Joint Relay Selection and Power Allocation for Time-Varying Energy Harvesting-Driven UASNs: A Stratified Reinforcement Learning Approach

Han, Song; Li, Luo; Liu, Zhixin; Yan, Lei; Zhang, Tongwei

doi:10.1109/jsen.2022.3203028

Cited by 11 publications

(5 citation statements)

References 22 publications

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“…It can be used for powerering sensor nodes and monitoring the locomotion of the fish. The authors in [90] enables a relay node to harvest energy from the marine environment when source-destination nodes communicate via the relay node. This enhances the operational lifetime of the battery of the relay node and, therefore, the overall throughput of the network is enhanced.…”

Section: E Energy Harvesting From Marine Lifementioning

confidence: 99%

“…Energy harvesting using solar cells[57]-[61], AUV motion or kinetic energy[62]-[78] and marine life[79]-[90]. The symbol x indicates unspecified metric value.…”

mentioning

confidence: 99%

“…(Cont). Energy harvesting using solar cells[57]-[61], AUV motion or kinetic energy[62]-[78] and marine life[79]-[90]. The symbol x means unspecified value.VOLUME 4, 2016 This article has been accepted for publication in IEEE Access.…”

mentioning

confidence: 99%

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Energy Harvesting in Underwater Acoustic Wireless Sensor Networks: Design, Taxonomy, Applications, Challenges and Future Directions

et al. 2022

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In underwater acoustic wireless sensor networks (UAWSNs), energy harvesting either enhances the lifetime of a network by increasing the battery power of sensor nodes or ensures battery-less operation of nodes. This, in effect, results in sustainable and reliable operation of the network deployed for various underwater applications. This work provides a survey of the energy harvesting techniques for UAWSNs. Our work is unique than the existing work on underwater energy harvesting in that it includes state-of-the art techniques designed in the last decade. It analyzes every harvesting scheme in terms of its main idea, merits, demerits and the extent of the harvested power (energy). The description of the merits results in selection of the suitable scheme for the suitable underwater applications. The demerits of the addressed schemes provide an insight to their future enhancement and improvement. Moreover, the harvested techniques are classified into various categories depending upon the involved energy harvesting mechanism and compared based on the maximum and minimum amount of harvested power, which helps in selection of the suitable category keeping in view the power budget of an underwater network before deployment. The challenges in energy harvesting and in UAWSNs are described to provide an insight to them and to address them for further enhancement in the harvested extent. Finally, research directions are specified for future investigation.

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Section: E Energy Harvesting From Marine Lifementioning

confidence: 99%

“…Energy harvesting using solar cells[57]-[61], AUV motion or kinetic energy[62]-[78] and marine life[79]-[90]. The symbol x indicates unspecified metric value.…”

mentioning

confidence: 99%

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Energy Harvesting in Underwater Acoustic Wireless Sensor Networks: Design, Taxonomy, Applications, Challenges and Future Directions

et al. 2022

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“…In response, the authors in [13] proposed to combine DQN and DPG into a deep deterministic policy gradient (DDPG) algorithm based on the actor-critic (AC) framework, which can solve high-dimensional continuous action space problems. Based on this, S. Han et al proposed a DDPG strategy to optimize the continuous power allocation [14]. However, it takes the nodes as individual agents and does not consider the collaborative learning of the agents.…”

Section: Introductionmentioning

confidence: 99%

Power Allocation Based on Multi-Agent Deep Deterministic Policy Gradient for Underwater Acoustic Communication Networks

Geng,

Hui

2024

Electronics

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This paper proposes a reinforcement learning-based power allocation for underwater acoustic communication networks (UACNs). The objective function is formulated as maximizing channel capacity under constraints of maximum power and minimum channel capacity. To solve this problem, a multi-agent deep deterministic policy gradient (MADDPG) approach is introduced, where each transmitter node is considered as an agent. Given the definition of a Markov decision process (MDP) model for this problem, the agents learn to collaboratively maximize the channel capacity by deep deterministic policy gradient (DDPG) learning. Specifically, the power allocation of each agent is obtained by a centralized training and distributed execution (CTDE) method. Simulation results show the sum rate achieved by the proposed algorithm approximates that of the fractional programming (FP) algorithm and improves by at least 5% compared with the DQN (deep Q-learning network) -based power allocation algorithm.

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“…Deep Neural Networks (DNNs) and Deep Reinforcement Learning (DRL) have recently been applied to relay selection problems in EH networks to provide low complexity, high accuracy, and robust solutions [13], [14]. [13] applies deep deterministic policy gradient (DDPG) and deep Q-network (DQN) algorithms for joint relay selection and power allocation problem with the objective of cumulative throughput maximization in single-source-multiple-relay networks. [14] proposes DNN-based solution for the relay selection problem with the objective of maximum throughput in single-source-multiple-relay SWIPT networks.…”

mentioning

confidence: 99%

Relay Selection, Scheduling, and Power Control in Wireless-Powered Cooperative Communication Networks

Onalan

Salik

Coleri

2020

IEEE Trans. Wireless Commun.

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Radio Frequency Energy Harvesting (RF-EH) networks are key enablers of massive Internet-ofthings by providing controllable and long-distance energy transfer to energy-limited devices. Relays, helping either energy or information transfer, have been demonstrated to significantly improve the performance of these networks. This paper studies the joint relay selection, scheduling, and power control problem in multiple-source-multiple-relay RF-EH networks under nonlinear EH conditions.We first obtain the optimal solution to the scheduling and power control problem for the given relay selection. Then, the relay selection problem is formulated as a classification problem, for which two convolutional neural network (CNN) based architectures are proposed. While the first architecture employs conventional 2D convolution blocks and benefits from skip connections between layers; the second architecture replaces them with inception blocks, to decrease trainable parameter size without sacrificing accuracy for memory-constraint applications. To decrease the runtime complexity further, teacher-student learning is employed such that the teacher network is larger, and the student is a smaller size CNN-based architecture distilling the teacher's knowledge. A novel dichotomous searchbased algorithm is employed to determine the best architecture for the student network. Our simulation results demonstrate that the proposed solutions provide lower complexity than the state-of-art iterative approaches without compromising optimality.

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Joint Relay Selection and Power Allocation for Time-Varying Energy Harvesting-Driven UASNs: A Stratified Reinforcement Learning Approach

Cited by 11 publications

References 22 publications

Energy Harvesting in Underwater Acoustic Wireless Sensor Networks: Design, Taxonomy, Applications, Challenges and Future Directions

Energy Harvesting in Underwater Acoustic Wireless Sensor Networks: Design, Taxonomy, Applications, Challenges and Future Directions

Power Allocation Based on Multi-Agent Deep Deterministic Policy Gradient for Underwater Acoustic Communication Networks

Relay Selection, Scheduling, and Power Control in Wireless-Powered Cooperative Communication Networks

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