Determination of the Number of Sensor Nodes Transmitting Data In Dynamic Underwater Acoustic Sensor Networks

2020 ◽  
Author(s):  
Nukhet Sazak
Keyword(s):  
Sensor Networks ◽  
Sensor Nodes ◽  
Acoustic Sensor ◽  
Underwater Acoustic Sensor Networks ◽  
Underwater Acoustic ◽  
Acoustic Sensor Networks

scholarly journals Optimal Deployment of Vector Sensor Nodes in Underwater Acoustic Sensor Networks

Sensors ◽  
2019 ◽  
Vol 19 (13) ◽  
pp. 2885 ◽  
Author(s):  
Sunhyo Kim ◽  
Jee Woong Choi
Keyword(s):  
Sensor Networks ◽  
Particle Velocity ◽  
Sensor Nodes ◽  
Acoustic Sensor ◽  
Underwater Acoustic Sensor Networks ◽  
Communication Performance ◽  
Underwater Acoustic ◽  
Acoustic Sensor Networks ◽  
Vector Sensor ◽  
Optimal Deployment

Underwater acoustic sensor networks have recently attracted considerable attention as demands on the Internet of Underwater Things (IoUT) increase. In terms of efficiency, it is important to achieve the maximum communication coverage using a limited number of sensor nodes while maintaining communication connectivity. In 2017, Kim and Choi proposed a new deployment algorithm using the communication performance surface, which is a geospatial information map representing the underwater acoustic communication performance of a targeted underwater area. In that work, each sensor node was a vertically separated hydrophone array, which measures acoustic pressure (a scalar quantity). Although an array receiver is an effective system to eliminate inter-symbol interference caused by multipath channel impulse responses in underwater communication environments, a large-scale receiver system degrades the spatial efficiency. In this paper, single-vector sensors measuring the particle velocity are used as underwater sensor nodes. A single-vector sensor can be considered to be a single-input multiple-output communication system because it measures the three directional components of particle velocity. Our simulation results show that the optimal deployment obtained using single-vector sensor nodes is more effective than that obtained using a hydrophone (three-channel vertical-pressure sensor) array.

scholarly journals Distortion Performance of Underwater Acoustic Sensor Networks

2015 ◽  
Vol 2015 ◽  
pp. 1-6
Author(s):  
Andrej Stefanov
Keyword(s):  
Sensor Networks ◽  
Sensor Nodes ◽  
Rician Fading ◽  
Acoustic Sensor ◽  
Underwater Acoustic Sensor Networks ◽  
Limited Region ◽  
Underwater Acoustic ◽  
Numerical Examples ◽  
Acoustic Sensor Networks ◽  
The Impact

The paper studies the distortion performance of multihop underwater acoustic sensor networks. The network is composed of bottom mounted sensor nodes and the sensor to sensor links experience Rician fading. The distortion is evaluated for the case when there is interference from other sensors in the network. The focus is on the sustainable number of hops in the network for a maximum allowed (target) route distortion requirement. Numerical examples are provided that illustrate the results of the analysis and the regions where the network operation is limited, namely, the coverage-limited region and the interference-limited region. The paper also considers the impact of retransmissions on the distortion performance. It is found that the network connectivity and robustness improve with automatic repeat request (ARQ). The improvements are manifested as a reduction of the regions of limited performance, that is, an increase of the region where the network exhibits full connectivity. The analysis results are illustrated through numerical examples.

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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