MétaCan
Menu
Back to cohort
Record W6976452253 · doi:10.60692/0f73v-11189

A Systematic Review on Clone Node Detection in Static Wireless Sensor Networks

2020· article· en· W6976452253 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

VenueGreater South Information System · 2020
Typearticle
Languageen
FieldComputer Science
TopicSecurity in Wireless Sensor Networks
Canadian institutionsUniversity of Northern British Columbia
Fundersnot available
Keywordsclone (Java method)Wireless sensor networkNode (physics)AdversaryReplication (statistics)Software deploymentSensor node

Abstract

fetched live from OpenAlex

The recent state of the art innovations in technology enables the development of low-cost sensor nodes with processing and communication capabilities. The unique characteristics of these low-cost sensor nodes such as limited resources in terms of processing, memory, battery, and lack of tamper resistance hardware make them susceptible to clone node or node replication attack. The deployment of WSNs in the remote and harsh environment helps the adversary to capture the legitimate node and extract the stored credential information such as ID which can be easily re-programmed and replicated. Thus, the adversary would be able to control the whole network internally and carry out the same functions as that of the legitimate nodes. This is the main motivation of researchers to design enhanced detection protocols for clone attacks. Hence, in this paper, we have presented a systematic literature review of existing clone node detection schemes. We have also provided the theoretical and analytical survey of the existing centralized and distributed schemes for the detection of clone nodes in static WSNs with their drawbacks and challenges.

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), Insufficient payload (model declined to judge)
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 score0.946
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
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

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 head0.024
GPT teacher head0.209
Teacher spread0.185 · 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