Statistical ON-OFF Link Modeling Based on Sea Trial Data

This paper presents a statistical ON-OFF link modeling approach for underwater acoustic networks (UANs) based on sea trial data. It aims to enable computationally efficient UAN simulation models that capture the complex temporal characteristics of underwater acoustic links. The main idea of the proposed method is to synthesize realistic link availability and outage patterns from empirical cumulative distribution functions (CDFs) derived from sea trial data. The Werbellin lake experiment dataset from ASUNA is used as a case study, representative of short range shallow water environments. In this dataset, weak correlation between link quality and distance and weak cross-correlation between different links allows us to model each link as an independent random process. However, we also propose a way of extending this method to generate multiple CDFs representing different link types, distances, node depths etc., all exhibiting different link statistics. The proposed statistical approach provides UAN researchers with a valuable tool for more realistic and efficient network simulation, supporting the development and evaluation of UAN protocols and systems. Additionally, it offers the potential to generate reproducible benchmark test environments for standardized protocol design evaluation.