scholarly journals DCO-MAC: A Hybrid MAC Protocol for Data Collection in Underwater Acoustic Sensor Networks

Sensors ◽  
2018 ◽  
Vol 18 (7) ◽  
pp. 2300 ◽  
Author(s):  
Min Deng ◽  
Huifang Chen ◽  
Lei Xie

In underwater acoustic sensor networks (UASNs), medium access control (MAC) is an important issue because of its potentially significant effect on the network performance. However, designing a suitable MAC protocol for the UASN is challenging because of the specific characteristics of the underwater acoustic channel and network, such as limited available bandwidth, long propagation delay, high bit-error-rate, and sparse network topology. In addition, as the traffic load is non-uniformly distributed in a UASN for data collection, it is essential to consider the application feature for the MAC protocol. In this paper, we propose a MAC protocol in a data-collection-oriented UASN, abbreviated as the DCO-MAC protocol. In the proposed protocol, the network is partitioned into two kinds of sub-networks according to the traffic load. A contention-based MAC protocol is used in the sub-network with a light traffic load, while a reservation-based MAC protocol is used in the sub-network with a heavy traffic load. Meanwhile, the DCO-MAC protocol supports the access of mobile nodes. The theoretical analysis and simulation results demonstrate that, in a UASN for data collection, the proposed MAC protocol outperforms the other existing MAC protocols, in terms of the network throughput, end-to-end packet delay, energy overhead, and fairness.

Sensors ◽  
2021 ◽  
Vol 21 (7) ◽  
pp. 2284
Author(s):  
Ibrahim B. Alhassan ◽  
Paul D. Mitchell

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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