A node estimation technique in underwater wireless sensor network * M. S. Anower 1 , M. A. Motin 1 , A. S. M. Sayem 1 , and S. A. H. Chowdhury 2 1 Department of Electrical and Electronic Engineering 2 Department of Electronic and Telecommunication Engineering Rajshahi University of Engineering and Technology Kazla-6204, Rajshahi, Bangladesh { * md.shamimanower, motin_eee_06, sayem_ruet_eee}@yahoo.com; arif.1968.ruet@gmail.com Abstract—The number of nodes in a deployed wireless sensor network might vary due to ad hoc nature, natural disaster. Counting the number is very important in useful data collection, network maintenance, node localisation. Although protocols are being used to count the number in terrestrial networks, harsh environment limits their use in underwater networks. A statistical signal processing approach of node estimation is proposed in this paper. The nodes are considered as acoustic signal sources and their number is obtained through the cross-correlation of the acoustic signals received at two sensors in the network. The ratio of mean and standard deviation of the cross-correlation function is related with the number of nodes and used as the estimation parameter in the process. Theoretical and simulation results are provided which shows effectiveness of the signal processing approach instead of protocols in node estimation process. Keywords- cross-correlation, estimation parameter, mean of cross-correlation function, node estimation, standard deviation of cross-correlation function, underwater acoustic sensor networks (UASN) I. INTRODUCTION In wireless sensor networks there may be a large number of nodes deployed over a large area for a practical purpose such as climatic data collection or pollution monitoring. In other applications underwater wireless sensor networks deployed with a sufficient fraction of operating nodes that can communicate with each other for the purpose of environmental monitoring, seismic and acoustic monitoring to surveillance and national security, military and health care, discovering natural resources as well as locating man-made artefacts or extracting information for scientific analysis. Optimal performance requires a balance of the number of operating nodes, energy efficiency, and the lifetime of the network. So the number of operating nodes is a crucial factor for the networks. However, the number of operating nodes can vary with time due to various artificial as well as natural reasons (for example, some nodes might fail, some could be damaged, or batteries might fail). So, it is a matter of great interest for a communication network to know how many operating nodes or transmitters are available in the region at any point in time to ensure proper network operation (such as routing) as well as network maintenance (such as replacement of faulty nodes). There have been many investigations regarding the estimation technique. For example, protocols [1-8] have been used to estimate the number of tag IDs in radio frequency identification (RFID) systems, which is a similar problem to the estimation of the number of nodes in wireless communication networks. Similarly, a Good- Turing estimator of node estimation for terrestrial sensor networks has been proposed in Budianu et al. [9-11], where each transmitting node transmits its ID in every slot according to a certain probability and the packet collection can be modeled as an i.i.d. sampling with uniform distribution by SENMA protocol (an ALOHA-like protocol). In this method they estimate the number of operating sensors by deriving an expression for it as a function of missing mass. Although the abovementioned systems are easy to apply in RFID as well as terrestrial systems, they do not take into account the capture effect, which means that they are difficult to apply in UASN. One solution has been proposed in Howlader et al. [12, 13], which proposes a node estimation technique taking the capture effect into account. The procedure is similar to probabilistic framed slotted ALOHA [1]. However all of the abovementioned procedures for the estimation of the number of nodes in RFID systems and in wireless sensor networks are similar in that they are based on protocol design. But, underwater propagation characteristics [14] such as propagation delay, high absorption, and dispersion may make the use of protocol methods difficult. Using these conventional protocol-based techniques to obtain precise measurements is often expensive and inefficient. 978-1-4799-0400-6/13/$31.00 ©2013 IEEE