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Record W4404576566 · doi:10.1016/j.yofte.2024.104045

Relay aided UWOC-SMF-FSO based hybrid link for underwater wireless optical sensor network

2024· article· en· W4404576566 on OpenAlexaff
Jawad Mirza, Ahmad Atieh, Benish Kanwal, Salman Ghafoor, Ahmad Almogren, Firdos Kanwal, Imran Aziz

Bibliographic record

VenueOptical Fiber Technology · 2024
Typearticle
Languageen
FieldEngineering
TopicOptical Wireless Communication Technologies
Canadian institutionsOptiwave Systems (Canada)
FundersKing Saud University
KeywordsRelayComputer scienceUnderwaterElectronic engineeringWireless sensor networkLink (geometry)Optical linkComputer networkTelecommunicationsOptical fiberEngineeringPhysics

Abstract

fetched live from OpenAlex

The Internet of Underwater Things (IoUTs) connects underwater devices to communicate, sense surroundings, and transmit data. Acoustic communication faces bandwidth limitations, making underwater wireless optical communication-free space optics (UWOC-FSO) hybrid systems a promising alternative. However, maintaining sufficient power budget and signal-to-noise ratio (SNR) is a challenging task, making wavelength translation (WT) from visible to infrared (IR) at the water-fiber-air interface crucial for reliable signal transmission. In this paper, we propose an underwater wireless optical communication-single mode fiber-free space optics (UWOC-SMF-FSO) hybrid link based on a photo-detection, remodulate, and forwarding (PRF) relay and intensity modulation-direct detection (IM/DD) scheme for 8 × 1-Gb/s underwater optical wireless sensor network (UWOSN). The PRF relay is installed at a remotely operated underwater vehicle (ROV) to perform WT from visible range to IR. The performance of the sensors is analyzed for different water bodies and weather conditions of underwater and free space optics channels, respectively using metrics of Bit-error rate (BER) and Quality factor (Q-factor) employing Gamma–Gamma channel model. The simulation results show that forward-error correction (FEC) target BER of 10 −4 for sensors is achieved under different water bodies and weather conditions. The results obtained from this study show that the proposed UWOC-SMF-FSO hybrid link is flexible, resilient to adverse channel effects, and can be a potential candidate for implementation of high-speed long-distance future IoUTs. • The Internet of Underwater Things (IoUTs) is an emerging technology used to interconnect multitude of devices. Acoustic communication is customarily used for data transmission across IoUTs, but due to of limited bandwidth and low data rates, IoUTs based on underwater wireless optical communication-free space optics (UWOC-FSO) hybrid links are catching the significant attention. However, the major issue for hybrid links is maintaining sufficient power budget and signal-to-noise ratio (SNR) at receiver which are required for optimum performance due to the attenuation of the transmitted signal. Therefore, wavelength translation (WT) from visible range to infrared (IR) at water-fiber-air interface is a crucial option to enable the reliable transmission of signals across different media. • We proposed an underwater wireless optical communication-single mode fiber-free space optics (UWOC-SMF-FSO) hybrid link based on a photo-detection, remodulate, and forwarding (PRF) relay and intensity modulation-direct detection (IM/DD) scheme for 8x1-Gb/s underwater optical wireless sensor network (UWOSN). The PRF relay is installed at a remotely operated underwater vehicle (ROV) to perform WT from visible range to IR. • The performance of the sensors is analyzed for different water bodies and weather conditions of underwater and free space optics channels, respectively using metrics of Bit-error rate (BER) and Quality factor (Q-factor) employing Gamma–Gamma channel model.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.013
GPT teacher head0.241
Teacher spread0.228 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations20
Published2024
Admission routes1
Has abstractyes

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