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Efficient Demand-Based Adaptive Task Assignment With Dynamic Worker Availability Windows
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DOI：10.1109/tmc.2026.3708395.png)
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Abstract
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With the rapid advancement of mobile networks and the widespread use of mobile devices, spatial crowdsourcing, which involves assigning location-based tasks to mobile workers, has gained significant attention. However, most existing research focuses on task assignment at the current moment, overlooking the fluctuating demand and supply between tasks and workers over time. To address this issue, we introduce an adaptive task assignment problem, which aims to maximize the number of assigned tasks by dynamically adjusting task assignments in response to changing demand and supply. We develop a spatial crowdsourcing framework, namely demand-based adaptive task assignment with dynamic worker availability windows, which consists of two components including task demand prediction and task assignment. In the first component, we construct a graph adjacency matrix representing the demand dependency relationships in different regions and employ a multivariate time series learning approach to predict future task demands. In the task assignment component, we match tasks to workers based on these predictions, worker availability windows, and current task assignments, where each worker has an availability window that indicates the time periods they can accept tasks. To reduce the search space for task assignments, we propose a worker dependency separation approach based on graph partition and a task value function with reinforcement learning. For efficient task assignment, we optimize task assignments by prioritizing task sequences with higher future opportunity values and designing a pruning strategy to eliminate invalid sequences, ensuring real-time responsiveness. Experiments on real data demonstrate that our proposals are both effective and efficient.
Keywords:
Task assignment
spatial crowdsourcing
task demand
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