scholarly journals Multi-Hop-Enabled Energy-Efficient MAC Protocol for Underwater Acoustic Sensor Networks

2015 ◽  
Vol 4 (3) ◽  
pp. 226-250 ◽  
Author(s):  
Khaja Shazzad ◽  
Kemal Tepe ◽  
Esam Abdel-Raheem
Keyword(s):  
Sensor Networks ◽  
Energy Efficient ◽  
Mac Protocol ◽  
Acoustic Sensor ◽  
Underwater Acoustic Sensor Networks ◽  
Underwater Acoustic ◽  
Acoustic Sensor Networks

scholarly journals An Improved Multi-Hop-Enabled Energy Efficient MAC Protocol for Underwater Acoustic Sensor Networks

2017 ◽  
Vol 13 (15) ◽  
pp. 240
Author(s):  
Tomal Kumer Mozumder ◽  
Sajjad Waheed
Keyword(s):  
Energy Efficiency ◽  
Sensor Networks ◽  
Energy Efficient ◽  
Mac Protocol ◽  
Multiple Time ◽  
Acoustic Sensor ◽  
Contention Resolution ◽  
Underwater Acoustic Sensor Networks ◽  
Underwater Acoustic ◽  
Acoustic Sensor Networks

Packet collisions occurred by hidden and local nodes in multi-hop enabled underwater acoustic sensor networks (UWASNs) have effect on throughput, energy efficiency and end-to-end delay. Existing Multi-HopEnabled Energy Efficient MAC Protocol for Underwater Acoustic Sensor Networks (MHEE MAC) utilized a double-phase contention resolution mechanism, which causes visit multiple time slot and energy overhead. In this paper, we propose a MAC protocol that use contention resolution mechanism with unique priority to provide energy efficiency. First, local nodes are eliminated comparing their priority and later, hidden nodes are mitigated. A simulation of proposed protocol is also developed to analyze the performance. Results obtained through simulation show that the proposed protocol achieves significantly lower energy consumption, reserve more energy and more stable throughput compared to MHEE-MAC, T-Lohi and slotted floor acquisition multiple access (S-FAMA).

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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