EDGF: Empirical dataset generation framework for wireless network networks

TL;DR

This paper introduces EDGF, a framework for generating empirical datasets in wireless sensor networks, addressing validation challenges by modeling randomness and node deployment effects.

Contribution

It presents a novel algorithm for creating unified, pseudo-random datasets for WSNs, improving validation and reproducibility of simulation results.

Findings

Generated datasets reflect pseudo-randomness accurately.

The framework allows efficient regeneration of datasets using seed values.

Analysis of node deployment errors impacts on network performance.

Abstract

In wireless sensor networks (WSNs), simulation practices, system models, algorithms, and protocols have been published worldwide based on the assumption of randomness. The applied statistics used for randomness in WSNs are broad in nature, e.g., random deployment, activity tracking, packet generation, etc. Even though with adequate formal and informal information provided and pledge by authors, validation of the proposal became a challenging issue. The minuscule information alteration in implementation and validation can reflect the enormous effect on eventual results. In this proposal, we show how the results are affected by the generalized assumption made on randomness. In sensor node deployment, ambiguity arises due to node error-value ($\epsilon$), and it's upper bound in the relative position is estimated to understand the delicacy of diminutives changes. Moreover, the effect of uniformity in the traffic and contribution of scheduling position of nodes also generalized. We propose an algorithm to generate the unified dataset for the general and some specific applications system models in WSNs. The results produced by our algorithm reflects the pseudo-randomness and can efficiently regenerate through seed value for validation.