Flying Over Dynamism: Predictive Intelligence and Evolutionary Optimization for Multi-UAV Crowdsensing

doi:10.1109/tsusc.2026.3715184

Flying Over Dynamism: Predictive Intelligence and Evolutionary Optimization for Multi-UAV Crowdsensing

2026-07-20

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DOI：10.1109/tsusc.2026.3715184

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The rapid growth of mobile crowdsensing (MCS) has motivated the deployment of uncrewed aerial vehicles (UAVs) for large-scale, flexible, and timely data collection. However, trajectory planning in multi-UAV MCS remains highly challenging due to the dynamic and uncertain activation of points of interest (PoIs), stringent energy limitations, and safety risks arising from high-density UAV operations. Existing reinforcement learning approaches can face scalability and training-stability challenges in large-scale coordination under dynamic and uncertain PoI settings, while many heuristic methods rely on oversimplified assumptions such as static PoIs, leading to suboptimal performance in dynamic environments. To overcome these limitations, this paper proposes Pre-Evo, a novel hybrid Prediction-Evolution framework that synergistically integrates predictive intelligence with evolutionary optimization for efficient and reliable multi-UAV coordination. On the prediction side, long short-term memory (LSTM) networks are employed to accurately forecast the active time windows of dynamic PoIs, thereby transforming temporal uncertainty into a tractable decision-making input. On the optimization side, we design an enhanced multi-agent genetic algorithm (EnhancedMAGA) equipped with three tailored mechanisms. This co-design of predictive modeling and evolutionary search enables Pre-Evo to strike a balanced trade-off among maximizing task completion, minimizing flight distance and energy usage, and reducing collision risk in dense UAV swarms. Extensive experiments on both real-world urban traffic datasets and diverse synthetic scenarios demonstrate that Pre-Evo consistently outperforms state-of-the-art benchmarks. Notably, Pre-Evo also maintains robust generalization to large-scale and highly dynamic environments, highlighting its potential for next-generation UAV-assisted MCS systems.

Keywords:

Dynamic and uncertain PoIs

multi-UAV collaborative sensing

trajectory planning

prediction

evolution

Flying Over Dynamism: Predictive Intelligence and Evolutionary Optimization for Multi-UAV Crowdsensing

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