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Record W2136757130 · doi:10.1109/percom.2009.4912893

Key predistribution scheme using keyed-hash chain and multipath key reinforcement for wireless sensor networks

2009· article· en· W2136757130 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 institutionsSt. Francis Xavier UniversityAcadia University
Fundersnot available
KeywordsComputer scienceHash chainHash functionComputer networkWireless sensor networkKey (lock)Cryptographic hash functionKey generationMultipath propagationKey spaceNode (physics)Distributed computingComputer securityEncryptionChannel (broadcasting)Engineering

Abstract

fetched live from OpenAlex

In this paper, we present an efficient, effective, and secure mechanism for key-distribution in homogeneous wireless sensor network (WSNs). The scheme is based on multipath key reinforcement. We introduce the concept of a keyed-hash-chain to use a different key in each session between a pair of sensors, without allocating a large amount of memory space to store the keys. First, each sensor node is preloaded with a number of generation keys. Using these generation keys and a publicly known seed value in a pre-defined hash function, distinct key-chains are generated. Multipath Key reinforcement is used to update the number of iterations to achieve a higher level of security.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.683
Threshold uncertainty score1.000

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 communication0.0000.001
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 head0.019
GPT teacher head0.255
Teacher spread0.236 · 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

Citations14
Published2009
Admission routes1
Has abstractyes

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