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Record W2031868952 · doi:10.5383/juspn.02.01.005

Energy-Efficient and Scalable Group Key Management for Hierarchical Sensor Network

2011· article· en· W2031868952 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.

venuePublished in a venue whose home country is Canada.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueJournal of Ubiquitous Systems and Pervasive Networks · 2011
Typearticle
Languageen
FieldComputer Science
TopicSecurity in Wireless Sensor Networks
Canadian institutionsnot available
Fundersnot available
KeywordsScalabilityKey (lock)Computer scienceWireless sensor networkGroup (periodic table)Distributed computingComputer networkComputer securityDatabaseChemistry

Abstract

fetched live from OpenAlex

Key management is deemed as the fundamental essential part of any secure communication. A secure sensor network communication protocol relies on the substantial secure, robust and efficient key management system. Implementing a private and public key pair for sensor network is impractical due to high computation and energy consumption. In this research, we put forward two group key management schemes for hierarchical self-organizing wireless sensor network architecture. Both of the schemes are designed in such a way that more computational and communication burden is placed on the forwarding node and the similar workload is kept as low as possible at the sensor nodes. Besides, multilevel security can be achieved to secure groups of sensors at different levels.

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 categoriesMeta-epidemiology (narrow)
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 score0.929
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
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 head0.017
GPT teacher head0.213
Teacher spread0.196 · 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