S. A. H. Chowdhury

A node estimation technique in underwater wireless sensor network

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.

Effect of sensor number and location in cross-correlation based node estimation technique for underwater communications network

In underwater wireless sensor network (UWSN), node estimation poses a great challenge using conventional protocol techniques due to underwater propagation characteristics. For this reason, a cross-correlation based technique is applied to estimate the number of nodes, which is equally suitable for any environment networks. The nodes are considered as acoustic signal sources in an underwater network and sensors are considered as receiving nodes. In this paper, the estimation of the number of nodes (N) in a spherical region of an underwater acoustic sensor network (UASN) has been investigated by cross-correlating the acoustic signals received at three sensors to analyze the effect of sensor number and location.

A signal processing approach of underwater network node estimation with three sensors

Estimation of the number of nodes in a communication network is very important, particularly in ad hoc networks. But it is difficult to estimate in underwater wireless sensor networks due to long propagation delay, high absorption, and dispersion. Cross-correlation, a statistical signal processing approach is applied for this purpose, which is suitable for any environment networks. Nodes are considered as transmitters and sensors are considered as receivers. In this work, a node estimation technique for underwater networks is proposed by cross-correlating the signals received at three sensors.

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

Size estimation is very important for a networks 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.

Effect of bandwidth in cross-correlation based underwater network size estimation

Due to the harshness of underwater environment, size estimation of underwater network is difficult using conventional protocol techniques. A statistical signal processing approach of size estimation is proposed for this purpose using cross-correlation of Gaussian signals, which is effective for any environment networks. Limited bandwidth of underwater communications channel poses constraint on using Gaussian signal, which has infinite bandwidth. The aim of this paper is to investigate the effect of this limited bandwidth in cross-correlation based underwater network size estimation using Gaussian signals with finite bandwidth.

Performance comparison of underwater network size estimation techniques

Nowadays, size number of nodes estimation has received much attention in the field of wireless sensor networks. Due to the harshness of underwater environment, size estimation is difficult in underwater wireless sensor network using existing methods of terrestrial networks. For this purpose, cross-correlation CC-based size estimation techniques using two and three sensors have been proposed by the authors in their previous works. In this paper, estimation performances of these CC-based techniques are compared with other underwater network size estimation approaches. It is observed from the comparison that, three sensor case of CC-based method shows better performance in terms of accuracy and estimation time than those of the other methods.

Effect of signal length in cross-correlation based underwater network size estimation

Signal length possesses a very important role in size estimation of underwater wireless sensor network (UWSN). As size estimation is very tough in UWSNs using conventional protocol techniques, a cross-correlation based technique is introduced to estimate the number of nodes. In UWSN, The greater the signal length, the more energy is required to perform the estimation. In this paper, we observe the effect of signal length (Ns) over the number of estimated nodes (N) in a spherical region of an underwater acoustic sensor network (UASN) using three sensors and investigate the error in node estimation for different Ns. As the theoretical required Ns is infinity (Ns = 106 samples is used in simulation), using minimum Ns how it is possible to go through a successful estimation process i.e., accurate node estimation is being observed and discussed.

Mitigating the effect of multipath using cross-correlation: application to underwater network cardinality estimation

Number of node cardinality is an important parameter for a networks proper operation and this parameter can be determined using conventional protocol-based techniques, but these techniques have several limitations in underwater environment due to large propagation latency, high error rate, node mobility, non-negligible capture effect and high path loss. To develop a suitable cardinality estimation technique in presence of unique characteristics of underwater network, a statistical signal processing approach based on cross-correlation of Gaussian signals received at multiple sensors has been proposed in our previous works. In the initial formulation of this approach, a simplified model has been investigated without considering the multipath propagation effect. One of the common challenges for underwater wireless communication is multipath propagation of signals which result in inter-symbol interference ISI. An important feature of cross-correlation is its ability to mitigate the multipath effect which is demonstrated in this paper and applied to our previously proposed estimation scheme for multipath compensation. It is shown that, due to high dispersion coefficient of the medium, multipath effects on estimation results are negligible and a generalised process of multipath compensation for any dispersion factor is obtained to improve the robustness of this scheme.

Transmit energy calculation in cross-correlation based underwater network cardinality estimation

Transmit energy is a very important performance indicator of network cardinality estimation techniques. It is more significant for underwater networks as battery lifetime of nodes depend on it. To overcome the difficulty of underwater network cardinality estimation (in presence of large propagation latency, high error rate, node mobility, non-negligible capture effect and high path loss) using existing methods based on protocols, a cross-correlation based approach has been introduced. In this approach, the transmitted Gaussian signals from all nodes are received by the sensors and then cross-correlated. Here, sensors are referred to as receiving nodes, whereas nodes are considered as transmitting nodes. It is necessary to determine the total transmit energy required to perform estimation. An expression has been developed in this paper to calculate the energy required for Gaussian signal transmission in corss-correlation based cardinality estimation scheme and verified by simulation.

Secure wireless multicasting through co-existing MIMO radio systems

We consider the problem of secure wireless multicasting through multiple-input multiple-output (MIMO) radio system in which two co-existing users communicate with two groups of receivers in the presence of multiple eavesdroppers. At first, we calculate the secrecy multicast capacity of each group and investigate the impact of interferences on it. Then, we use zero-forcing (ZF) precoding at a relay to enhance the secrecy multicast capacity of each group removing the impact of interferences. Finally, we propose a scheme to improve the secrecy multicast capacity of each group making use of interference energy that already exists in the communication medium employing selective precoding at the relay. In the proposed scheme, interference provides an additional source of energy that enhances secrecy multicast capacity at each group without the need to increase the transmitted power. Our results show that exploiting free interference power, the performance of coexisting wireless multicasting can be improved without affecting the performance of each other.