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Where to go? Predicting next location in IoT environment

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Abstract

Next location prediction has aroused great interests in the era of internet of things (IoT). With the ubiquitous deployment of sensor devices, e.g., GPS and Wi-Fi, IoT environment offers new opportunities for proactively analyzing human mobility patterns and predicting user’s future visit in low cost, no matter outdoor and indoor. In this paper, we consider the problem of next location prediction in IoT environment via a session-based manner. We suggest that user’s future intention in each session can be better inferred for more accurate prediction if patterns hidden inside both trajectory and signal strength sequences collected from IoT devices can be jointly modeled, which however existing state-of-the-art methods have rarely addressed. To this end, we propose a trajectory and sIgnal sequence (TSIS) model, where the trajectory transition regularities and signal temporal dynamics are jointly embedded in a neural network based model. Specifically, we employ gated recurrent unit (GRU) for capturing the temporal dynamics in the multivariate signal strength sequence. Moreover, we adapt gated graph neural networks (gated GNNs) on location transition graphs to explicitly model the transition patterns of trajectories. Finally, both the low-dimensional representations learned from trajectory and signal sequence are jointly optimized to construct a session embedding, which is further employed to predict the next location. Extensive experiments on two real-world Wi-Fi based mobility datasets demonstrate that TSIS is effective and robust for next location prediction compared with other competitive baselines.

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Acknowledgements

This work was supported by the National Natural Science Foundation of China (NSFC) (Grant Nos. 71701007 and 71531001).

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Authors and Affiliations

  1. School of Economics and Management, Beihang University, Beijing, 100191, China

    Hao Lin, Guannan Liu, Fengzhi Li & Yuan Zuo

Authors
  1. Hao Lin
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  2. Guannan Liu
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  3. Fengzhi Li
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  4. Yuan Zuo
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Corresponding author

Correspondence to Guannan Liu.

Additional information

Hao Lin received the Bachelor’s degree in management information systems from Beihang University, China in 2013. He is currently working toward the PhD degree in the School of Economics and Management at Beihang University, China. His research interests generally lie in the areas of data mining and machine learning, with special interests in user modeling and heterogeneous data fusion.

Guannan Liu is currently an assistant professor in the Department of Information Systems with Beihang University, China. He received the PhD degree from Tsinghua University, China. His research interests include data mining, social networks, and business intelligence. His work has been published in the journal of IEEE TKDE

ACM TKDD, ACM TIST, Decision Support Systems, Neurocomputing, and also in the conference proceedings such as KDD, ICDM, SDM, etc.

Fengzhi Li received his Bachelor’s degree in applied physics from Beihang University, China in 2018. He is currently a MS candidate in the School of Economics and Management at Beihang University, China. His research interests include information extraction, topic model and multi-label learning.

Yuan Zuo received his PhD degree from Beihang University, China in 2017. He is currently a post-doctor in Information Systems Department of Beihang University, China. His research interests include topic modeling and social computing.

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Lin, H., Liu, G., Li, F. et al. Where to go? Predicting next location in IoT environment. Front. Comput. Sci. 15, 151306 (2021). https://doi.org/10.1007/s11704-019-9118-9

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  • DOI: https://doi.org/10.1007/s11704-019-9118-9

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