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Record W2109725703 · doi:10.1109/glocom.2010.5684052

ESPR: Efficient Security Scheme for Position-Based Routing in Vehicular Ad Hoc Networks

2010· article· en· W2109725703 on OpenAlexaff
Nizar Alsharif, Albert Wasef, Xuemin Shen

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicVehicular Ad Hoc Networks (VANETs)
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsHash-based message authentication codeComputer scienceMessage authentication codeComputer networkWireless ad hoc networkKey (lock)Scheme (mathematics)Probabilistic logicAuthentication (law)Vehicular ad hoc networkOverhead (engineering)Hash functionRouting (electronic design automation)Distributed computingCryptographyAlgorithmComputer securityMathematicsWirelessTelecommunications

Abstract

fetched live from OpenAlex

In this paper, we propose an efficient security scheme for position-based routing (ESPR) in vehicular ad hoc networks (VANETs). The ESPR scheme considers both digital signature and keyed Hash Message Authentication Code (HMAC), where the key used in calculating the HMAC is shared only between unrevoked On-Board Units (OBUs). In addition, the ESPR scheme uses a novel probabilistic key distribution, which enables unrevoked OBUs to securely share and update a secret key. By conducting security analysis and performance evaluation, the ESPR scheme is demonstrated to outperform its counterparts in terms of communication overhead and delay while achieving robust and secure operation.

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)0.002
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.002
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.004
Open science0.0020.004
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.004
GPT teacher head0.202
Teacher spread0.198 · 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 designSimulation or modeling
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
Published2010
Admission routes1
Has abstractyes

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