scholarly journals Stochastic medium access control for underwater acoustic sensor networks

2016 ◽  
Author(s):  
Yu Han
Keyword(s):  
Sensor Networks ◽  
Access Control ◽  
Medium Access Control ◽  
Acoustic Sensor ◽  
Underwater Acoustic Sensor Networks ◽  
Medium Access ◽  
Stochastic Medium ◽  
Underwater Acoustic ◽  
Acoustic Sensor Networks

Cooperative Protocol for Medium Access Control in Underwater Acoustic Sensor Networks

2018 ◽  
pp. 133-142
Author(s):  
Lucas S. Cerqueira ◽  
Alex B. Vieira ◽  
Luiz F. M. Vieira ◽  
Marcos A. M. Vieira ◽  
José Augusto M. Nacif
Keyword(s):  
Sensor Networks ◽  
Access Control ◽  
Medium Access Control ◽  
Acoustic Sensor ◽  
Underwater Acoustic Sensor Networks ◽  
Medium Access ◽  
Underwater Acoustic ◽  
Acoustic Sensor Networks ◽  
Cooperative Protocol

scholarly journals Packet Flow Based Reinforcement Learning MAC Protocol for Underwater Acoustic Sensor Networks

Sensors ◽  
2021 ◽  
Vol 21 (7) ◽  
pp. 2284
Author(s):  
Ibrahim B. Alhassan ◽  
Paul D. Mitchell
Keyword(s):  
Sensor Networks ◽  
Mac Protocol ◽  
Propagation Delay ◽  
Basic Function ◽  
Acoustic Sensor ◽  
Underwater Acoustic Sensor Networks ◽  
Medium Access ◽  
Underwater Acoustic ◽  
Q Learning ◽  
Acoustic Sensor Networks

Medium access control (MAC) is one of the key requirements in underwater acoustic sensor networks (UASNs). For a MAC protocol to provide its basic function of efficient sharing of channel access, the highly dynamic underwater environment demands MAC protocols to be adaptive as well. Q-learning is one of the promising techniques employed in intelligent MAC protocol solutions, however, due to the long propagation delay, the performance of this approach is severely limited by reliance on an explicit reward signal to function. In this paper, we propose a restructured and a modified two stage Q-learning process to extract an implicit reward signal for a novel MAC protocol: Packet flow ALOHA with Q-learning (ALOHA-QUPAF). Based on a simulated pipeline monitoring chain network, results show that the protocol outperforms both ALOHA-Q and framed ALOHA by at least 13% and 148% in all simulated scenarios, respectively.

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