Enhancing Worker Recruitment in Collaborative Mobile Crowdsourcing: A Graph Neural Network Trust Evaluation Approach

被引:4
作者
Zhan, Zhongwei [1 ]
Wang, Yingjie [1 ]
Duan, Peiyong [2 ]
Sai, Akshita Maradapu Vera Venkata [3 ]
Liu, Zhaowei [1 ]
Xiang, Chaocan [4 ]
Tong, Xiangrong [1 ]
Wang, Weilong [5 ]
Cai, Zhipeng [3 ]
机构
[1] Yantai Univ, Sch Comp & Control Engn, Yantai 264005, Peoples R China
[2] Qilu Univ Technol, Shandong Acad Sci, Dept Elect Elect & Control, Jinan 250000, Peoples R China
[3] Georgia State Univ, Dept Comp Sci, Atlanta, GA 30303 USA
[4] Chongqing Univ, Coll Comp Sci, Chongqing 400044, Peoples R China
[5] Southeast Univ, Dept Comp Sci & Engn, Nanjing 211189, Peoples R China
基金
中国国家自然科学基金;
关键词
Task analysis; Privacy; Social networking (online); Collaboration; Sensors; Target tracking; Mobile crowdsourcing; recruitment; trust evaluation; graph neural network; collaboration; TEAM PERFORMANCE; SCHEME;
D O I
10.1109/TMC.2024.3373469
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Collaborative Mobile Crowdsourcing (CMCS) allows platforms to recruit worker teams to collaboratively execute complex sensing tasks. The efficiency of such collaborations could be influenced by trust relationships among workers. To obtain the asymmetric trust values among all workers in the social network, the Trust Reinforcement Evaluation Framework (TREF) based on Graph Convolutional Neural Networks (GCNs) is proposed in this paper. The task completion effect is comprehensively calculated by considering the workers' ability benefits, distance benefits, and trust benefits in this paper. The worker recruitment problem is modeled as an Undirected Complete Recruitment Graph (UCRG), for which a specific Tabu Search Recruitment (TSR) algorithm solution is proposed. An optimal execution team is recruited for each task by the TSR algorithm, and the collaboration team for the task is obtained under the constraint of privacy loss. To enhance the efficiency of the recruitment algorithm on a large scale and scope, the Mini-Batch K-Means clustering algorithm and edge computing technology are introduced, enabling distributed worker recruitment. Lastly, extensive experiments conducted on five real datasets validate that the recruitment algorithm proposed in this paper outperforms other baselines. Additionally, TREF proposed herein surpasses the performance of state-of-the-art trust evaluation methods in the literature.
引用
收藏
页码:10093 / 10110
页数:18
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