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 paper, 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 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 two-dimensional genetic algorithm 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 outperform the baseline by 57 and 65 percent respectively in terms of mean absolute error. By means of model aggregation, a further 4 percent 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.
In recent years, there has been considerable interest in indoor positioning with the advent of smartphones. Conventional indoor positioning methods are mostly infrastructure-based and non-collaborative. With the recent development of Ultra-WideBand (UWB) technologies, highly accurate distance and orientation detection have become available for supporting collaborative positioning, a new positioning paradigm. Hence there is a strong need to study collaborative positioning, which is referred to in this paper as mobile ad-hoc positioning (MAP). To contribute to the development of MAP, we present novel positioning vectors with the potential to tackle many related collaborative positioning problems and open an interesting area of research. Our contributions are outlined as follows. First, we present the concept of positioning vectors with the foundational features. Second, we present both experimental and simulation results, illustrating the use of positioning vectors. In particular, we discuss a COVID-19 related case study on social distancing. Last but not least, we discuss the future research directions of positioning vectors. In summary, this paper provides valuable insights into the development of MAP in general and positioning vectors in particular.
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