Design of Algorithms and Protocols for Underwater Acoustic Wireless Sensor Networks

2020 ◽  
Vol 53 (6) ◽  
pp. 1-34 ◽  
Author(s):  
Azzedine Boukerche ◽  
Peng Sun
Author(s):  
Rodrigo Santos ◽  
Javier Orozco ◽  
Matías Micheletto ◽  
Sergio F. Ochoa ◽  
Roc Meseguer ◽  
...  

2014 ◽  
Vol 548-549 ◽  
pp. 1530-1535
Author(s):  
Lin Zou ◽  
De Feng Huang ◽  
Roberto Togneri

Delay tolerance is a major design concern for supporting underwater acoustic wireless sensor networks (UA-WSNs) to carry out tasks in harsh subsea environments. Due to the great difference between the underwater acoustic channel and the radio frequency channel, most of the existing routing protocols developed for terrestrial wireless sensor networks perform poorly in underwater acoustic communications. In this work, we present a Neural-Q-Learning algorithm based delay tolerant protocol for UA-WSNs. Due to the advantages of the artificial neural network along with the Q-Learning algorithm, the ferry node is capable of determining an optimal route in a two-dimensional continuous space to relay packets effectively and efficiently between sensors. Simulation results show that the delivery delay and delivery cost of the network significantly decrease by maximizing the meeting probability between the ferry node and sensors.


2020 ◽  
Vol 13 (4) ◽  
pp. 136-155
Author(s):  
Basaprabhu S. Halakarnimath ◽  
Ashok V. Sutagundar

The deployment of acoustic sensor nodes in 3-D underwater acoustic wireless sensor networks (UAWSN) is a difficult task due to various aquatic conditions and physical obstacles. This work proposes multi-agent-based acoustic sensor node deployment (MASD) to deploy the acoustic nodes at ideal positions to enhance coverage and seamless connectivity. The proposed scheme works is threefold: 1) AUV initiates random walk in the network to gather the information and prospective common reference points; 2) the base station gets this information through surface buoys and computes the routing path, feasible locations for deploying new nodes; and 3) AUV collects this information and follows the path to deploy nodes with the help of agents. The multi-agent-enabled deployment framework (MADF) is proposed to support the deployment process at each level of the proposed MASD scheme. The performance of propagation loss, coverage, and overhead tradeoffs are analyzed to validate the proposed scheme. Mobility issues can be further re-investigated in shallow water as a future direction to the MASD scheme.


Sensors ◽  
2014 ◽  
Vol 14 (1) ◽  
pp. 795-833 ◽  
Author(s):  
Salvador Climent ◽  
Antonio Sanchez ◽  
Juan Capella ◽  
Nirvana Meratnia ◽  
Juan Serrano

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