Underwater network size estimation using cross-correlation: Selection of estimation parameter

Size estimation is very important for a network's proper operation, but it is difficult to estimate in underwater environment using conventional protocol based techniques. An alternative approach of node estimation based on cross-correlation of the signals from the nodes is proposed in this paper, which can be applied to any environment networks, from underwater to space. In this process, different parameters (sum, mean, standard deviation, ratio of standard deviation to the mean) of cross-correlation function (CCF) are used for node estimation. A relative comparison has been discussed which in turn leads us to select the suitable estimation parameter of network size estimation.