Cross-correlation based approach of underwater network cardinality estimation with random placement of sensors

Cardinality estimation is a major concern for proper network operation and maintenance. Cardinality estimation of a network can be performed using protocol based techniques. But, underwater environment makes these protocol based methods inefficient in underwater network. To solve this problem, underwater network cardinality has been estimated using cross-correlation of the received Gaussian signals at multiple sensors. In cross-correlation based approach, the sensors are placed at the centre of the network for better estimation performance. This may not be possible in some practical cases. So, it is essential to introduce a new cardinality estimation technique which is equally effective for both central and random placement of sensors. In this paper, cardinality estimation is performed with central and random placement of sensors within the network which make the estimation process more flexible. It is observed from the investigation that, efficient estimation is possible with and without central placement of the sensors.