Empirical dataset generation framework (EDGF)
收藏arXiv2021-01-26 更新2024-08-06 收录
下载链接：
http://arxiv.org/abs/2101.10758v1
下载链接
链接失效反馈官方服务：
资源简介：
Empirical dataset generation framework (EDGF) 是由印度理工学院计算机科学与工程系开发的，旨在为无线传感器网络（WSNs）提供统一的数据集，以支持系统模型、算法和协议的验证。该数据集通过一种改进的线性同余函数生成，能够根据用户输入的种子值产生伪随机性，适用于2D部署坐标和流量矩阵的生成。EDGF特别关注于随机部署、活动跟踪和数据包生成等应用，通过Kolmogorov-Smirnov测试、χ2测试和自相关测试确保数据的均匀性和随机性。该数据集的应用领域广泛，包括但不限于无线网络的模拟和验证，旨在解决随机性假设对网络性能影响的问题。
The Empirical Dataset Generation Framework (EDGF) was developed by the Department of Computer Science and Engineering, Indian Institute of Technology. It aims to provide a unified dataset for Wireless Sensor Networks (WSNs) to support the validation of system models, algorithms and protocols. This framework generates datasets via an improved linear congruential function, which can produce pseudo-random sequences based on user-specified seed values, and is suitable for generating 2D deployment coordinates and traffic matrices. EDGF specifically focuses on applications including random deployment, activity tracking and packet generation, and ensures the uniformity and randomness of the generated data through Kolmogorov-Smirnov test, χ² test and autocorrelation test. The datasets generated by EDGF have a wide range of application scenarios, including but not limited to simulation and validation of wireless networks, and aim to address the impact of randomness assumptions on network performance.
提供机构：
印度理工学院（印度矿业学院）计算机科学与工程系
创建时间：
2021-01-26
