Worker Selection towards High Service Quality in Mobile Crowd Sensing

In the field of mobile crowd sensing (MCS), worker selection is a key research issue and has progressively gained considerable interests in the academic community in recent years. The goal of worker selection is to choose the superior workers for tasks that require high-performance characteristics....

Full description

Saved in:
Bibliographic Details
Published in:IEEE Vehicular Technology Conference pp. 1 - 5
Main Authors: Zou, Hong, Wang, Hongli, Cui, Yaping, He, Peng, Wu, Dapeng, Wang, Ruyan
Format: Conference Proceeding
Language:English
Published: IEEE 01.09.2022
Subjects:
Computer architecture
Matrix decomposition
Mobile crowd sensing
Quality of service
Sensors
Simulation
Tensors
Vehicular and wireless technologies
worker selection
ISSN:2577-2465
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:In the field of mobile crowd sensing (MCS), worker selection is a key research issue and has progressively gained considerable interests in the academic community in recent years. The goal of worker selection is to choose the superior workers for tasks that require high-performance characteristics. To solve the problems of long delay and poor perceived quality, we present a worker selection architecture for a recommendation system applied to the MCS system. A worker selection algorithm with high quality of service (QoS) is designed within the architecture, which considers the worker's reputation and willingness attributes to address the challenge of efficiently selecting excellent workers. Based on these two attributes, we then compute worker QoS and develop a three-dimensional tensor to optimize the worker's service. Finally, we get a continuously updated list of workers. Extensive experiments on real-world datasets show that the proposed algorithm performs better than the benchmarks, including random, greedy, and matrix-based algorithm. The results indicate that the proposed algorithm's efficiency has risen by 31% compared to the matrix-based algorithm.
ISSN:2577-2465
DOI:10.1109/VTC2022-Fall57202.2022.10012834