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Record W3151955359 · doi:10.1109/date.2007.364523

A Tiny and Efficient Wireless Ad-hoc Protocol for Low-cost Sensor Networks

2007· article· en· W3151955359 on OpenAlexaff
Paweł Gburzyński, Bożena Kamińska, W. Olesinski

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicMobile Ad Hoc Networks
Canadian institutionsCommunications Research Centre CanadaSimon Fraser UniversityUniversity of Alberta
Fundersnot available
KeywordsComputer scienceWireless ad hoc networkComputer networkRobustness (evolution)Fault toleranceSoftware deploymentQuality of serviceDistributed computingOptimized Link State Routing ProtocolWireless sensor networkAd hoc wireless distribution serviceReliability (semiconductor)Routing protocolMobile ad hoc networkWirelessRouting (electronic design automation)Network packetTelecommunications

Abstract

fetched live from OpenAlex

The authors introduce a simple ad-hoc routing scheme that operates in the true spirit of ad-hoc networking, i.e., in a modeless fashion, without neighborhood discovery or explicit point-to-point forwarding, while offering a high (and tunable) degree of reliability, fault-tolerance and robustness. Being aimed at truly tiny devices (e.g., with 1KB of RAM), the scheme can automatically take advantage of extra memory resources to improve the quality of routes for critical nodes. In contrast to some popular low-cost solutions, like ZigBeetrade the approach involves a single node type and exhibits lower resource requirements. The presented scheme has been verified in an industrial deployment with stringent quality of service requirements

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)0.001
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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.015
GPT teacher head0.281
Teacher spread0.267 · 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 designSimulation or modeling
Domainnot available
GenreEmpirical

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

Citations13
Published2007
Admission routes1
Has abstractyes

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