Sub-aquatic data processing is a procedure that exchanges datasets through underwater sensory devices in the distributed computing environment. This paradigm has evolved techniques of data exchange and signal processing over time and uses big data frameworks to store processed datasets at edge nodes. Also, it uses modern IoT devices that capture sensory data tuples of water temperature, turbidity, speed, and pressure levels. Recently, we observe that the edge nodes that acquire the dataset of heterogeneous IoT devices are becoming overwhelmed with the issue of tuple non-classification at the level of data encapsulation. This issue raises a few concerns such as (a) ineffective tuple wrapup, (b) bundle compression failovers, (c) bundle block placement latency, and (d) end-of-file replica build latency. This paper proposes a fine-grained processing framework that normalizes tuple non-classification through enhanced false-positive function and assembles IoT sensory tuples with the in-memory capacity to rectify compression failovers. This solution leads to a tremendous decrease in bundle block placement and end-of-file replica latencies. The simulation results depict the effectiveness of fine-grained processing framework through easing the edge nodes in the sub-aquatic distributed environment.

水下水数据处理是通过分布式计算环境中的水下传感设备交换数据集的过程。这种范例随着时间的推移发展了数据交换和信号处理技术，并使用大数据框架将处理后的数据集存储在边缘节点上。此外，它使用现代物联网设备来捕获水温，浊度，速度和压力水平的感官数据元组。最近，我们观察到获取异构物联网设备数据集的边缘节点在数据封装级别的元组非分类问题变得不知所措。此问题引起了一些问题，例如（a）元组包装无效，（b）捆绑压缩故障转移，（c）捆绑块放置延迟和（d）文件结束副本构建延迟。本文提出了一种细粒度的处理框架，该框架通过增强的假阳性功能对元组非分类进行归一化，并组装具有内存容量的IoT感觉元组以纠正压缩故障转移。该解决方案极大地减少了束块放置和文件结束副本延迟。仿真结果通过缓和亚水生分布式环境中的边缘节点，描述了细粒度处理框架的有效性。