Friendship-aware task planning in mobile crowdsourcing

Yuan LIANG; Wei-feng LV; Wen-jun WU; Ke XU

doi:10.1631/FITEE.1601860

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Friendship-aware task planning in mobile crowdsourcing

Special Issue on Artificial Intelligence 2.0 | Updated：2022-05-19

- Friendship-aware task planning in mobile crowdsourcing
- 移动众包环境下基于友谊度的任务规划
- Frontiers of Information Technology & Electronic Engineering Vol. 18, Issue 1, Pages: 107-121(2017)
- Affiliations：
  
  State Key Laboratory of Software Development Environment, School of Computer Science, Beihang University, Beijing 100191, China
- Author bio：
  
  E-mail:liangyuan120@nlsde.buaa.edu.cn
  E-mail:lwf@nlsde.buaa.edu.cn
  E-mail:wwj@nlsde.buaa.edu.cn
  E-mail:kex@nlsde.buaa.edu.cn
- Funds：
- DOI：10.1631/FITEE.1601860
  CLC： TP3
- Received：23 December 2016，
  
  Accepted：09 January 2017，
  
  Published：2017-01
- Accepted：
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Yuan LIANG, Wei-feng LV, Wen-jun WU, et al. Friendship-aware task planning in mobile crowdsourcing[J]. Frontiers of Information Technology & Electronic Engineering, 2017, 18(1): 107-121.
DOI：

Yuan LIANG, Wei-feng LV, Wen-jun WU, et al. Friendship-aware task planning in mobile crowdsourcing[J]. Frontiers of Information Technology & Electronic Engineering, 2017, 18(1): 107-121. DOI： 10.1631/FITEE.1601860.

摘要

最近，众包平台已吸引了大量的注册用户在线下执行特定的任务。然而，大部分现有方法仅考虑了工人和任务信息都已提前获知的情况（静态离线情景），而较少考虑到工人动态抵达的情况（动态在线情景）。因此，当工人动态抵达时，如何给工人安排适合的任务以获得其最大的满意度成为了一个具有挑战性的问题。为解决这一问题，本文提出了一种有用并且普遍的人工-任务模型，该模型不仅考虑在静态离线时给工人安排任务的情况，还同时考虑了在工人动态抵达时如何给工人安排合适的任务。在本文中，为解决静态离线情景下的任务分配问题，我们首先提出一个有效的贪心算法。由于贪心算法极易陷入局部最优，我们又加入了模拟退火法来提高贪心算法的性能。另外，为解决动态在线情景下的分配问题，我们提出了一种贪心算法。最后，在真实数据集和具有不同分布人造数据集上进行了大量实验，实验结果验证了算法的效果与性能。

Abstract

Recently

crowdsourcing platforms have attracted a number of citizens to perform a variety of locationspecific 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.

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