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

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    This study introduces a dynamic task allocation model for mobile crowdsensing, improving data quality by considering user availability and sensing abilities. The new Q-learning-based hyperheuristic algorithm enhances participant selection for better sensing outcomes.

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    Area of Science:

    • Computer Science
    • Mobile Computing
    • Artificial Intelligence

    Background:

    • Mobile crowdsensing systems face challenges with dynamic participant availability and varying user data collection abilities.
    • Existing systems often fail to account for user departures, impacting long-term sensing tasks and data quality.
    • A uniform assessment of user sensing capabilities across different task types leads to suboptimal data acquisition.

    Purpose of the Study:

    • To develop a dynamic task allocation model for mobile crowdsensing that addresses mobile user availability and temporal task changes.
    • To propose a novel indicator for evaluating mobile user sensing abilities tailored to specific tasks and locations.
    • To introduce a Q-learning-based hyperheuristic evolutionary algorithm for adaptive and efficient task allocation.

    Main Methods:

    • A dynamic task allocation model incorporating mobile user availability and task dynamics.
    • A new indicator to comprehensively evaluate sensing abilities for diverse tasks.
    • A Q-learning-based hyperheuristic evolutionary algorithm with memory-based initialization.
    • A comprehensive strength-based neighborhood search as a low-level heuristic (LLH).
    • A Q-learning-based high-level strategy to select appropriate LLHs.

    Main Results:

    • The proposed hyperheuristic algorithm demonstrated superior performance over state-of-the-art methods in both static and dynamic experimental settings.
    • Empirical results from 30 static and 20 dynamic experiments validated the effectiveness of the new approach.
    • The dynamic model successfully improved sensing quality by adapting to user availability and task changes.

    Conclusions:

    • The developed dynamic task allocation model and Q-learning-based hyperheuristic significantly enhance mobile crowdsensing efficiency and data quality.
    • The novel sensing ability indicator and adaptive LLH selection contribute to more reliable data collection.
    • This approach offers a robust solution for optimizing task allocation in dynamic mobile crowdsensing environments.