A Prediction-Based User Selection Framework for Heterogeneous Mobile CrowdSensing | IEEE Journals & Magazine | IEEE Xplore

A Prediction-Based User Selection Framework for Heterogeneous Mobile CrowdSensing


Abstract:

Mobile CrowdSensing is a new paradigm in which requesters launch tasks to the mobile users who provide the sensing services. The tasks, in practice, are usually heterogen...Show More

Abstract:

Mobile CrowdSensing is a new paradigm in which requesters launch tasks to the mobile users who provide the sensing services. The tasks, in practice, are usually heterogeneous (have diverse spatial-temporal requirements), which make it hard to select an efficient subset of users to perform the tasks. In this paper, we present a point of interest (PoI) based mobility prediction model to obtain the probabilities that tasks would be completed by users. Based on it, we propose a greedy offline algorithm to select a set of users under a participant number constraint. Furthermore, we extend the user selection problem to a more realistic online setting where users come in real time and we decide to select or not immediately. We formulate the problem as a submodular k-secretaries problem and propose an online algorithm. Finally, we design a distributed user selection framework Crowd UserS and implement an Android prototype system as proof of the concept. Extensive simulations have been conducted on three real-life mobile traces and the results prove the efficiency of our proposed framework.
Published in: IEEE Transactions on Mobile Computing ( Volume: 18, Issue: 11, 01 November 2019)
Page(s): 2460 - 2473
Date of Publication: 01 November 2018

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