Abstract: The focus of this paper is on developing an early detection and warning system for near-field tsunami to mitigate its impact on communities at risk. This a challenging task, given the stringent reliability and timeliness requirements, the development of such an infrastructure entails. To address this challenge, we propose a hybrid infrastructure, which combines cheap but unreliable undersea sensors with expensive but highly reliable fiber optic, to meet the stringent constraints of this warning system. The derivation of a low-cost tsunami detection and warning infrastructure is cast as an optimization problem, and a heuristic approach is used to determine the minimum cost network configuration that meets the targeted reliability and timeliness requirements. To capture the intrinsic properties of the environment and model accurately the main characteristics of the sound wave propagation undersea, the proposed optimization framework incorporates the Bellhop propagation model and accounts for significant environment factors, including noise, varying undersea sound speed and sea floor profile. We apply our approach to a region which is prone to near-field tsunami threats to derive a cost-effective under sea infrastructure for detection and warning. For this case study, the results derived from the proposed framework show that a feasible infrastructure, which operates with a carrier frequency of 12-KHz, can be deployed in calm, moderate and severe environments and meet the stringent reliability and timeliness constraints, namely 20 minutes warning time and 99 % data communication reliability, required to mitigate the impact of a near-field tsunami. The proposed framework provides useful insights and guidelines toward the development of a realistic detection and warning system for near-field tsunami.

X. Xerandy, Taieb Znati and Louise K Comfort, “Cost-Effective, Cognitive Undersea Network for Timely and Reliable Near-Field Tsunami Warning” International Journal of Advanced Computer Science and Applications(IJACSA), 6(7), 2015. http://dx.doi.org/10.14569/IJACSA.2015.060730

@article{Xerandy2015,
title = {Cost-Effective, Cognitive Undersea Network for Timely and Reliable Near-Field Tsunami Warning},
journal = {International Journal of Advanced Computer Science and Applications}
doi = {10.14569/IJACSA.2015.060730},
url = {http://dx.doi.org/10.14569/IJACSA.2015.060730},
year = {2015},
publisher = {The Science and Information Organization},
volume = {6},
number = {7},
author = {X. Xerandy and Taieb Znati and Louise K Comfort},
}