Mobile Crowd Sensing (MCS) enables large-scale data collection for applications such as traffic monitoring, environmental sensing, and public health by leveraging mobile participants. However, maintaining high task completion rates under strict time constraints remains challenging due to worker unavailability, skill mismatches, and dynamic mobility. This paper proposes a Deadline-Aware Adaptive Task Assignment (DAATA) framework that explicitly integrates worker profiling (skills, availability, and historical reliability), deadline-sensitive prioritization, and real-time monitoring with adaptive task reassignment. DAATA employs a priority-based optimization strategy that jointly considers task urgency and worker–task skill compatibility to improve timely execution. Experiments conducted on real-world datasets, including Chengdu taxi trajectories and D4D mobile records, show that DAATA achieves consistently high task completion performance in moderate workload scenarios, outperforming baseline methods by 18–32%. Additionally, the framework reduces average latency by 21–27%, energy consumption by 19–25%, and task failure rate by up to 30% compared to greedy and random assignment strategies. These results demonstrate that deadline-aware prioritization and adaptive reassignment significantly enhance reliability and efficiency in time-critical MCS applications for smart city environments.
BiBi et al. (Mon,) studied this question.
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