Worker Development-Aware Task Allocation Strategy in Mobile Crowd Sensing

LÜ Yi; WANG Yan; CUI Yaping; HE Peng; WU Dapeng; WANG Ruyan

doi:10.11999/JEIT220249

Volume 45 Issue 4

Apr. 2023

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Article Navigation > Journal of Electronics & Information Technology > 2023 > 45(4): 1505-1513

LÜ Yi, WANG Yan, CUI Yaping, HE Peng, WU Dapeng, WANG Ruyan. Worker Development-Aware Task Allocation Strategy in Mobile Crowd Sensing[J]. Journal of Electronics & Information Technology, 2023, 45(4): 1505-1513. doi: 10.11999/JEIT220249

Citation:

LÜ Yi, WANG Yan, CUI Yaping, HE Peng, WU Dapeng, WANG Ruyan. Worker Development-Aware Task Allocation Strategy in Mobile Crowd Sensing[J]. Journal of Electronics & Information Technology, 2023, 45(4): 1505-1513. doi: 10.11999/JEIT220249

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Worker Development-Aware Task Allocation Strategy in Mobile Crowd Sensing

doi: 10.11999/JEIT220249

1.
School of Communication and Information Engineering, Chongqing University of Posts and Telecommunications, Chongqing 400065, China
2.
Advanced Network and Intelligent Connection Technology Key Laboratory of Chongqing Education Commission, Chongqing 400065, China
3.
Chongqing Key Laboratory of Ubiquitous Sensing and Networking, Chongqing 400065, China

Funds: The National Natural Science Foundation of China (61771082, 61801065, 61871062, 61901070, 62061007, U20A20157), The Science and Technology Research Program of Chongqing Municipal Education Commission (KJQN201900611, KJQN202000603), The University Innovation Research Group of Chongqing (CXQT20017), The Natural Science Foundation of Chongqing (cstc2020jcyj-zdxmX0024, cstc2021jcyj-msxmX0892)

Received Date: 2022-02-15
Accepted Date: 2022-08-02
Rev Recd Date: 2022-07-30

Available Online: 2022-08-04

Publish Date: 2023-04-10

Abstract

Abstract

Mobile Crowd Sensing (MCS) is a new paradigm that collects sensing data via the mobility of massive workers and carried sensing devices. Current works focus on the task allocation issue and improving sensing data quality. However, they ignore the sensing tasks lacking qualified workers and decrease the task completion quality. To tackle the above problem, for sensing tasks that lack qualified workers, inexperienced workers are developed to qualified workers and execute these tasks. As a result, the qualified workers can long-term execute these tasks, and the sensing data quality and long-term platform utility are improved. Furthermore, both the capacities that sensing tasks need and the capacities that workers own are considered. According the above capacities, first, a stable matching algorithm is applied to select workers to be developed. And then a Multi-stage Worker Selection and Development (MWSD) algorithm is proposed based on capacity fusion and Semi-Markov prediction. The results show that compared to Blockchain-based Nondeterministic Teamwork Cooperation (BNTC) algorithm, the mechanism can improve the data quality of sensing tasks lacking qualified workers by 24% and long-term platform utility by 17%.
- Mobile Crowd Sensing (MCS),
- Task allocation,
- Worker development,
- Worker selection

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References(21)

References

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