不稳定移动应急网络的包级网内语义自适应
Packet-Level In-Network Semantic Adaptation for Unstable Mobile Emergency Networks
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中文总结 AI 辅助
针对移动应急网络中链路变化快于反馈的问题，提出包级网内语义自适应方法DINA，通过自描述数据包和冻结选择器实现即时转换，显著提升任务性能。
中文摘要 AI 辅助
移动应急网络可能经历中间无线链路的独立变化，其时间尺度短于端点反馈所能跟踪的范围。当数据包发出后出口发生变化时，反馈仅影响后续的源数据，而路径上的节点在受影响的数据包仍可修改时观察到当前状况。本文提出DINA，一种包级网内语义自适应方法。图像被划分为自描述的空间数据包，携带坐标、当前表示标识符和载荷。在每个符合条件的节点上，一个离线训练的冻结选择器对兼容算子进行评分，立即转换数据包，并在不进行图像重建或跨包自适应状态的情况下转发它。后续节点可以通过相同的类型化兼容性契约保留或进一步压缩数据包。接收器按坐标放置可用数据包，用黑色填充缺失区域，并运行固定的机器任务。我们在一个24节点无人机环境中使用XDP和AF_XDP实现了DINA。在主要的森林火灾轨迹中，相对于转发，DINA将截止时间瓦片覆盖率从40.4%提高到72.6%，分类准确率从77.5%提高到95.0%。在独立训练的RescueNet分割案例中，它将覆盖率从65.6%提高到91.7%，前景mIoU从0.486提高到0.541。充足和极端容量配置文件分别暴露了无增益边界和常见任务失败边界。
英文摘要
Mobile emergency networks can experience independently changing intermediate wireless links on timescales shorter than endpoint feedback can track. When an egress changes after packet emission, feedback affects only later source data, while the on-path node observes the current condition with the affected packet still mutable. This paper presents DINA, a packet-level in-network semantic adaptation method. An image is divided into self-describing spatial packets carrying coordinates, a current representation identifier, and payload. At each eligible node, an offline-trained frozen selector scores compatible operators, immediately transforms the packet, and forwards it without image reconstruction or cross-packet adaptation state. Later nodes can retain or further compact the packet through the same typed compatibility contract. The receiver places available packets by coordinate, fills missing regions with black, and runs a fixed machine task. We realize DINA in a 24-node UAV environment using XDP and AF_XDP. In the primary forest-fire trace, DINA raises deadline tile coverage from 40.4% to 72.6% and classification accuracy from 77.5% to 95.0% relative to forwarding. In an independently trained RescueNet segmentation case, it raises coverage from 65.6% to 91.7% and foreground mIoU from 0.486 to 0.541. Sufficient- and extreme-capacity profiles expose a no-gain boundary and a common task-failure boundary, respectively.
发表机构
- School of Telecommunications Engineering, Xidian University（西安电子科技大学通信工程学院）
机构由 AI 辅助整理，请以论文原文为准。
