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Securing Underwater Wireless Communication with Frequency-Hopping Spread Spectrum

2024· article· en· W4404954558 on OpenAlexfundno aff
Khaliq Ur Rahman, Nazila Fough, Christopher D. McDermott, Rida Sundas, Nauman Anwar Baig

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicUnderwater Vehicles and Communication Systems
Canadian institutionsnot available
FundersFederation for the Humanities and Social Sciences
KeywordsFrequency-hopping spread spectrumSpread spectrumWirelessComputer scienceUnderwaterUnderwater acoustic communicationComputer networkTelecommunicationsGeographyCode division multiple access

Abstract

fetched live from OpenAlex

The significance of subsea communication has increased substantially, with growing interest in replacing wired communication with wireless alternatives. However, subsea communication faces several challenges due to the harsh environment, including high noise, significant multipath propagation, signal attenuation, salinity, and temperature variations. Additionally, it is vulnerable to cyber-attacks. Ensuring secure and reliable communication for authorized users remains a significant challenge, requiring an integrated approach that balances efficiency, stability, and security considerations. Current efforts primarily focus on designing multimodal wireless communication systems, but integrating robust security measures into subsea wireless infrastructure is challenging due to its inherently open nature and susceptibility to external interference. Technical advancements are necessary to strengthen the security of subsea wireless communication while managing power loss effectively. To address these challenges, we propose a mechanism that implements a pre-determined frequency hopping schedule, enhancing security and improving reliability through periodic changes in transmission frequency. The hopping sequence is shared in advance among authorized parties. While the current system does not adapt to real-time threats, future work could explore dynamic adjustments in response to detected threats or unauthorized access attempts.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.567
Threshold uncertainty score0.493

Codex and Gemma teacher scores by category

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 communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.011
GPT teacher head0.203
Teacher spread0.192 · 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 teacher head, 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

Citations4
Published2024
Admission routes1
Has abstractyes

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