Implementation Patterns of AquaSim for Simulation of Underwater Acoustic Wireless Sensor Networks

Now days, a number of corporate as well as social applications are connected with wireless technologies which are covered under the domain of Internet of Things (IoT) and Cloud of Things (CoT). To work with the development and implementation of these scenarios, there is need of high performance costly gadgets which are difficult for the self finance researchers and small organizations. To cope up with the financial aspects of developing and getting the results from advanced wireless environment, the use of simulators and software libraries is done. In wireless environment, the segment of Underwater Wireless Sensor (UWSN) or Underwater Acoustic Network (UAN) is quite prominent which are used for the underwater applications including Military, Naval and Underwater Surveillance. Underwater sensor networks are famous for researchers and engineers in wireless technology. This field offers a lot of academic work in different disciplines. The main challenge of this UWSN is the energy saving for sensor nodes. For this cause, the location of sensor nodes shifts about regularly. There are several algorithms for energy production and collection, but this area is still in need of study due to political and national security concerns.

Channel Modeling for Underwater Acoustic Network Simulation

This manuscript was submitted to IEEE Access on 12 Jun 2020.<div><br></div><div>Abstract:</div><div><br></div><div>Simulation forms an important part of the development and empirical evaluation of underwater acoustic network (UAN) protocols. The key feature of a credible network simulation model is a realistic channel model. A common approach to simulating realistic underwater acoustic (UWA) channels is by using specialised beam tracing software such as BELLHOP. However, BELLHOP and similar modeling software typically require knowledge of ocean acoustics and a substantial programming effort from UAN protocol designers to integrate it into their research. In this paper, we bridge the gap between low level channel modeling via beam tracing and automated channel modeling, e.g. via the World Ocean Simulation System (WOSS), by providing a distilled UWA channel modeling tutorial from the network protocol design point of view. The tutorial is accompanied by our MATLAB simulation code that interfaces with BELLHOP to produce channel data for UAN simulations. As part of the tutorial, we describe two methods of incorporating such channel data into network simulations, including a case study for each of them: 1) directly importing the data as a look-up table, 2) using the data to create a statistical channel model. The primary aim of this paper is to provide a useful learning resource and modeling tool for UAN protocol researchers. Initial insights into the UAN protocol design and performance provided by the statistical channel modeling approach presented in this paper demonstrate its potential as a powerful modeling tool for future UAN research.<br></div>

Channel Modeling for Underwater Acoustic Network Simulation

This manuscript was submitted to IEEE Access on 12 Jun 2020.<div><br></div><div>Abstract:</div><div><br></div><div>Simulation forms an important part of the development and empirical evaluation of underwater acoustic network (UAN) protocols. The key feature of a credible network simulation model is a realistic channel model. A common approach to simulating realistic underwater acoustic (UWA) channels is by using specialised beam tracing software such as BELLHOP. However, BELLHOP and similar modeling software typically require knowledge of ocean acoustics and a substantial programming effort from UAN protocol designers to integrate it into their research. In this paper, we bridge the gap between low level channel modeling via beam tracing and automated channel modeling, e.g. via the World Ocean Simulation System (WOSS), by providing a distilled UWA channel modeling tutorial from the network protocol design point of view. The tutorial is accompanied by our MATLAB simulation code that interfaces with BELLHOP to produce channel data for UAN simulations. As part of the tutorial, we describe two methods of incorporating such channel data into network simulations, including a case study for each of them: 1) directly importing the data as a look-up table, 2) using the data to create a statistical channel model. The primary aim of this paper is to provide a useful learning resource and modeling tool for UAN protocol researchers. Initial insights into the UAN protocol design and performance provided by the statistical channel modeling approach presented in this paper demonstrate its potential as a powerful modeling tool for future UAN research.<br></div>

Design and Implement Beacon based scheme for Node Localization in Underwater Acoustic Network

A network that can sense the surroundings and collected all the information from the sensor nodes and passed it to the base station is known as a wireless sensor network. The underwater acoustic networks are the type of network deployed under the oceans and passed information to the base station. Due to the dynamic nature of the network, nodes change their location at any time. To maximum aggregate information from the sensor nodes, to estimate exact node location is very important. The sensor node position estimation is a major issue of the underwater acoustic network. The process of estimating node position is called node localization. In the existing RSSI based approach for the node, localization has a high delay, which reduces its efficiency. The technique needs to be designed, which localizes more nodes in less amount of time. This research is based on the advancement of the range based scheme for node localization. In the proposed scheme, mobile beacons are responsible for node localization. The beacon nodes send beacon messages in the network, and sensor nodes respond back with a reply message. When two beacons receive the reply of a sensor node that is considered as a localized node, the sensor nodes which are already localized will not respond back to the beacon messages, which reduce delay in the network for node localization.

Channel Modeling for Underwater Acoustic Network Simulation

This manuscript was submitted to IEEE Communications Surveys & Tutorials on 18 Feb 2020.<div><br></div><div>Abstract:</div><div><br></div><div>Simulation forms an important part of the development and empirical evaluation of underwater acoustic network (UAN) protocols. The key feature of a credible network simulation model is a realistic representation of the underwater acoustic (UWA) channel characteristics. A common approach to obtaining a realistic UWA channel model is by using specialised software such as BELLHOP. However, BELLHOP and similar modeling software typically require knowledge of ocean acoustics and a substantial programming effort from UAN protocol designers to integrate it into their research. In this paper, we bridge the gap between low level channel modeling via software like BELLHOP and automated channel modeling, e.g. via the World Ocean Simulation System (WOSS), by providing a distilled UWA channel modeling tutorial from the network protocol design point of view. The tutorial is accompanied by our MATLAB simulation code that interfaces with BELLHOP to produce channel data for UAN simulations. As part of the tutorial, we describe two methods of incorporating such channel data into network simulations, including a case study for each of them: 1) directly importing the data as a look-up table, 2) using the data to create a statistical channel model. The primary aim of this tutorial is to provide a useful learning resource aimed at UAN protocol researchers without a background in underwater acoustics. However, the initial insights provided by the statistical channel modeling framework presented in this paper also show its great potential to serve as the channel modeling tool for future UAN research.<br></div>

A Linear Path Combined MAC Based Routing for Improving the Energy Efficiency in Underwater Acoustic Network

Energy is one of the important major constraints in all kinds of networks. Sensor nodes meets very less energy, it causes node death, where it creates various problems in data transmission. In this paper it is aimed to improve the energy efficiency by reducing the energy consumption. To do this, a linear path is constructed between end-points, nodes mode is changed into Sleep and Awake, Awake and Sleep and scheduling is applied. These three functionalities provide a very good data transmission with less energy consumption. Simulation of this proposed approach is carried out in NS2 software and the performance is verified in terms of Energy, throughput and delay