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Abstract
The uncertainty of indoor Wi-Fi positioning is susceptible to many factors, such as sensor distribution, the internal environment (e.g., of a shopping mall), differences between receivers, and the flow of people. In this paper, an indoor pedestrian trajectory pattern mining approach for the assessment of the error and accuracy of indoor Wi-Fi positioning is proposed. First, the stay points of the customer were extracted from the pedestrian trajectories based on the spatiotemporal staying patterns of the customers in a shopping mall. Second, the drift points were distinguished from the stay points through analysis of noncustomer behavior patterns. Finally, the drift points were presented to calculate the errors in the pedestrian trajectories for the accuracy assessment of the indoor Wi-Fi positioning system. A one-month indoor pedestrian trajectories dataset from the Xinxiang Baolong shopping mall in Henan Province, China, was used for the assessment of the error and accuracy values with the proposed approach. The experimental results were verified by incorporating the distribution of the AP sensors. The proposed approach using big data pattern mining can explore the error distribution of indoor positioning systems, which can provide strong support for improving indoor positioning accuracy in the future.
Highlights
The modern rhythm of life means that most human activities, whether work, entertainment or rest, occur indoors
We proposed an original method for evaluating the uncertainty range of indoor positioning based on the Wi-Fi access points deployed in a shopping mall
We provide a clear definition of stay points and drift points in indoor positioning data
Summary
The modern rhythm of life means that most human activities, whether work, entertainment or rest, occur indoors. GNSS trajectories are used for urban traffic analyses, pattern analyses of crowd motion and recommending locations to friends [52] These spatiotemporal data mining methods can be implemented for the trajectory data from indoor Wi-Fi positioning systems. Our paper introduces a GIS-based spatiotemporal data-processing method for very large amounts of indoor trajectory data to extract the stay patterns of pedestrians for the accuracy assessment of indoor Wi-Fi positioning systems. The phenomenon of positioning drift was found in the indoor pedestrian trajectory data in a shopping mall, and we used this phenomenon to evaluate the errors in the indoor Wi-Fi positioning system. The results show that the proposed approach using big data pattern mining can explore the error distribution of indoor positioning systems, which can provide strong support for improving indoor positioning accuracy in the future. Their smartphones interact with the Wi-Fi AP sensors, and the server of the
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