Exploiting Propagation Delay in Underwater Acoustic Communication Networks via Deep Reinforcement Learning

Geng, Xuan; Zheng, Yahong Rosa

doi:10.1109/tnnls.2022.3170050

Cited by 19 publications

(4 citation statements)

References 30 publications

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“…In UWSNs, depth-based layering MAC (DL-MAC) provides energy-saving and collisionfree characteristics. DL-MAC addresses near-far effects as well as hidden/exposed terminal concerns in underwater networks, in addition to spatial temporal uncertainty [89]. The suggested innovative protocol employs layering and a distributed clustering algorithm to efficiently communicate data packet exchanges, while reducing the likelihood of data collisions inside a network, hence improving the network's overall energy efficiency [90,91].…”

Section: Depth-based Layering Mac (Dl Mac)mentioning

confidence: 99%

A Comprehensive Survey of Energy-Efficient MAC and Routing Protocols for Underwater Wireless Sensor Networks

et al. 2022

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Underwater wireless sensor networks (UWSNs) have become highly efficient in performing different operations in oceanic environments. Compared to terrestrial wireless sensor networks (TWSNs), MAC and routing protocols in UWSNs are prone to low bandwidth, low throughput, high energy consumption, and high propagation delay. UWSNs are located remotely and do not need to operate with any human involvement. In UWSNs, the majority of sensor batteries have limited energy and very difficult to replace. The uneven use of energy resources is one of the main problems for UWSNs, which reduce the lifetime of the network. Therefore, an energy-efficient MAC and routing techniques are required to address the aforementioned challenges. Several important research projects have been tried to realize this objective by designing energy-efficient MAC and routing protocols to improve efficient data packet routing from Tx anchor node to sensor Rx node. In this article, we concentrate on discussing about different energy-efficient MAC and routing protocols which are presently accessible for UWSNs, categorize both MAC and routing protocols with a new taxonomy, as well as provide a comparative discussion. Finally, we conclude by presenting various current problems and research difficulties for future research.

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Section: Depth-based Layering Mac (Dl Mac)mentioning

confidence: 99%

A Comprehensive Survey of Energy-Efficient MAC and Routing Protocols for Underwater Wireless Sensor Networks

et al. 2022

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“…I N ORDER to understand and explore the ocean, many underwater sensor nodes, including multibeam swath bathymeter, sonar array, and acoustic Doppler current profiler, have been deployed to form the underwater sensor networks (USNs) [1], [2]. The deployment of USNs can increase the Jing Yan, Wenqiang Cao, and Xian Yang are with the Institute of Electrical Engineering, Yanshan University, Qinhuangdao 066004, China (e-mail: jyan@ysu.edu.cn; cwq@stumail.ysu.edu.cn; xyang@ysu.edu.cn).…”

Section: Introductionmentioning

confidence: 99%

Communication-Efficient and Collision-Free Motion Planning of Underwater Vehicles via Integral Reinforcement Learning

Yan

Cao

Yang

et al. 2024

IEEE Trans. Neural Netw. Learning Syst.

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Motion planning of underwater vehicles is regarded as a promising technique to make up the flexibility deficiency of underwater sensor networks (USNs). Nonetheless, the unique characteristics of underwater channel and environment make it challenging to achieve the above mission. This article is concerned with a communication-efficient and collision-free motion planning issue for underwater vehicles in fading channel and obstacle environment. We first develop a model-based integral reinforcement learning (IRL) estimator to predict the stochastic signal-to-noise ratio (SNR). With the estimated SNR, an integrated optimization problem for the codesign of communication efficiency and motion planning is constructed, in which the underwater vehicle dynamics, communication capacity, collision avoidance, and position control are all considered. In order to tackle this problem, a model-free IRL algorithm is designed to drive underwater vehicles to the desired position points while maximizing the communication capacity and avoiding the collision. It is worth mentioning that, the proposed motion planning solution in this article considers a realistic underwater communication channel, as well as a realistic dynamic model for underwater vehicles. Finally, simulation and experimental results are demonstrated to verify the effectiveness of the proposed approach.

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“…MAC is one of the core protocols of UASN, which determines the channel usage and distribution mode in UASN. Compared with terrestrial wireless sensor networks, MAC protocol for UASN faces the following difficulties in design [1]:…”

Section: Introductionmentioning

confidence: 99%

MR-SFAMA-Q: A MAC Protocol based on Q-Learning for Underwater Acoustic Sensor Networks

Wei-Kai Sun,

Xiao-Mei Wang

et al. 2024

Journal of Computers

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<p>In recent years, with the rapid development of science and technology, many new technologies have made people’s exploration of the ocean deeper and deeper, and due to the requirements of national defense and marine development, the underwater acoustic sensor network (UASN) has been paid more and more attention. Nevertheless, the underwater acoustic channel has the properties of considerable propagation delay, limited bandwidth, and unstable network topology. In order to improve the performance of the medium access control (MAC) protocol in UASN, we propose a new MAC protocol based on the Slotted-FAMA of Multiple Reception (MR-SFAMA) protocol. The protocol uses the Q-Learning algorithm to optimize the multi-receiver handshake mechanism. The current state is judged according to the received node request, and the Q-table is established. Through the multi-round interaction between the node and the environment, the Q-table is continuously updated to obtain the optimal strategy and determine the optimal data transmission scheduling scheme. The reward function is set according to the total back-off time and frame error rate, which can reduce the packet loss rate during network data transmission while reducing the delay. In addition, the matching asynchronous operation and uniform random back-off algorithm are used to solve the problem of long channel idle time and low channel utilization. This new protocol can be well applied to unstable network topology. The simulation results show that the protocol performs better than Slotted-FAMA and MR-SFAMA regarding delay and normalized throughput.</p> <p> </p>

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Exploiting Propagation Delay in Underwater Acoustic Communication Networks via Deep Reinforcement Learning

Cited by 19 publications

References 30 publications

A Comprehensive Survey of Energy-Efficient MAC and Routing Protocols for Underwater Wireless Sensor Networks

A Comprehensive Survey of Energy-Efficient MAC and Routing Protocols for Underwater Wireless Sensor Networks

Communication-Efficient and Collision-Free Motion Planning of Underwater Vehicles via Integral Reinforcement Learning

MR-SFAMA-Q: A MAC Protocol based on Q-Learning for Underwater Acoustic Sensor Networks

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