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Review on Energy Efficient V2V Communication Techniques for a Dynamic and Congested Traffic Environment

2022· article· en· W4220670973 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

Venue2022 International Conference on Computer Communication and Informatics (ICCCI) · 2022
Typearticle
Languageen
FieldEngineering
TopicAdvanced MIMO Systems Optimization
Canadian institutionsHorizon College and Seminary
Fundersnot available
KeywordsComputer scienceWirelessEfficient energy useInformation exchangeBeamformingField (mathematics)Energy (signal processing)Computer networkDistributed computingTelecommunicationsEngineeringElectrical engineering

Abstract

fetched live from OpenAlex

High Speed Electric vehicles are very significant in urban environment to accomplish the information exchange wirelessly. Meanwhile, energy crisis that the present generation is facing is contributed in major by wireless communication, specifically in (V2V) domain. As the traffic grows and the information content is quite bulky, V2V faces many challenges in terms of energy utilization and power wastage. Modern wireless architectures and allied researches need to be intact on V2V communication to minimize the power loss issues and maximizes the energy efficiency. This paper is focused on recent advance technologies in the field of V2V communication that aims in uninterrupted connectivity in a very dynamic and congested environment. A comparative study of different architectures and algorithms are provided in this paper, to enable the readers to select appropriate techniques based on the real-time traffic conditions. In addition, energy efficient beamforming algorithms and related optimization techniques are briefly described that contributes to a faithful information exchange between the high-speed vehicles.

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.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.910
Threshold uncertainty score0.762

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 science0.0010.000
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 head0.018
GPT teacher head0.251
Teacher spread0.233 · 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