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Open AccessArticle

Indoor Location Prediction Method for Shopping Malls Based on Location Sequence Similarity

by Peixiao Wang 1, Sheng Wu 1, Hengcai Zhang 2,3,* and Feng Lu 2,3
1
The Academy of Digital China, Fuzhou University, Fuzhou 350002, China
2
State Key Laboratory of Resources and Environmental Information System, IGSNRR, CAS, Beijing 100101, China
3
Fujian Collaborative Innovation Center for Big Data Applications in Governments, Fuzhou 350002, China
*
Author to whom correspondence should be addressed.
ISPRS Int. J. Geo-Inf. 2019, 8(11), 517; https://doi.org/10.3390/ijgi8110517
Received: 7 September 2019 / Revised: 4 November 2019 / Accepted: 13 November 2019 / Published: 15 November 2019
Fast and accurate indoor location prediction plays an important part in indoor location services. This work proposes an indoor location prediction framework named Indoor-WhereNext. First, a novel algorithm, “indoor spatiotemporal density-based spatial clustering of applications with noise” (Indoor-STDBSCAN), is proposed to detect the stay points in an indoor trajectory and convert them into a location sequence. Then, a spatial-semantic similarity (SSS) method for measuring the similarity between location sequences is defined. SSS comprehensively considers the spatial and semantic similarities between location sequences. Finally, a clustering algorithm is used to obtain similarity user groups based on SSS. These groups are used to train different prediction models to achieve improved results. Extensive experiments were conducted using real indoor Wi-Fi positioning datasets collected in a shopping mall. The results show that the Indoor-WhereNext model markedly outperforms the three existing baseline methods in terms of prediction accuracy and precision. View Full-Text
Keywords: indoor location prediction; sequence similarity; similar user clustering; indoor movement trajectory indoor location prediction; sequence similarity; similar user clustering; indoor movement trajectory
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Wang, P.; Wu, S.; Zhang, H.; Lu, F. Indoor Location Prediction Method for Shopping Malls Based on Location Sequence Similarity. ISPRS Int. J. Geo-Inf. 2019, 8, 517.

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