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Record W2079890614 · doi:10.1145/1577222.1577277

Group key management in wireless mesh networks

2007· article· en· W2079890614 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

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicSecurity in Wireless Sensor Networks
Canadian institutionsYork University
Fundersnot available
KeywordsWireless mesh networkComputer networkComputer scienceKey managementKey (lock)Group keyMulticastScalabilityDistributed computingSecure multicastRekeyingProtocol (science)Wireless networkRouting protocolWirelessComputer securityRouting (electronic design automation)TelecommunicationsProtocol Independent MulticastReliable multicastEncryption

Abstract

fetched live from OpenAlex

Group key management (GKM) refers to the actions taken to up-date and distribute the group key upon members joining and leaving a multicast group. Although there exist several GKM schemes, they are not readily applicable to wireless mesh networks (WMNs) due to many differences between WMNs and wireline networks. We present a review of existing GKM protocols and identify their applicability to WMNs. Based on the review, we propose a framework for scalable and efficient GKM in WMNs, and a GKM protocol named CCoKA (Centralized COntributory Key Agreement) for use in a WMN. CCoKA is based on the scalable and efficient key tree approach and the well-known Diffie-Hellman cryptographic protocol. The proposed framework and CCoKA protocol take into account the characteristics of mesh network operations, wireless routers and mobile devices. We also suggest directions for future research on GKM in WMNs.

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.966
Threshold uncertainty score0.768

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.010
GPT teacher head0.235
Teacher spread0.225 · 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

Quick stats

Citations1
Published2007
Admission routes1
Has abstractyes

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