Abstract
underwater wireless acoustic sensor networks (UWASNs) have been used as an efficient means of communication to discover and extract data in aquatic environments. Applications of UWASNs include marine exploration, mine reconnaissance, oil and gas inspection, marine exploration, and border surveillance and military applications. However, these applications are limited by the huge volumes of data involved in detection, discovery, transmission, and forwarding. In particular, the transmission and receipt of large volumes of data require an exhaustive amount of time and substantial power to execute, and may still fail to meet real-time constraints. This shortcoming directed our research focus to the advancement of an underwater computer embedded system to meet the required limitations. Our research activities have included the extraction of valuable information from under the ocean using data mining approaches. We previously introduced real-time underwater system architectures that use a single computer. In this study, we extend our results and propose a new real-time underwater system architecture for large-scale networks. This architecture uses multiple computers to enhance its reliability. Determining the optimal locations of computers and their membership of acoustic sensors with minimum delay time, power consumption, and load balance is an NP-hard problem. We therefore propose a heuristic approach to find the optimal locations of computers and their membership of acoustic sensor nodes. We then develop sensor network topologies that reduce data-aggregation latency and data loss and increase the network lifespan. This paper merges heuristic solutions and topologies to achieve the best network performance. A simulation is performed to show the merit of our results and to measure the performance of our proposed solution.