发表机构
Florida Atlantic University（佛罗里达大西洋大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对通信受限分布式系统，提出基于时变信道条件在OR折叠布隆过滤器表示中选择传输的方案，在满足假阳性率约束下提升通信效率与接收方新鲜度。
AI 中文摘要
在通信受限的分布式系统中，周期性传输布隆过滤器可能带来巨大开销。无损压缩保留了成员关系行为，但仅提供单一传输大小，而已有的OR折叠方法在保持无假阴性性质的同时，产生更小的表示形式，但具有更高的假阳性率（FPR）。本文研究在OR折叠表示之间进行信道感知选择。发送方保留未更改的规范过滤器，构建满足最大假阳性率的目录，并在每次报告机会时，根据可用通信资源选择具有最大保留长度且符合假阳性率要求的表示。与主要受基数和假阳性约束驱动的折叠不同，该选择由时变通信条件驱动。使用两个钓鱼URL数据集，在五状态马尔可夫容量、Gilbert-Elliott突发错误和瑞利块衰落模型下评估该框架。当通信机会变化显著时，与完整过滤器和无损压缩基线相比，信道感知折叠提高了通信效率并改善了接收方的新鲜度。在更有利的Gilbert-Elliott模型下，它在保持效率竞争力的同时，维持了最新的接收方状态。这些结果表明，当较新的低保真更新优于延迟较大的表示时，符合假阳性率要求的折叠视图提供了有用的传输工作点。
英文摘要
Periodic Bloom-filter transmission can impose substantial overhead in communication-constrained distributed systems. Lossless compression preserves membership behavior but provides a single transmission size, whereas established OR folding produces smaller representations with higher false-positive rates (FPRs) while preserving the no-false-negative property. This paper investigates channel-aware selection among OR-folded representations. The sender retains an unchanged canonical filter, constructs a catalog satisfying a maximum FPR, and selects the FPR-qualified representation with the largest retained length supported by the communication resources available at each reporting opportunity. Unlike folding driven principally by cardinality and false-positive constraints, selection is driven by time-varying communication conditions. Using two phishing URL datasets, the framework is evaluated under Five-State Markov Capacity, Gilbert--Elliott burst-error, and Rayleigh block-fading models. Channel-aware folding improves communication efficiency and receiver freshness relative to complete-filter and lossless-compression baselines when communication opportunities vary substantially. Under the more favorable Gilbert--Elliott model, it remains competitive in efficiency while maintaining the freshest receiver state. These results show that FPR-qualified folded views provide useful transmission operating points when a recent lower-fidelity update is preferable to delaying a larger representation.
Comments7 pages, 5 figures. Accepted for presentation at the 2026 IEEE International Conference on Internet of Things and Intelligence System (IoTaIS 2026)
