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Record W2058194282 · doi:10.1109/eit.2006.252218

Security Routing in MANETs - A Comparative Study

2006· article· en· W2058194282 on OpenAlexaff
Sasan Adibi, S. Erfani, Hym Al Harbi

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicMobile Ad Hoc Networks
Canadian institutionsUniversity of WindsorUniversity of Waterloo
Fundersnot available
KeywordsComputer scienceComputer networkMobile ad hoc networkWireless ad hoc networkQuality of serviceAdaptive quality of service multi-hop routingPopularityVariety (cybernetics)Vehicular ad hoc networkKey (lock)Routing protocolComputer securityOptimized Link State Routing ProtocolRouting (electronic design automation)TelecommunicationsWireless

Abstract

fetched live from OpenAlex

Mobile-IP ad-hoc networks (MANETs) have gained popularity in the past few years with the creation of variety of ad hoc protocols that specifically offer quality of service (QoS) for various multimedia traffic between mobile nodes (MNs) and base stations (BSs). The lack of proper end-to-end coverage, on the other hand, is a challenging issue as the nature of such networks with no specific infrastructure is prone to relatively more attacks, in variety of forms. The attention of this paper is to overview types of attacks in MANETs and the introduction of two entities; ad-hoc key distribution center (AKDC) and decentralize key generation and distribution (DKGD). Through simulations, the performance of these functional entities is compared to current schemes

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)0.003
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: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.003
Science and technology studies0.0000.000
Scholarly communication0.0020.002
Open science0.0000.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.270
Teacher spread0.252 · 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

Citations1
Published2006
Admission routes1
Has abstractyes

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