Mobile Crowdsourced Sensors Selection for Journey Services

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11236)


We propose a mobile crowdsourced sensors selection approach to improve the journey planning service especially in areas where no wireless or vehicular sensors are available. We develop a location estimation model of journey services based on an unsupervised learning model to select and cluster the right mobile crowdsourced sensors that are accurately mapped to the right journey service. In our model, the mobile crowdsourced sensors trajectories are clustered based on common features such as speed and direction. Experimental results demonstrate that the proposed framework is efficient in selecting the right crowdsourced sensors.


IoT Travel planning service Spatiotemporal data Crowdsourcing Sensors selection Unsupervised learning 



This research was made possible by NPRP 9-224-1-049 grant from the Qatar National Research Fund (a member of The Qatar Foundation) and DP160100149 and LE180100158 grants from Australian Research Council. The statements made herein are solely the responsibility of the authors.


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Authors and Affiliations

  1. 1.Department of Computer Science and Engineering, College of EngineeringQatar UniversityDohaQatar
  2. 2.School of Information TechnologiesUniversity of SydneySydneyAustralia

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