance estimation, we also present a new MAP scenario with an iterative algorithm to estimate mobile positions in an ad-hoc environment. Experimental results show the effectiveness of the hybrid learning method. In particular, hybrid learning without flagging and with flagging outperforms the baseline by 57% and 65%, respectively, in terms of mean absolute error. By means of model aggregation, a further 4% improvement can be realized. The hybrid learning approach can also be applied to previous work to enhance distance estimation accuracy and provide valuable insights for further research.