计算机科学
次模集函数
拥挤感测
Android(操作系统)
贪婪算法
选择(遗传算法)
移动设备
选择算法
兴趣点
分布式计算
机器学习
人工智能
计算机安全
万维网
算法
数学优化
操作系统
数学
作者
Yintang Yang,Wenbin Liu,En Wang,Jie Wu
标识
DOI:10.1109/tmc.2018.2879098
摘要
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.
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