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Record W2535641182 · doi:10.1109/icima.2004.1384260

Power-efficient routing in sensor information systems

2005· article· en· W2535641182 on OpenAlexaff
Yimin Liu, Peter Liu

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicEnergy Efficient Wireless Sensor Networks
Canadian institutionsCarleton University
Fundersnot available
KeywordsWireless sensor networkComputer networkRouting protocolComputer scienceWireless Routing ProtocolKey distribution in wireless sensor networksEnergy consumptionHazy Sighted Link State Routing ProtocolMobile wireless sensor networkZone Routing ProtocolDynamic Source RoutingEfficient energy useDistributed computingRouting (electronic design automation)Wireless networkWirelessEngineeringTelecommunicationsElectrical engineering

Abstract

fetched live from OpenAlex

Wireless sensor networks are finding applications in a variety of areas such as environmental monitoring, military target detection and border surveillance. One of the most significant outstanding challenges is the stringent constraint on energy consumption. In this paper, we present a novel energy-efficient routing protocol called THEEM (Two- Hop Energy-Eficient Mesh) for wireless sensor networks. In the THEEM protocol, :a two-hop scheme and a centralized mesh (cluster) formation method are employed along with other design innovations, such as the concepts of mesh layer/column, power-aware meshhead assignment and a low-energy media access protocol, to achieve energy efficiency. This protocol reduces energy consumption quite significantly and thus prolongs the longevity of a sensor network. In the meantime, it increases network data throughput. Moreover, equal energy dissipation among all sensor nodes in a network is achieved. Simulation results show that the THEEM protocol performs much better in terms of all the above criteria when compared with other wireless sensor network routing protocols.

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 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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
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 score0.800
Threshold uncertainty score0.463

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
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 head0.006
GPT teacher head0.204
Teacher spread0.198 · 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 teacher head, 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
Published2005
Admission routes1
Has abstractyes

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