AUV Trajectory Optimization for an Optical Underwater Sensor Network in the Presence of Ocean Currents

Mahmoodi, Khadijeh Ali; Uysal, Murat

doi:10.1109/blackseacom52164.2021.9527844

Cited by 2 publications

(5 citation statements)

References 27 publications

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“…Therefore, the power of the AUV trajectory can be expressed as

where

denotes the Euclidean vector norm and

is the density of water.

and

indicate the efficiency of the AUV’s propulsion system, the drag coefficient and the wetted surface area, respectively [ 7 ]. Consequently, with the relations in ( 4 ), ( 6 ), ( 11 )–( 13 ) and ( 16 ), the total energy consumption can be expressed as

where

is a weighted parameter that measures the balance between the energy consumption of the sensor node and that of the AUV.…”

Section: Multi-modal Data Collection Analysismentioning

confidence: 99%

“…To solve the aforementioned problems, autonomous underwater vehicles (AUVs) have been rapidly developed in recent years in terms of data storage and signal processing capabilities, which can better enable underwater mobile data collection. Moreover, the durability and mobility of AUVs alleviate the unbalanced energy consumption problem of underwater sensors [ 6 , 7 ].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

AUV-Aided Optical—Acoustic Hybrid Data Collection Based on Deep Reinforcement Learning

Luo

et al. 2023

Sensors

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Autonomous underwater vehicles (AUVs)-assisted mobile data collection in underwater wireless sensor networks (UWSNs) has received significant attention because of their mobility and flexibility. To satisfy the increasing demand of diverse application requirements for underwater data collection, such as time-sensitive data freshness, emergency event security as well as energy efficiency, in this paper, we propose a novel multi-modal AUV-assisted data collection scheme which integrates both acoustic and optical technologies and takes advantage of their complementary strengths in terms of communication distance and data rate. In this scheme, we consider the age of information (AoI) of the data packet, node transmission energy as well as energy consumption of the AUV movement, and we make a trade-off between them to retrieve data in a timely and reliable manner. To optimize these, we leverage a deep reinforcement learning (DRL) approach to find the optimal motion trajectory of AUV by selecting the suitable communication options. In addition to that, we also design an optimal angle steering algorithm for AUV navigation under different communication scenarios to reduce energy consumption further. We conduct extensive simulations to verify the effectiveness of the proposed scheme, and the results show that the proposed scheme can significantly reduce the weighted sum of AoI as well as energy consumption.

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“…Therefore, the power of the AUV trajectory can be expressed as

where

denotes the Euclidean vector norm and

is the density of water.

and

where

is a weighted parameter that measures the balance between the energy consumption of the sensor node and that of the AUV.…”

Section: Multi-modal Data Collection Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

AUV-Aided Optical—Acoustic Hybrid Data Collection Based on Deep Reinforcement Learning

Luo

et al. 2023

Sensors

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show abstract

“…In [27], assuming VLC for data transmission, the trajectory finding problem of the AUV is solved using a greedy algorithm under VoI constraints. While the works in [23]- [27] simply assume that the AUV follows a straight path between each two sensor nodes, our previous work [28] further considers the effect of ocean currents and optimizes the trajectory between each two nodes.…”

Section: Introductionmentioning

confidence: 99%

“…The underlying assumption in above works [18]- [28] is that the AUV is battery powered. Battery-powered AUVs are required to return to the shore or the support vessel after a certain period in order to get recharged whether or not their mission is complete.…”

Section: Introductionmentioning

confidence: 99%

“…In previous works where battery-powered AUVs are considered [18]- [28], the energy consumption is mainly related to the mission trajectory defined to cover a given area of interest. Energy harvesting (EH) aware trajectory optimization for solar-powered AUVs might indicate a conflicting trajectory to maximize the harvested energy, e.g., being closer to sea surface.…”

Section: Introductionmentioning

confidence: 99%

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Energy Aware Trajectory Optimization of Solar Powered AUVs for Optical Underwater Sensor Networks

Mahmoodi

Uysal

2022

IEEE Trans. Commun.

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Visible light communication (VLC) provides an alternative underwater wireless connectivity solution with its low latency and high data rates albeit at relatively shorter distances in the order of tens of meters. In the context of underwater sensor networks (USNs), VLC is particularly suitable to establish connectivity between "data mule" autonomous underwater vehicles (AUVs) and sensor nodes since communications is enabled only when the sensor node and mule AUV are in close proximity. In this paper, we consider a USN scenario where a solar-powered AUV gathers data from the sensor nodes using VLC signaling. We formulate a three-dimensional trajectory optimization for solar-powered AUVs with the goal of maximizing the harvested energy under constraints imposed by the data transmission. The optimization constraints include the minimum required data transfer rate, therefore a corresponding transmission distance, between the sensors and the AUV. We formulate the problem as a bilevel optimization problem. The lower-level objective function is in the form of traveling salesman problem which determines the optimum sequence order of the sensor nodes to be visited while the upper-level objective function is the optimization of the trajectory between each pair of adjacent nodes for the given order of node visits. Our numerical results demonstrate that the proposed trajectory significantly prolongs the mission time and autonomous operation of the AUV without the need to return to home base. Furthermore, since the proposed trajectory optimization is reactive to ocean currents, it brings reductions in the energy consumption of the AUVs.

show abstract

AUV Trajectory Optimization for an Optical Underwater Sensor Network in the Presence of Ocean Currents

Cited by 2 publications

References 27 publications

AUV-Aided Optical—Acoustic Hybrid Data Collection Based on Deep Reinforcement Learning

AUV-Aided Optical—Acoustic Hybrid Data Collection Based on Deep Reinforcement Learning

Energy Aware Trajectory Optimization of Solar Powered AUVs for Optical Underwater Sensor Networks

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