Authored by Sadman Saffaf Ahmed
To overcome the common problem, i.e., protocol complexity, of conventional underwater node estimation techniques, a statistical signal processing technique was proposed as an alternative. The technique uses cross-correlation as signal processing tool and hence it is called cross-correlation based underwater node estimation technique. This technique can overcome the protocol complexities, human interaction, over cost, etc., of conventional techniques. However, to introduce the readers with different technical aspects regarding this technique, the paper is introduced. We have used mean, and standard deviation as our estimation parameter to estimate nodes. At the same time, we have illustrated a brief discussion on effect of signal length, underwater bandwidth, etc., simultaneously in this paper. This writing can boost the researches in this field.
Introduction
Use of protocol-based techniques for node estimation in in underwater wireless communication network (UWCN) is very difficult because of the propagation characteristics of underwater environment. Hence, in this thesis, a statistical tool-based estimation process is implemented, and this tool is called crosscorrelation. However, a matter is greatly important that the nodes can the distributed according to different types of distributions. Six different types of distributions called exponential distribution, normal distribution, Rayleigh distribution, gamma distribution, Weibull distribution and chi-squire distribution, are considered with respect to two sensors, as the distribution of numerous nodes. The estimation of nodes at each of these distribution cases is the key work in this thesis. Though the thesis is performed for underwater environment, it can be equally implemented to other network environments such as space communication networks (SCNs), terrestrial communication networks (TCNs), and underground communication networks (UGCNs). In ad hoc type of networks, number of active nodes can vary with time and that’s why counting the number of active nodes is very useful for proper operation and maintenance of the networks. The key importance of node estimation of different types of network is given bellow [1].• Maintaining coverage area in wireless sensor network (WSN)
• Providing proper identification of tagged body in RFID system. To count speakers in multi-speaker teleconferencing system
• To regain network topology
• Assisting traffic management in mobile ad hoc network
• Background noise calculation
In the recent advancement of underwater communication, the underwater wireless sensor networks become one of the most effective areas due to its importance. Both acoustic waves [2,3] and magnetic waves [4,5] are used here. However, Underwater acoustic sensor networks (UASN) are more flexible form of communication in underwater environment, which have several applications, such as monitoring daily ocean life, seismic and volcanic prediction, oceanographic data collection, pollution monitoring, deep-sea archaeology, seawater data collection [6], offshore exploration, climatic data collection, disaster alleviation [7], environmental research, oil/gas spills keeping track of, tactical surveillance.
In UWCNs, the signal propagation is completely different than the signal propagation in TCNs because of changing of medium. Relative to TCNs, more challenges are faced at UWCNs for signal propagation due to its water-medium. There are three possible physical waves used in UWCNs as underwater communication carriers, electromagnetic (EM) wave, optical wave and acoustic wave. Because of being high frequency of EM wave, it suffers from very high absorption in underwater environment, therefore, can only propagate extremely short distances [8]. Optical waves have a rapid attenuation in water due to absorption and backscattering [9]. Underwater acoustic communication poses the limitation of long and variable delays, high path losses, strong background noises and multipath of signal propagation [10]. Despite of these limitations acoustic waves are commonly used in UWCNs, as they can propagate over long distances [11]. In this thesis, the proposed node estimation technique is performed in underwater wireless acoustic sensor networks (UWASNs).
Node estimation of underwater network is difficult using terrestrial node estimation methods because there are some characteristics of underwater environment such as large propagation latency, node mobility, non-negligible capture effect, and high error rate which creates problem in estimation. For this reason, node estimation method based on cross- correlation of Gaussian signals is used for underwater network. In this process different estimation parameters can be formulated from the crosscorrelation function (CCF). However, cross-correlation is a novel signal processing tool which is used in fish estimation to node estimation [12-18]. Cross-correlation based node estimation using mean and standard deviation is the main goal of this paper. We have used three sensors straight line case in this paper.
Cross-Correlation Based Technique
There have been many investigations regarding the use of the ambient noise cross-correlation to extract the time-domain GF in various environments and frequency ranges of interest, for example, underwater acoustics. The procedural steps for determining the noise CCF are similar for all the above-mentioned environments. In brief, the procedure is as follows: firstly, the signals from a number of different noise sources are collected by two sensors separated by a certain distance in the region of interest; secondly, the received signals are summed at each of the two sensor locations; and, finally, these two noise signals are cross-correlated.
Gaussian signal has a certain property that, cross-correlation of two Gaussian signals results a delta function, which is the basic idea of this estimation method and also the reason of using Gaussian signals as transmitted signals. The propagation velocity is assumed to be constant, which is the sound velocity ‘Sp’ in the medium (Figure 1).
Most researchers have only tried to retrieve an estimate of the GF; for example, it has been shown theoretically that the GF can be obtained with ambient noise cross-correlation in the simple case of a homogeneous medium with attenuation.
Estimation Process
Two sensors in line
To estimate the number of nodes we use CCF formulation. For this formulation, a 3D spherical region is considered as network, which contains N evenly distributed nodes. Two sensors send probe request and every node in the network transmit Gaussian signals at the same time in respond to that probe request using acoustic wave as the underwater communication carrier. Simultaneously transmitted Gaussian signals form N nodes summed at each sensor location with different delay differences to form composite Gaussian signals and these composite signals are then received by the sensors.Bins, b in the CCF (Figure 2) is defined as a place occupied by a delta inside a space of a width twice the distance between sensors and that place is determined by the delay difference of the signal coming to the sensors [13,16-20]. The deltas of equal delay differences are placed in that particular bin.
To read more about this article...Open access Journal of Engineering Sciences
Please follow the URL to access more information about this article
https://irispublishers.com/gjes/fulltext/cross-correlation-based-underwater-node-estimation-technique-and-a-concise-summary-of-different-effects-on-it.ID.000516.php
To know more about our Journals... Iris Publishers
To know about Open Access publisher 
No comments:
Post a Comment