Spatial crowdsourcing task allocation for heterogeneous multi-task hybrid scenarios: a model-embedded role division approach

Zhenhui FENG; Renbin XIAO; Mingzhi XIAO

doi:10.1631/FITEE.2500035

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Spatial crowdsourcing task allocation for heterogeneous multi-task hybrid scenarios: a model-embedded role division approach

Regular Papers | Updated：2025-07-28

- Spatial crowdsourcing task allocation for heterogeneous multi-task hybrid scenarios: a model-embedded role division approach
  Enhanced Publication
- 面向异构多任务混合场景的空间众包任务分配：一种嵌入模型的角色分工方法
- Frontiers of Information Technology & Electronic Engineering Vol. 26, Issue 7, Pages: 1144-1163(2025)
- Affiliations：
  
  1.School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan 430074, China
  2.Petro China Kunlun Gas Company Limited Hubei Branch, Wuhan 430077, China
  3.School of Information Science and Engineering, Wuchang Shouyi University, Wuhan 430064, China
- Author bio：
  
  Email: feng_zh@hust.edu.cn
  ‡ Corresponding author
  2024111014@wsyu.edu.cn
- Funds：
- DOI：10.1631/FITEE.2500035
  CLC： TP399
- Received：15 January 2025，
  
  Revised：2025-05-05，
  
  Published Online：05 July 2025，
  
  Published：2025-07
- Accepted：
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Zhenhui FENG, Renbin XIAO, Mingzhi XIAO. Spatial crowdsourcing task allocation for heterogeneous multi-task hybrid scenarios: a model-embedded role division approach[J]. Frontiers of Information Technology & Electronic Engineering, 2025, 26(7): 1144-1163.
DOI：

Zhenhui FENG, Renbin XIAO, Mingzhi XIAO. Spatial crowdsourcing task allocation for heterogeneous multi-task hybrid scenarios: a model-embedded role division approach[J]. Frontiers of Information Technology & Electronic Engineering, 2025, 26(7): 1144-1163. DOI： 10.1631/FITEE.2500035.

摘要

空间众包作为一种完成时空任务的有效范式，逐渐引起工业界和学术界广泛关注。随着移动技术的进步，为了更好地满足用户的多样化需求，空间众包的服务模式变得更加灵活和多样化。现有研究大多聚焦于单一类型下的同构任务分配问题，未考虑任务需求和工作者的个体差异，因此在实际应用中难以得到令人满意的结果。本文根据空间众包现实场景，研究了混合场景下异构多任务分配问题，并给出问题的形式化描述和定义。在问题求解方面，提出一种嵌入个体排序模型的角色分工方法，该方法以批处理框架为基础，可分为两部分。首先，引入个体排序模型，根据任务与工人的时空属性确定对象的排序。其次，基于吸引—排斥机制设计角色分工模型，以匹配任务与工人。经多批次迭代，获得最终匹配结果。使用真实和合成数据集验证了该方法的有效性，并通过与其他算法的比较来证明其性能。此外，研究了不同参数的影响，确认了其可扩展性。

Abstract

Spatial crowdsourcing (SC)

as an effective paradigm for accomplishing spatiotemporal tasks

has gradually attracted widespread attention from both industry and academia. With the advancement of mobile technology

the service modes of SC have become more diversified and flexible

aiming to better meet the variable requirements of users. However

most research has focused on homogeneous task allocation problems under a single service model

without considering the individual differences among task requirements and workers. Consequently

many of these studies fail to achieve satisfactory outcomes in real scenarios. Based on real service scenarios

in this study

we investigate a heterogeneous multi-task allocation (HMTA) problem for hybrid scenarios and provide a formal description and definition of the problem. To solve the problem

we propose a role division approach embedded with an individual sorting model (RD-ISM). This approach is implemented based on a batch-based mode (BBM) and consists of two parts. First

an individual sorting model is introduced to determine the sequence of objects based on spatiotemporal attributes

prioritizing tasks and workers. Second

a role division model is designed based on an attraction‍–‍repulsion mechanism to match tasks and workers. Following several iterations over multiple batches

the approach obtains the final matching results. The effectiveness of the approach is verified using real and synthetic datasets and its performance is demonstrated through comparisons with other algorithms. Additionally

the impact of different parameters within the approach is investigated

confirming its scalability.

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Keywords

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