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Erschienen in: Wireless Personal Communications 4/2019

17.05.2019

Trajectory-Based User Encounter Prediction Over Wireless Sensor Networks

verfasst von: Meng Tong, Yu Tao, Yuanxing Zhang, Kaigui Bian, Wei Yan

Erschienen in: Wireless Personal Communications | Ausgabe 4/2019

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Abstract

People or friends may encounter with each other offline, when they have a location proximity. With the rapid development of the wireless sensor network, smart city applications can leverage the sensed data of people’s mobility or trajectory to predict their future encounter opportunity and then arrange their offline activities (e.g., meeting, travel) accordingly. This paper studies the encounter prediction problem of mobile users by mining the similarity between their sensed mobile trajectories. We define the similarity of two mobile trajectories both temporally and spatially, and then propose two approaches, namely a probabilistic similarity maximization algorithm and a machine leaning based prediction algorithm, for addressing the encounter prediction problem. Results over a real-world social network dataset show that the proposed recurrent neural network based model can predict the encounter of two users precisely, and it outperforms the probabilistic algorithm and other algorithms, in terms of the precision and F1 score.

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Metadaten
Titel
Trajectory-Based User Encounter Prediction Over Wireless Sensor Networks
verfasst von
Meng Tong
Yu Tao
Yuanxing Zhang
Kaigui Bian
Wei Yan
Publikationsdatum
17.05.2019
Verlag
Springer US
Erschienen in
Wireless Personal Communications / Ausgabe 4/2019
Print ISSN: 0929-6212
Elektronische ISSN: 1572-834X
DOI
https://doi.org/10.1007/s11277-019-06367-1

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