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Smart city article details
| Title | Joint Trajectory And Incentive Optimization For Privacy-Preserving Uav Crowdsensing Via Multi-Agent Federated Reinforcement Learning |
|---|---|
| ID_Doc | 34442 |
| Authors | Zhu C.; Zhu X.; Qin T. |
| Year | 2025 |
| Published | Internet of Things (The Netherlands), 33 |
| DOI | http://dx.doi.org/10.1016/j.iot.2025.101689 |
| Abstract | UAV-assisted mobile crowdsensing (MCS) presents a promising paradigm for enhancing data collection in smart city environments, but faces a critical systems challenge: the intricate coupling between spatial trajectory planning, economic incentive mechanisms, and information-theoretic privacy guarantees. Traditional approaches addressing these dimensions in isolation often lead to suboptimal performance. In this paper, we propose PRISM, a unified framework designed around a holistic optimization approach with three components. First, PRISM models the strategic interactions among Service Providers, UAVs, and ground devices through a multi-level Stackelberg game, capturing the hierarchical economic dynamics that influence participation decisions. Second, it incorporates a privacy-aware incentive mechanism that explicitly links UAV navigation decisions to privacy constraints, dynamically managing the privacy-utility trade-off based on data quality. Third, to address the resulting multi-objective optimization across distinct decision timescales, we introduce TMFR, a Two-timescale Multi-agent Federated Reinforcement Learning algorithm that enables UAVs to collaboratively learn policies for both spatial navigation and incentive allocation. Experimental evaluations demonstrate that TMFR achieves 30% faster convergence and 20% higher data quality compared to baselines that optimize only subsets of the problem. These results highlight its suitability for next-generation smart city applications. © 2025 The Authors |
| Author Keywords | Incentive mechanism; Mobile crowdsensing; Multi-agent federated reinforcement learning; Privacy preservation; Stackelberg game; Unmanned aerial vehicles |
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