Global Network Connectivity Assessment via Local Data Exchange for Underwater Acoustic Sensor Networks

Abstract

Underwater sensor networks consist of a number of fixed or mobile sensors, deployed in the underwater environment. The sensors are capable of sending/receiving data using acoustic, optical, or radio-frequency communication channels [1], [2], [3]. A typical objective for such networks is to perform data aggregation for applications as diverse as environmental monitoring, underwater exploration, disaster prevention, climate reporting, and mine detection [1], [4], [5].The acoustic communication channel is the most typical physical layer technology used in underwater sensors. Unlike the communication channels used in the terrestrial sensor networks, there are several sources of uncertainty which influence the communication between underwater nodes such as underwater currents, temperature fluctuations, multi-path fading, ambient noise, and sound speed profile [6], [7], [8]. Furthermore, these sources of uncertainty vary over time and space in an unpredictable manner, and hence one can use random graphs to model the underwater sensor networks more effectively [9]. This results in a time-varying network structure with the possibility of edge addition/deletion. Also, the node deletion can occur as a probable scenario for underwater applications due to limited sensor battery life [10].

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Document Details

Document Type
Technical Report
Publication Date
Mar 31, 2014
Accession Number
AD1004223

Entities

People

  • A. Ajorlou
  • A. G. Aghdam
  • H. Mahboubi
  • J. Habibi
  • M. M. Asadi

Organizations

  • Concordia University

Tags

Communities of Interest

  • Sensors

DTIC Thesaurus Topics

  • Acoustic Detectors
  • Acoustic Propagation
  • Acoustic Waves
  • Algorithms
  • Bernoulli Distribution
  • Communication Channels
  • Contracts
  • Convergence
  • Detectors
  • Information Exchange
  • Intervals
  • Learning
  • National Security
  • Networks
  • Probability
  • Random Variables
  • Sensor Networks

Readers

  • Acoustical Oceanography.
  • Neural Network Machine Learning.
  • Sensor Fusion and Tracking Systems.

Technology Areas

  • Space