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Record W2145593641 · doi:10.1109/icccn.2007.4318008

Reducing Communication Overhead of Key Distribution Schemes for Wireless Sensor Networks

2007· article· en· W2145593641 on OpenAlexaff
Eunchan Park, Ian F. Blake

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicSecurity in Wireless Sensor Networks
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsComputer scienceKey (lock)Computer networkOverhead (engineering)Key distribution in wireless sensor networksWireless sensor networkKey distributionWirelessNode (physics)Key managementTransmission (telecommunications)Broadcasting (networking)Distributed computingWireless networkIdentifierPublic-key cryptographyComputer securityTelecommunicationsEngineeringEncryption

Abstract

fetched live from OpenAlex

Establishing a secure communication link in a wireless sensor network is a challenging task due to resource limitation and wireless nature of transmission. A symmetric key distribution is a practical and efficient solution which has been studied extensively [4][5][6][7][9][11]. A secure communication link is established between sensors that hold common keys, which are discovered by each node broadcasting the key identifiers of its key ring. We make modifications to existing schemes to reduce communication overhead during the key discovery phase. This is achieved without weakening network connectivity or system resiliency against key compromise. We also introduce families of new deterministic key distribution schemes with low-communication overhead.

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)0.003
metaresearch head score (Gemma)0.015
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 score0.003
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.015
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.002
Science and technology studies0.0010.001
Scholarly communication0.0010.007
Open science0.0020.002
Research integrity0.0010.002
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 head0.016
GPT teacher head0.267
Teacher spread0.251 · 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

Citations8
Published2007
Admission routes1
Has abstractyes

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