SEANet G2

Demirors, Emrecan; Shi, Jiacheng; Guida, Raffaele; Melodia, Tommaso

doi:10.1145/2999504.3001112

Cited by 31 publications

(3 citation statements)

References 29 publications

(32 reference statements)

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“…The bulk of the available literature on WPT in underwater networks is based on inductive and capacitive coupling [7]- [10], [18], [23], [24]. Inductive coupling is based on electromagnetic induction, whereby two conductors are configured in such a way as to induce a voltage in one conductor due to a change in electric current in the other.…”

Section: Literature Reviewmentioning

confidence: 99%

“…The parameter Γ is defined as {Γ ∈ R : 0 ≤ Γ ≤ 1}. Equation (23) shows that the agent obtains the highest reward by moving to a position that maximises both throughput and harvested power. However, the agent must take such actions intelligently to conserve its battery, which is also limited in supply.…”

Section: A Joint Maximisation Of Throughput and Wpt As An Rl Problemmentioning

confidence: 99%

“…If the AUV passes through a node and there is data to transmit, the reward is similar to that given in Eq. (23). Otherwise, the reward is −P 𝑚 .…”

Section: A Joint Maximisation Of Throughput and Wpt As An Rl Problemmentioning

confidence: 99%

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Toward a Sustainable Internet of Underwater Things Based on AUVs, SWIPT, and Reinforcement Learning

Omeke,

Mollel,

Shah

et al. 2024

IEEE Internet Things J.

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Life on Earth depends on healthy oceans, which supply a large percentage of the planet's oxygen, food, and energy. However, the oceans are under threat from climate change, which is devastating the marine ecosystem and the economic and social systems that depend on it. The Internet-of-underwaterthings (IoUTs), a global interconnection of underwater objects, enables round-the-clock monitoring of the oceans. It provides high-resolution data for training machine learning (ML) algorithms for rapidly evaluating potential climate change solutions and speeding up decision-making. The sensors in conventional IoUTs are battery-powered, which limits their lifetime, and constitutes environmental hazards when they die. In this paper, we propose a sustainable scheme to improve the throughput and enable wireless charging of underwater networks, enabling them to potentially operate indefinitely. The scheme is based on simultaneous wireless information and power transfer (SWIPT) from an autonomous underwater vehicle (AUV) used for data collection. We model the problem of jointly maximising throughput and harvested power as a Markov Decision Process (MDP), and develop a model-free reinforcement learning (RL) solution. The model's reward function incentivises the AUV to find optimal trajectories that maximise throughput and power transfer to the underwater nodes while minimising its own energy consumption. To the best of our knowledge, this is the first attempt at using RL for this application. The scheme is implemented in an open 3D RL environment specifically developed in MATLAB for this study. The performance results show up 207% improvement in energy efficiency compared to those of a random trajectory scheme used as a baseline model.Index Terms-wireless underwater sensor networks, machine learning, reinforcement learning, internet-of-underwater-things, simultaneous wireless and information transfer, and wireless power transfer (SWIPT), autonomous underwater vehicles (auv).

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Section: Literature Reviewmentioning

confidence: 99%

Section: A Joint Maximisation Of Throughput and Wpt As An Rl Problemmentioning

confidence: 99%

See 1 more Smart Citation

Toward a Sustainable Internet of Underwater Things Based on AUVs, SWIPT, and Reinforcement Learning

Omeke,

Mollel,

Shah

et al. 2024

IEEE Internet Things J.

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Software-Defined Multimodal Underwater Wireless Sensor Network Platform Powered by Seawater Battery

Wang

Zhao

et al. 2020

Communications in Computer and Information Science

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Reinforcement Learning-Based Adaptive Transmission in Time-Varying Underwater Acoustic Channels

Wang

Sun

et al. 2018

IEEE Access

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Partial differential equations (PDEs) are commonly employed to model complex industrial systems characterized by multivariable dependence. Existing physics-informed neural networks (PINNs) excel in solving PDEs in a homogeneous medium. However, their feasibility is diminished when PDE parameters are unknown due to a lack of physical attributions and time-varying interface is unavailable arising from heterogeneous media. To this end, we propose a data-physics-hybrid method, physically informed synchronic-adaptive learning (PISAL), to solve PDEs for industrial systems modeling in heterogeneous media. First, Net 1 , Net 2 , and Net I , are constructed to approximate the solutions satisfying PDEs and the interface. Net 1 and Net 2 are utilized to synchronously learn each solution satisfying PDEs with diverse parameters, while Net I is employed to adaptively learn the unavailable time-varying interface. Then, a criterion combined with Net I is introduced to adaptively distinguish the attributions of measurements and collocation points. Furthermore, Net I is integrated into a data-physics-hybrid loss function. Accordingly, a synchronic-adaptive learning (SAL) strategy is proposed to decompose and optimize each subdomain. Besides, we theoretically prove the approximation capability of PISAL. Extensive experimental results verify that the proposed PISAL can be used for industrial systems modeling in heterogeneous media, which faces the challenges of lack of physical attributions and unavailable time-varying interface.Note to Practitioners-The motivation behind this paper is to devise a method for industrial systems modeling, even in cases where unknown PDE parameters are caused by a lack of physical attributions and an unavailable time-varying interface is caused by heterogeneous media. Existing methods apply domain decomposition technique under the assumption that the interface is available. To this end, a data-physics-hybrid method, PISAL, in which Net 1 , Net 2 , and Net I with SAL strategy are proposed to solve PDEs for industrial system modeling in heterogeneous media. Net 1 , Net 2 , and Net I are first constructed to learn the solutions satisfying PDEs and the time-varying interface. Net 1 and Net 2 are for synchronously learning each solution satisfying PDEs with diverse parameters; Net I is for adaptively learning the unavailable time-varying interface. Subsequently, a criterion combined with Net I is introduced, which is to adaptively distinguish different physical attributions of measurements and collocation points. Additionally, Net I is integrated into a dataphysics-hybrid loss function. Accordingly, a SAL strategy is proposed to decompose and optimize each subdomain. Besides, we theoretically prove the approximation capability of PISAL. To validate the efficacy of the proposed PISAL, the classical two-phase Stefan problem and the mixed Navier-Stokes problem are employed. Results highlight the feasibility of our method in industrial systems modeling with the abovementioned challenges and demonstrate some com...

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SEANet G2

Cited by 31 publications

References 29 publications

Toward a Sustainable Internet of Underwater Things Based on AUVs, SWIPT, and Reinforcement Learning

Toward a Sustainable Internet of Underwater Things Based on AUVs, SWIPT, and Reinforcement Learning

Software-Defined Multimodal Underwater Wireless Sensor Network Platform Powered by Seawater Battery

Reinforcement Learning-Based Adaptive Transmission in Time-Varying Underwater Acoustic Channels

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