The proliferation of Mobile Edge Computing (MEC) and distributed sensing necessitates efficient Probabilistic Skyline (PSKY) query analytics at the network edge. However, this process is severely constrained by the computation-communication trade-off under constrained wireless capacity and dynamic traffic conditions. Furthermore, mobility-induced workload fluctuations and spatio-temporal data shifts render static filtering policies inefficient, often triggering network congestion or compromising query fidelity. To address these systemic inefficiencies in dynamic mobile environments, this paper introduces SA-PSKY, a self-adaptive framework integrating deep reinforcement learning into a distributed query optimization architecture. We formulate the probabilistic skyline filtering as an adaptive control problem and develop a State-Aware Adaptive Weighting (SAAW) mechanism to dynamically regulate computation, communication, and fidelity. By incorporating Prioritized Experience Replay (PER) to stabilize learning around abrupt network transitions, our framework reliably navigates the Pareto-efficient operating point. Empirical evaluations confirm that SA-PSKY achieves substantial latency reduction while preserving query fidelity under constrained communication resources. Furthermore, zero-shot robustness analyses reveal superior adaptation to unseen mobility-induced uncertainty shifts, where rigid methods exhibit severe policy degradation.

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