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Record W2152318830 · doi:10.1109/ccece.2005.1557395

On the connectivity of nodes in wireless ad hoc and sensor net

2006· article· en· W2152318830 on OpenAlexaff
Mohammad Naserian, Kemal Tepe

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicEnergy Efficient Wireless Sensor Networks
Canadian institutionsUniversity of Windsor
Fundersnot available
KeywordsWireless ad hoc networkComputer networkComputer scienceNode (physics)Mobile ad hoc networkTransmission (telecommunications)Overhead (engineering)Wireless sensor networkRouting (electronic design automation)Network topologyTopology (electrical circuits)Routing protocolRange (aeronautics)Optimized Link State Routing ProtocolDistributed computingKey distribution in wireless sensor networksWireless networkWirelessNetwork packetTelecommunicationsMathematicsEngineering

Abstract

fetched live from OpenAlex

The transmission range of the nodes in ad hoc networks affects the connectivity and routing overhead. Therefore in the process of selecting the transmission range, knowledge about the connectivity of the network is crucial. Sensor network is considered as n node that are randomly located in some region with known dimensions. We create a model for the relationship between the number of nodes (n) and the transmission range so that the resulting network topology is connected with high probability. We utilize NS-2 to investigate the effect of transmission range, number of nodes and dimensions of the network on connectivity

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.903
Threshold uncertainty score0.278

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.008
GPT teacher head0.198
Teacher spread0.190 · 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
Published2006
Admission routes1
Has abstractyes

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