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Record W2527246464 · doi:10.1109/jsyst.2015.2475276

Releasing Network Isolation Problem in Group-Based Industrial Wireless Sensor Networks

2015· article· en· W2527246464 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

VenueIEEE Systems Journal · 2015
Typearticle
Languageen
FieldComputer Science
TopicEnergy Efficient Wireless Sensor Networks
Canadian institutionsUniversity of British Columbia
FundersNational Natural Science Foundation of ChinaDepartment of Education of Guangdong ProvinceGuangdong University of Petrochemical Technology
KeywordsWireless sensor networkComputer scienceScheduling (production processes)Isolation (microbiology)WirelessAccelerationMathematical optimizationComputer networkMathematicsTelecommunications

Abstract

fetched live from OpenAlex

In this paper, we propose a cross-layer optimization scheme named Adjusting the Transmission Radius (ATR), which is based on the Energy Consumed uniformly Connected K-Neighborhood (EC-CKN) sleep scheduling algorithm in wireless sensor networks (WSNs). In particular, we discovered two important problems, namely, the death acceleration problem and the network isolation problem, in EC-CKN-based WSNs. Furthermore, we solve these two problems in ATR, which creates sleeping opportunities for the nodes that cannot get a chance to sleep in the EC-CKN algorithm. Simulation and experimental results show that the network lifetime of ATR-Connected-K-Neighborhood-based WSNs increases by 19%, on average, and the maximum increment is 41%. In addition, four important insights were discovered through this research work and presented in this paper.

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)0.003
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 score0.894
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0000.001
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 head0.042
GPT teacher head0.237
Teacher spread0.195 · 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