基于BP神经网络的边缘设备可信评估模型数据集

该数据集主要用于边缘计算可信评估模型的开发。利用开源IoT边缘计算框架EdgeX Foundry建立3个边缘服务器，然后通过EdgeX Foundry的虚拟设备模块建立100个模拟设备节点用于后续的测试和开发。在服务器环境中,使终端节点都与服务器进行多次的数据交互和通讯传输，并利用EdgeX Foundry提供日志记录工具，数据筛选清理得到2000条。将其中1800条作为训练集数据，200条作为测试集数据

This dataset is primarily intended for the development of trustworthy evaluation models for edge computing. First, three edge servers were established using the open-source IoT edge computing framework EdgeX Foundry. Subsequently, 100 simulated device nodes were created via the virtual device module of EdgeX Foundry to support subsequent testing and development work. Within the server environment, terminal nodes conducted multiple rounds of data interaction and communication with the servers, and 2000 valid samples were obtained after data screening and cleaning using the logging tools provided by EdgeX Foundry. Of these samples, 1800 were allocated as the training dataset, with the remaining 200 serving as the test dataset.

搜集汇总

背景与挑战

背景概述

该数据集包含2000条边缘计算设备与服务器交互的数据，用于开发基于BP神经网络的可信评估模型，其中1800条为训练数据，200条为测试数据。数据来源于3个边缘服务器和100个模拟设备节点的模拟环境。