Weighted Record Sample for Underwater Seismic Monitoring Application

8 pages•Published: March 13, 2019

Hussain Albarakati, Reda Ammar and Raafat Elfouly

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.

Keyphrases: architecture, information extraction, multipath., real time constraints, underwater acoustic sensor networks, underwater embedded system

In: Gordon Lee and Ying Jin (editors). Proceedings of 34th International Conference on Computers and Their Applications, vol 58, pages 99-106.

Links:	https://easychair.org/publications/paper/RPrM
	https://doi.org/10.29007/flck

BibTeX entry

@inproceedings{CATA2019:Weighted_Record_Sample_Underwater,
  author    = {Hussain Albarakati and Reda Ammar and Raafat Elfouly},
  title     = {Weighted Record Sample for Underwater Seismic Monitoring Application},
  booktitle = {Proceedings of 34th International Conference on Computers and Their Applications},
  editor    = {Gordon Lee and Ying Jin},
  series    = {EPiC Series in Computing},
  volume    = {58},
  publisher = {EasyChair},
  bibsource = {EasyChair, https://easychair.org},
  issn      = {2398-7340},
  url       = {/publications/paper/RPrM},
  doi       = {10.29007/flck},
  pages     = {99-106},
  year      = {2019}}

Download PDF Open PDF in browser