Q-Learning-Based Hyperheuristic Evolutionary Algorithm for Dynamic Task Allocation of Crowdsensing

Task allocation is a crucial issue of mobile crowdsensing. The existing crowdsensing systems normally select the optimal participants giving no consideration to the sudden departure of mobile users, which significantly affects the sensing quality of tasks with a long sensing period. Furthermore, the...

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Bibliographic Details
Published in:IEEE transactions on cybernetics Vol. 53; no. 4; pp. 2211 - 2224
Main Authors: Ji, Jian-Jiao, Guo, Yi-Nan, Gao, Xiao-Zhi, Gong, Dun-Wei, Wang, Ya-Peng
Format: Journal Article
Language:English
Published: United States IEEE 01.04.2023
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Costs
Crowdsensing
dynamic optimization
Evolutionary algorithms
Genetic algorithms
Heuristic algorithms
hyperheuristic
Machine learning
Optimization
Q-learning
Resource management
Sensors
Strategy
task allocation
Task analysis
ISSN:2168-2267, 2168-2275, 2168-2275
Online Access:Get full text
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Summary:Task allocation is a crucial issue of mobile crowdsensing. The existing crowdsensing systems normally select the optimal participants giving no consideration to the sudden departure of mobile users, which significantly affects the sensing quality of tasks with a long sensing period. Furthermore, the ability of a mobile user to collect high-precision data is commonly treated as the same for different types of tasks, causing the unqualified data for some tasks provided by a competitive user. To address the issue, a dynamic task allocation model of crowdsensing is constructed by considering mobile user availability and tasks changing over time. Moreover, a novel indicator for comprehensively evaluating the sensing ability of mobile users collecting high-quality data for different types of tasks at the target area is proposed. A new <inline-formula> <tex-math notation="LaTeX">Q </tex-math></inline-formula>-learning-based hyperheuristic evolutionary algorithm is suggested to deal with the problem in a self-learning way. Specifically, a memory-based initialization strategy is developed to seed a promising population by reusing participants who are capable of completing a particular task with high quality in the historical optima. In addition, taking both sensing ability and cost of a mobile user into account, a novel comprehensive strength-based neighborhood search is introduced as a low-level heuristic (LLH) to select a substitute for a costly participant. Finally, based on a new definition of the state, a <inline-formula> <tex-math notation="LaTeX">Q </tex-math></inline-formula>-learning-based high-level strategy is designed to find a suitable LLH for each state. Empirical results of 30 static and 20 dynamic experiments expose that this hyperheuristic achieves superior performance compared to other state-of-the-art algorithms.
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ISSN:2168-2267
2168-2275
2168-2275
DOI:10.1109/TCYB.2021.3112675