Optimizing environmentally-conditioned communications in an underwater autonomous network (UAN) through acoustic models and experimental data: results from LCAS 18

This paper investigates how robot navigation strategies can improve the communication performance among the nodes of a deployed underwater autonomous network (UAN). To this aim, we present an algorithm that evaluates the impact of environmental conditions and network configurations in the connectivity performance of an UAN. The algorithm exploits acoustic propagation models and data from experimental campaigns for the definition of the so-called communication performance function (CPF). The CPF estimates the probability of successful packet delivery by correlating it with the environmental conditions and the output of acoustic propagation models with past communication data. Once the CPF is designed, it offers a way to assess the communication performance of certain geometric configurations. The environmental and communication data from the littoral continuous active sonar (LCAS18) sea-trial are analysed for the definition and estimation of the CPF. The resulting CPF shows the coherence achieved with the LCAS18 dataset.