基于移动性预测的群智感知混合式任务分配

蒋伟进; 聂彩燕; 刘茜; 杜熙晨; 杨璇; 蒋意容

doi:10.12263/DZXB.20240632

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基于移动性预测的群智感知混合式任务分配

学术论文 | 更新时间：2025-10-16

- 基于移动性预测的群智感知混合式任务分配
- Hybrid Task Assignment for Crowd Sensing Based on Mobility Prediction
- 电子学报 2025年53卷第6期页码：1979-1995
- 作者机构：
  
  1.湖南工商大学计算机学院，湖南长沙 410205
  2.湘江实验室，湖南长沙 410205
  3.湖南工商大学前沿交叉学院，湖南长沙 410205
  4.湖南信息学院，湖南长沙 410151
- 作者简介：
  
  [ "蒋伟进男，1964年7月出生于湖南省益阳市.现为湖南工商大学计算机学院二级教授、硕士生导师.主要研究方向为群智感知、联邦学习、边缘计算. E-mail: jwjnudt@163.com" ]
  [ "聂彩燕女，2001年5月出生于湖南省株洲市. 现为湖南工商大学计算机学院硕士研究生.主要研究方向为群智感知、隐私保护. E-mail: 516278890@qq.com" ]
- 基金信息：
  
  国家自然科学基金(61772196);湖南省自然科学基金(2020JJ4249);湖南省教育厅科学研究重点项目(21A0374)
- DOI：10.12263/DZXB.20240632
  中图分类号： TP274;
- 收稿：2024-07-02，
  
  修回：2025-01-10，
  
  纸质出版：2025-06-25
- 稿件说明：
移动端阅览
蒋伟进, 聂彩燕, 刘茜, 等. 基于移动性预测的群智感知混合式任务分配[J]. 电子学报, 2025, 53(06): 1979-1995.

JIANG Wei-jin, NIE Cai-yan, LIU Qian, et al. Hybrid Task Assignment for Crowd Sensing Based on Mobility Prediction[J]. Acta Electronica Sinica, 2025, 53(06): 1979-1995.
蒋伟进, 聂彩燕, 刘茜, 等. 基于移动性预测的群智感知混合式任务分配[J]. 电子学报, 2025, 53(06): 1979-1995. DOI：10.12263/DZXB.20240632

JIANG Wei-jin, NIE Cai-yan, LIU Qian, et al. Hybrid Task Assignment for Crowd Sensing Based on Mobility Prediction[J]. Acta Electronica Sinica, 2025, 53(06): 1979-1995. DOI：10.12263/DZXB.20240632

摘要

移动群智感知（Mobile Crowd Sensing，MCS）通过用户随身携带的感知设备来收集数据，是一种大规模数据感知范式，其中任务分配是主要挑战之一.文章研究具有异构质量延迟敏感任务的混合用户任务分配问题，设计目标是在机会式用户和参与式用户共享总预算下，最大限度地提高任务完成质量.针对现有预测方法预测精度不足问题，文章提出一种基于迁移学习的移动性预测模型，通过将轨迹丰富的旧参与者的数据转移给新参与者，解决历史数据稀缺导致的预测误差.基于此预测模型，设计了一个混合用户任务分配算法，该算法利用移动性预测模型为机会式用户分配任务；此外，将剩余任务聚类到不同区域，构造二部图匹配问题使参与式用户和任务区域绑定；之后提出一种基于行程距离平衡的蚁群优化算法（Ant Colony Optimization algorithm based on Travel Distance Balance，ACOTDB），在用户行程距离预算下实现最优路径规划.在真实数据集上的大量仿真实验表明，与现有算法进行比较，本文算法在任务完成质量和任务分配效率方面具有显著的优势，验证了其有效性.

Abstract

Mobile crowd sensing (MCS) collects data through the sensing devices carried by users and is a large-scale data sensing paradigm

where task allocation is one of the main challenges. This paper studies the task allocation problem of mixed users with heterogeneous quality delay-sensitive tasks. The design objective is to maximize the quality of task completion under the shared total budget of opportunistic users and participatory users. In response to the problem of insufficient prediction accuracy of existing prediction methods

this paper proposes a mobility prediction model based on transfer learning. By transferring the data of old participants with rich trajectories to new participants

it solves the prediction errors caused by the scarcity of historical data. Based on this prediction model

a mixed user task allocation algorithm is designed. The algorithm uses the mobility prediction model to allocate tasks to opportunistic users. In addition

the remaining tasks are clustered into different areas

and a bipartite graph matching problem is constructed to bind participatory users and task areas. Subsequently

an ant colony optimization algorithm based on travel distance balance (ACOTD) is proposed to achieve optimal path planning under the user’s travel distance budget. Through a large number of simulation experiments on real datasets

this paper compares with existing algorithms. The results show that the algorithm has significant advantages in task completion quality and task allocation efficiency

verifying its effectiveness.

关键词

Keywords

references

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