scholarly journals A Feasibility Analysis of an Application-Based Partial Initialization (API) Protocol for Underwater Wireless Acoustic Sensor Networks

Sensors ◽  
2020 ◽  
Vol 20 (19) ◽  
pp. 5635
Author(s):  
Changho Yun ◽  
Suhan Choi

Initialization methods for underwater wireless acoustic sensor networks (UWASNs) have been proposed as a subset of other network protocols under the simple assumption that all the nodes in the network can be initialized at once. However, it is generally time- and energy-intensive to initialize all nodes in a UWASN due to unstable underwater channel conditions. To improve network efficiency, we propose the Application-based Partial Initialization (API) protocol, which initializes only the same number of nodes as the number of activated nodes required to run a specific application. Reducing the number of active nodes is also particularly advantageous underwater since the replacement of batteries is costly. To the best of our knowledge, the API is the first approach that initializes nodes partially according to applications. Thus, we investigate the feasibility of the API for a UWASN by analyzing its performance via simulations. From the results, it is shown that the API provides similar data statistics compared with the conventional full initialization that initializes all nodes. Moreover, the API outperforms the full initialization in terms of the initialization time and message overhead performances.

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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