RealTime 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 realtime 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 realtime 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 realtime 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 realtime 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 realtime constraints. The system performance is assessed in terms of the endtoend 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.

CAPTAIN: A Data Collection algorithm for underwater opticalAcoustic sensor networks Abstract   Underwater sensor networks are used to collect data from aquatic environments. Nodes from these networks usually communicate via acoustic or optical transmissions due to the poor performance of radiofrequency communication in these environments. Acoustic transmissions achieve longer distances than optical ones, but also consume more energy and have lower bandwidth. Underwater opticalacoustic sensor networks (UOASNs) combine both types of communication to explore the best of each one. In this paper, we propose CAPTAIN, an algorithm to perform data collection with data aggregation in UOASNs. CAPTAIN divides the network into clusters, builds a routing tree, and uses data aggregation to deliver all data collected to the sink node. Experiments showed that, compared to the shortest path algorithm, CAPTAIN led to lower network energy consumption, especially in denser networks, where it was able to consume, on average, up to 73% less. CAPTAIN could also achieve lower average latencies (up to almost 83% lower) and higher rates of data collected per hour by the sink node using fewer acoustic transmissions in clustered networks.