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Record W2112044368 · doi:10.1142/s0219878904000276

A TWO-HOP ENERGY-EFFICIENT MESH PROTOCOL FOR WIRELESS SENSOR NETWORKS

2004· article· en· W2112044368 on OpenAlexafffund
Peter Liu, Yimin Liu

Bibliographic record

VenueInternational Journal of Information Acquisition · 2004
Typearticle
Languageen
FieldComputer Science
TopicEnergy Efficient Wireless Sensor Networks
Canadian institutionsCarleton University
FundersCanada Research Chairs
KeywordsComputer scienceOrder One Network ProtocolWireless mesh networkComputer networkMesh networkingWireless sensor networkHazy Sighted Link State Routing ProtocolSwitched meshRouting protocolEfficient energy useShared meshEnergy consumptionHop (telecommunications)Distributed computingWireless Routing ProtocolWireless networkNetwork packetWirelessTelecommunications

Abstract

fetched live from OpenAlex

We develop a novel energy-efficient routing protocol called the THEEM (Two-Hop Energy-Efficient Mesh) protocol for wireless sensor networks. In the THEEM protocol, a comprehensive and integrated treatment is employed to achieve energy efficiency. In specific, a two-hop in-mesh transmission scheme and a centralized mesh (cluster) formulation method are employed, along with other design innovations, such as the concepts of mesh layer/column, the power-aware assignment of mesh heads and a low-energy media access protocol. Simulation results show that the THEEM protocol is able to reduce energy consumption quite significantly compared to currently existing protocols. Equal energy dissipation among all sensor nodes in a network is also achieved. In addition, the protocol maximizes network data throughput.

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 score0.001
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.009
GPT teacher head0.271
Teacher spread0.263 · 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

Citations2
Published2004
Admission routes2
Has abstractyes

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