Underwater acoustic sensor networks have emerged as a new technology for underwater real-time applications such as oil inspection, seismic monitoring, and disaster prevention. However, this new technology is bound to data sensing, transmission, and forwarding, which makes the transmission of large volumes of data costly in terms of both time and power. This has inspired our research activities to develop underwater computing systems. In this advanced technology, information is extracted under the water using embedded processors via data mining and/or data compression. In our previous study, we developed a new set of real-time underwater embedded system architectures that can handle various network configurations. The aim was to minimize end-to-end delay and power consumption based on network parameters (i.e., data rate, central processing node capabilities, gathering node capabilities, and water depth) for both homogenous and heterogeneous applications. In this study, we developed a data-gathering algorithm that divides sensor nodes into clusters to find the best location for master nodes and their sensor members and the best location for the central computer. The system performance is calculated in terms of minimum end-to-end delay, power consumption, and load balancing among master nodes. Simulation is used to verify the results and to evaluate the performance of various sensor topologies.