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PCon: A Novel Opportunistic Routing Protocol for Duty-Cycled Internet of Underwater Things

2019· article· en· W3003738595 on OpenAlexaff
Rodolfo W. L. Coutinho, Azzedine Boukerche

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicUnderwater Vehicles and Communication Systems
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsComputer networkComputer scienceRouting protocolUnderwaterNetwork packetInternet ProtocolDuty cycleEnergy consumptionUnderwater acoustic communicationThe InternetReal-time computingEngineeringElectrical engineeringVoltage

Abstract

fetched live from OpenAlex

Internet of Underwater Things (IoUTs) has emerged as an evolution of traditional underwater wireless sensor networks, with programmable nodes interconnected to the Internet. Despite the advancements, IoUTs will still face critical challenges imposed by the use of the lossy and energy-hungry underwater acoustic channel. Two major critical challenges faced by IoUT applications are the low reliable data delivery due to the poor quality of underwater acoustic links, and the high energy cost for underwater wireless communication. In this paper, we tackle both challenges by proposing the PCon protocol. The PCon is a power-controlled opportunistic routing protocol for data routing in duty-cycled IoUTs. At each hop, the PCon protocol selects the most suitable transmission power, from a set of discrete transmission power levels, to maintain a reasonable data delivery ratio while reducing the energy consumption of duty-cycled IoUTs. To do so, the PCon takes into consideration the energy cost for delivering the data packet, calculated as a function if the probability of having the next-hop node awake during the transmission. Simulation results showed that the PCon protocol, even in a harsh scenario of duty-cycling of 50%, ensures a packet delivery rate of 40% while decreases the energy cost in 78%.

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: none
Teacher disagreement score0.945
Threshold uncertainty score0.409

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 head0.047
GPT teacher head0.271
Teacher spread0.224 · 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

Citations6
Published2019
Admission routes1
Has abstractyes

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