Time-Constrained Task Allocation and Worker Routing in Mobile Crowd-Sensing Using a Decomposition Technique and Deep Q-Learning
Mobile crowd-sensing (MCS) is a data collection paradigm, which recruits mobile users with smart devices to perform sensing tasks on a city-wide scale. In MCS, a key challenge is task allocation, especially when MCS applications are time-sensitive, and the platform needs to consider task completion...
Main Authors: | Shathee Akter, Thi-Nga Dao, Seokhoon Yoon |
---|---|
Format: | Article |
Language: | English |
Published: |
IEEE
2021-01-01
|
Series: | IEEE Access |
Subjects: | |
Online Access: | https://ieeexplore.ieee.org/document/9474442/ |
Similar Items
-
DaTask: A Decomposition-Based Deadline-Aware Task Assignment and Workers’ Path-Planning in Mobile Crowd-Sensing
by: Shathee Akter, et al.
Published: (2020-01-01) -
LCBPA: two-stage task allocation algorithm for high-dimension data collecting in mobile crowd sensing network
by: Ning Zhou, et al.
Published: (2019-12-01) -
Location and Time Aware Multitask Allocation in Mobile Crowd-Sensing Based on Genetic Algorithm
by: Abd El-Latif, A.A, et al.
Published: (2022) -
Location and Time Aware Multitask Allocation in Mobile Crowd-Sensing Based on Genetic Algorithm
by: Abd El-Latif, A.A, et al.
Published: (2022) -
Failure-Aware Mobile Crowd Sensing: A Social Relationship-Based Transfer Approach
by: Liang Wang, et al.
Published: (2019-01-01)