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Record W2382504263

Low Power Consumption Design for Wireless Sensor Networks Nodes

2009· article· en· W2382504263 on OpenAlexvenueno aff
Liu Jijiang

Bibliographic record

VenueMicrocomputer applications · 2009
Typearticle
Languageen
FieldEngineering
TopicWireless Sensor Networks and IoT
Canadian institutionsnot available
Fundersnot available
KeywordsComputer scienceWireless sensor networkGeneral Packet Radio ServiceEmbedded systemKey distribution in wireless sensor networksNode (physics)TransceiverInterface (matter)Sensor nodeWirelessWireless networkComputer networkTelecommunicationsOperating system
DOInot available

Abstract

fetched live from OpenAlex

Wireless sensor network is the new generation sensor network,which has an extensive application and bright foreground.Design and realization scheme of network node in wireless sensor networks are proposed based on the introduction of its system structure,characteristics and function.The low consumption embedded CPU-MSP430F1611 is the core of network node,which has the TinyOS embedded operation system,the interface of GPRS model with MAC based IEEE 802.15.4 and wireless transceiver CC2420.It can collect and transmit data reliably at high speed.The system meets the needs of low-power and real-time.Test results show that the method has low power consumption,easy to use and other advantages.

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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.004
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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)0.0040.002

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 head0.011
GPT teacher head0.219
Teacher spread0.208 · 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 designNot applicable
Domainnot available
GenreMethods

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

Citations4
Published2009
Admission routes1
Has abstractyes

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