scholarly journals PC-MAC: A Prescheduling and Collision-Avoided MAC Protocol for Underwater Acoustic Sensor Networks

2017 ◽  
Vol 2017 ◽  
pp. 1-8 ◽  
Author(s):  
Zhigang Jin ◽  
Shenyang Xiao ◽  
Yishan Su ◽  
Yajing Li
Keyword(s):  
Sensor Networks ◽  
Mac Protocol ◽  
Collision Probability ◽  
Acoustic Sensor ◽  
Underwater Acoustic Sensor Networks ◽  
Underwater Acoustic ◽  
Control Packet ◽  
Acoustic Sensor Networks ◽  
The Impact ◽  
Control Packets

The impact of the acoustic modem with long preamble characteristic on the collision feature of the media access control scheme in underwater acoustic sensor networks (UANs) is evaluated. It is observed that the collision probability is relatively high due to the extremely long duration of preamble. As a result, UANs generally have much lower network throughput. To address this problem, a prescheduling MAC protocol named PC-MAC for UANs is proposed, which leverages a novel prescheduling scheme for the exchange of control packet to alleviate the collision probability among control packets. PC-MAC is a reservation-based channel access scheme. In the proposed protocol, an extra guard time is introduced to avoid the influence of dynamic spatial-temporal uncertainty of the sender and receiver positions. Simulation results show that PC-MAC outperforms classic reservation-based MAC protocol named SFAMA in terms of network goodput and end-to-end delay and lowers collision probability among control packets in two representative network scenarios.

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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