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Record W2141584030 · doi:10.1109/glocom.2008.ecp.979

Link Rate Allocation under Bandwidth and Energy Constraints in Sensor Networks

2008· article· en· W2141584030 on OpenAlexaff
Maggie X. Cheng, Xuan Gong, Lin Cai

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicEnergy Efficient Wireless Sensor Networks
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsHeuristicsBandwidth (computing)Computer scienceRouting (electronic design automation)Constraint (computer-aided design)ThroughputBandwidth allocationMathematical optimizationComputer networkDistributed computingMathematicsTelecommunicationsWireless

Abstract

fetched live from OpenAlex

In sensor networks, both energy and bandwidth are scarce resources. In the past, the energy efficient routing problem has been vastly studied in order to maximize network lifetime, but link bandwidth has been optimistically assumed to be abundant. As energy constraint affects how data should be routed, link bandwidth also affects not just the routing topology but also the allowed data rate on each link, which in turn affects lifetime. Previous works that focus on energy efficient operations in sensor networks with the sole objective of maximizing network lifetime only consider the energy constraint and ignore the bandwidth constraint. This article shows how infeasible these solutions could be if bandwidth does become a constraint, then provides a new mathematical model to tackle both energy and bandwidth constraints. Two efficient heuristics are proposed based on this model; Simulation results show these heuristics provide more feasible routing solutions than previous works, and provide significant improvement on 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 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 score0.926
Threshold uncertainty score0.620

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 head0.013
GPT teacher head0.205
Teacher spread0.191 · 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

Citations11
Published2008
Admission routes1
Has abstractyes

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