Topology Optimization of Long-Thin Sensor Networks in Under-Ice Environments

Long-thin network topologies are useful in monitoring tunnels and bridges because of their linear structure. In this paper, we apply the long-thin concept to under-ice acoustic sensor networks in the Arctic Ocean. We develop an acoustic wave propagation model for the under-ice environments using the...

Full description

Saved in:
Bibliographic Details
Published in:IEEE journal of oceanic engineering 2019-10, Vol.44 (4), p.1264-1278
Main Authors: Liu, Shengxing, Song, Aijun, Shen, Chien-Chung
Format: Article
Language:English
Subjects:
Acoustic propagation
Acoustic waves
Acoustics
Arctic
Arctic environments
Arctic zone
Bridges
Coefficients
Communication
Computer simulation
Energy consumption
Epontic environment
Evolutionary computation
Ice environments
IP (Internet Protocol)
Long thin
Monte Carlo simulation
Network topologies
Network topology
Sea ice
Seawater
sensor networks
Sensors
Sound propagation
Sound waves
Statistical methods
Topology
Topology optimization
Transmission loss
Tunnels
under-ice communication
Underwater communication
Wave propagation
Wireless sensor networks
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Long-thin network topologies are useful in monitoring tunnels and bridges because of their linear structure. In this paper, we apply the long-thin concept to under-ice acoustic sensor networks in the Arctic Ocean. We develop an acoustic wave propagation model for the under-ice environments using the ray-tracing method. Specifically, we derive the reflection coefficients of plane acoustic waves penetrating from seawater to ice in a ray-based model. The impulse responses and transmission loss, including their dependencies on the communication range, are examined in the under-ice environment. Furthermore, to maximize the operational lifetime of the under-ice long-thin network, we optimize the network topology by integrating Monte Carlo simulations with a differential evolution technique. The optimization results are presented for long-thin sensor network scenarios under the Arctic sea ice. Computer simulations show that by optimizing the sensor locations in the long-thin topology, one can take advantage of the sound convergent and divergent effects of the under-ice acoustic channel. As a result, the energy consumption of the long-thin sensor network can be significantly reduced so that its operational lifetime can be extended in the Arctic environment.
ISSN:0364-9059
1558-1691
DOI:10.1109/JOE.2018.2861958