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User-centric Context Inference for Mobile Crowdsensing

Abstract : Mobile crowdsensing is a powerful mechanism to aggregate hyper-local knowledge about the environment. Indeed, users may contribute valuable observations across time and space using the sensors embedded in their smartphones. However, the relevance of the provided measurements depends on the adequacy of the sensing context with respect to the phenomena that are analyzed. This paper concentrates more specifically on assessing the sensing context when gathering observations about the physical environment beyond its geographical position in the Euclidean space, i.e., whether the phone is in-/out-pocket, in-/out-door and on-/under-ground. We introduce an online learning approach to the local inference of the sensing context so as to overcome the disparity of the classification performance due to the heterogeneity of the sensing devices as well as the diversity of user behavior and novel usage scenarios. Our approach specifically features a hierarchical algorithm for inference that requires few opportunistic feedbacks from the user, while increasing the accuracy of the context inference per user.
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Submitted on : Wednesday, March 27, 2019 - 10:49:42 PM
Last modification on : Friday, January 21, 2022 - 3:19:24 AM
Long-term archiving on: : Friday, June 28, 2019 - 6:16:33 PM


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Yifan Du, Valerie Issarny, Francoise Sailhan. User-centric Context Inference for Mobile Crowdsensing. IoTDI 2019: ACM/IEEE International Conference on Internet of Things Design and Implementation, Apr 2019, Montreal, Canada. pp.261-266, ⟨10.1145/3302505.3310088⟩. ⟨hal-02082034⟩



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