Real-Time Decision Making for Underwater Big Data Applications Using the Apriori Algorithm

Underwater acoustic sensor networks (UWASNs) have been introduced as a new technology to extract the data for underwater real-time applications such as seismic monitoring, undersea monitoring and control, oil well inspection, military applications, and disaster prevention. This new technology adds more networking capabilities and enables real-time reporting. However, it is restricted to data sensing, forwarding and data transmission. Specifically, transmitting large volumes of data takes a long time and requires a lot of power to be executed. This has inspired our research activities to focus on building an underwater real-time computing system with the minimum execution time and low power consumption. In our research, valuable information is extracted underwater using data mining or compression algorithms. In our previous study, we proposed a set of real-time underwater embedded system (RTUWES) architectures that can handle multiple network configurations. In this study, we extend our research results to develop information extraction algorithms for big data underwater applications to meet real-time constraints. The system performance is assessed in terms of the end-to-end delay and power consumption. We have built a simulator for practical study. The simulation results are presented to demonstrate the performance of our proposed system based on an information extraction algorithm.