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Record W2011835144 · doi:10.1109/vetecs.2012.6240045

Effects of Relaying on Network Lifetime in 2.4GHz IEEE802.15.4 Based Body Area Networks

2012· article· en· W2011835144 on OpenAlexaff
Pooyan Abouzar, Kaveh Shafiee, David G. Michelson, Victor C. M. Leung

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicWireless Body Area Networks
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsPath lossTransmitterRelayComputer scienceWireless sensor networkBody area networkAmplifierTransmission (telecommunications)Computer networkWirelessTransmitter power outputEnergy consumptionPower (physics)Electronic engineeringTelecommunicationsChannel (broadcasting)Electrical engineeringEngineeringBandwidth (computing)

Abstract

fetched live from OpenAlex

Relaying is a major technique to increase network lifetime in wireless sensor networks (WSNs) and body area networks (BANs). The results of relaying studies highly depend on power consumption model of relay's transmitter and receiver radio, channel and mobility models. In this work, we study the effect of relaying with 2.4GHz IEEE802.15.4 modules on network lifetime. We analytically derive the energy expenditure of relays in which we use the path loss probability distribution function (PDF) over links, which is derived from our link characterization using micaZ motes, and the existing power consumption models. It turns out that in order for relaying to be beneficial to network lifetime and for the same transmitter power amplifier efficiency, receive power consumption of IEEE802.15.4 widely used RF modules, e.g., micaZ motes are required to be decreased. For instance, receive power of 17mW helps us achieve 25% more lifetime with 3-relay scheme than single-hop transmission over ankle-waist link during walking, whereas with the current receiver, we achieve 12% less lifetime compared to single-hop scenario.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.174
Threshold uncertainty score1.000

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 communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.005
GPT teacher head0.188
Teacher spread0.183 · 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.

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
Published2012
Admission routes1
Has abstractyes

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