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Mobile crowdsourced sensors selection for journey services

المؤلفBen Said, Ahmed
المؤلفErradi, Abdelkarim
المؤلفGharia Neiat, Azadeh
المؤلفBouguettaya, Athman
تاريخ الإتاحة2023-04-10T09:10:03Z
تاريخ النشر2018
اسم المنشورLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
المصدرScopus
معرّف المصادر الموحدhttp://dx.doi.org/10.1007/978-3-030-03596-9_33
معرّف المصادر الموحدhttp://hdl.handle.net/10576/41792
الملخصWe 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. Springer Nature Switzerland AG 2018.
راعي المشروعAcknowledgment. 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.
اللغةen
الناشرSpringer Verlag
الموضوعCrowdsourcing
IoT
Sensors selection
Spatiotemporal data
Travel planning service
Unsupervised learning
العنوانMobile crowdsourced sensors selection for journey services
النوعConference Paper
الصفحات463-477
رقم المجلد11236 LNCS
dc.accessType Abstract Only


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