Friendship-aware task planning in mobile crowdsourcing
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- 作者：Yuan Liang ; Wei-feng Lv ; Wen-jun Wu ; Ke Xu
- 关键词：Key wordsMobile crowdsourcing ; Task planning ; Greedy algorithms ; Simulated annealing
- 刊名：Frontiers of Information Technology & Electronic Engineering
- 出版年：2017
- 出版时间：January 2017
- 年：2017
- 卷：18
- 期：1
- 页码：107-121
- 全文大小：
- 刊物类别：Computer Science, general; Electrical Engineering; Computer Hardware; Computer Systems Organization
- 刊物主题：Computer Science, general; Electrical Engineering; Computer Hardware; Computer Systems Organization and Communication Networks; Electronics and Microelectronics, Instrumentation; Communications Engine
- 出版者：Zhejiang University Press
- ISSN：2095-9230
- 卷排序：18
文摘
Recently, crowdsourcing platforms have attracted a number of citizens to perform a variety of location-specific tasks. However, most existing approaches consider the arrangement of a set of tasks for a set of crowd workers, while few consider crowd workers arriving in a dynamic manner. Therefore, how to arrange suitable location-specific tasks to a set of crowd workers such that the crowd workers obtain maximum satisfaction when arriving sequentially represents a challenge. To address the limitation of existing approaches, we first identify a more general and useful model that considers not only the arrangement of a set of tasks to a set of crowd workers, but also all the dynamic arrivals of all crowd workers. Then, we present an effective crowd-task model which is applied to offline and online settings, respectively. To solve the problem in an offline setting, we first observe the characteristics of task planning (CTP) and devise a CTP algorithm to solve the problem. We also propose an effective greedy method and integrated simulated annealing (ISA) techniques to improve the algorithm performance. To solve the problem in an online setting, we develop a greedy algorithm for task planning. Finally, we verify the effectiveness and efficiency of the proposed solutions through extensive experiments using real and synthetic datasets.