Weighted Record Sample for Underwater Seismic Monitoring Application

Abstract

Underwater acoustic sensor networks have been developed as a new technology for real-time underwater applications, including seismic monitoring, disaster prevention, and oil well inspection. Unfortunately, this new technology is constrained to data sensing, large-volume transmission, and forwarding. As a result, the transmission of large volumes of data is costly in terms of both time and power. We thus focused our research activities on the development of embedded underwater computing systems. In this advanced technology, information extraction is performed underwater using data mining techniques or compression algorithms. We previously presented a new set of real-time underwater embedded system architectures that can manage multiple network configurations. In this study, we extend our research to develop information extraction for seismic monitoring underwater application to meet real-time constraints. The system performance is measured in terms of the minimum end-to-end delay and power consumption. The simulation results are presented to measure the performance of our architecture based on the information extraction algorithm.