In the era of the Internet of Everything (IoE), the exponential growth of sensor-generated data at the network edge renders efficient Probabilistic Skyline Query (PSKY) processing a critical challenge. Traditional distributed PSKY methodologies predominantly rely on pre-defined static thresholds to filter local candidates. However, these rigid approaches are fundamentally ill-suited for the highly volatile and heterogeneous nature of edge computing environments, often leading to either severe communication bottlenecks or excessive local computational latency. To resolve this resource conflict, this paper presents SA-PSKY, a novel Self-Adaptive framework designed for distributed edge-cloud collaborative systems. We formalize the dynamic threshold adjustment problem as a continuous Markov Decision Process (MDP) and leverage a Deep Deterministic Policy Gradient (DDPG) agent to autonomously optimize filtering intensities in real-time. By intelligently analyzing multi-dimensional system states, including data arrival rates, uncertainty distributions, and instantaneous resource availability, our framework effectively minimizes a joint objective function of computation and communication costs. Comprehensive experimental evaluations demonstrate that SA-PSKY consistently outperforms state-of-the-art static and heuristic baselines. Specifically, it achieves a reduction of up to 60\% in communication overhead and 40\% in total response time, while ensuring robust scalability across diverse data distributions.

翻译：在万物互联（IoE）时代，网络边缘传感器生成的数据呈指数级增长，使得高效的概率天际线查询（PSKY）处理成为一项关键挑战。传统的分布式PSKY方法主要依赖预定义的静态阈值来过滤本地候选数据。然而，这些僵化的方法从根本上无法适应边缘计算环境高度动态和异构的特性，常常导致严重的通信瓶颈或过高的本地计算延迟。为解决这一资源冲突，本文提出了SA-PSKY，一种专为分布式边缘-云协同系统设计的新型自适应框架。我们将动态阈值调整问题形式化为一个连续的马尔可夫决策过程（MDP），并利用深度确定性策略梯度（DDPG）智能体实时自主优化过滤强度。通过智能分析多维系统状态（包括数据到达率、不确定性分布和瞬时资源可用性），我们的框架有效最小化了计算与通信成本的联合目标函数。全面的实验评估表明，SA-PSKY始终优于最先进的静态和启发式基线方法。具体而言，它在确保跨不同数据分布的鲁棒可扩展性的同时，实现了高达60%的通信开销降低和40%的总响应时间减少。