Data transmission in Underwater Wireless Sensor Networks (UWSNs) is one of the key enablers used in technologies for future oceanmonitoring systems and other underwater applications. The use of acoustic waves in UWSNs suffer from a high propagation delay, as well as a limited available bandwidth due to the high-noise level, making the use of the different traditional existing protocols a major challenge in this environment. In this paper, we have chosen two TCP mechanisms already defined: TCP Vegas and TCP New Reno, to evaluate the effects of variable TCP packet size and TCP connection density in a subsea network under two common routing protocols, namely AODV and DSDV. The simulation results show that the performances of the two TCPs using DSDV routing protocol provide a more efficient result than those using the AODV routing protocol and that New Reno TCP gives better results than Vegas TCP in the UWSNs.
Data transmission in Underwater Wireless Sensor Networks (UWSNs) is one of the key enablers used in technologies for future oceanmonitoring systems and other underwater applications. The use of acoustic waves in UWSNs suffer from a high propagation delay, as well as a limited available bandwidth due to the high-noise level, making the use of the different traditional existing protocols a major challenge in this environment. In this paper, we have chosen two TCP mechanisms already defined: TCP Vegas and TCP New Reno, to evaluate the effects of variable TCP packet size and TCP connection density in a subsea network under two common routing protocols, namely AODV and DSDV. The simulation results show that the performances of the two TCPs using DSDV routing protocol provide a more efficient result than those using the AODV routing protocol and that New Reno TCP gives better results than Vegas TCP in the UWSNs.
Due to the complex underwater environment, conventional measurement and sensing methods used for land are difficult to apply directly in the underwater environment. Especially for seabed topography, it is impossible to perform long-distance and accurate detection by electromagnetic waves. Therefore, various types of acoustic and even optical sensing devices for underwater applications have been used. Equipped with submersibles, these underwater sensors can detect a wide underwater range accurately. In addition, the development of sensor technology will be modified and optimized according to the needs of ocean exploitation. In this paper, we propose a multiagent approach for optimizing the quality of monitoring (QoM) in underwater sensor networks. Our framework aspires to optimize the QoM by resorting to the machine learning concept of diversity. We devise a multiagent optimization procedure which is able to both reduce the redundancy among the sensor readings and maximize the diversity in a distributed and adaptive manner. The mobile sensor positions are adjusted iteratively using a gradient type of updates. The overall framework is tested through simulations based on realistic environment conditions. The proposed approach is compared to other placement approaches and is found to achieve a higher QoM with a smaller number of sensors.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.