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Record W2292416797 · doi:10.1109/glocom.2015.7417595

Zoning Based MAC with Support for Recharging Process in WSN

2015· article· en· W2292416797 on OpenAlexaff
Mohammad Shahnoor Islam Khan, Jelena Mišić, Vojislav B. Mišić

Bibliographic record

Venue2015 IEEE Global Communications Conference (GLOBECOM) · 2015
Typearticle
Languageen
FieldEngineering
TopicEnergy Harvesting in Wireless Networks
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsComputer scienceZoningProcess (computing)Computer networkComputer securityOperating systemEngineering

Abstract

fetched live from OpenAlex

Radio-frequency (RF) based recharging of sensor nodes is a promising way to reduce maintenance and extend the operational life of wireless sensor networks. However RF attenuation causes the network nodes with largest distance from the access point (master node) to dictate the rate of recharging which imposes unnecessary breaks in the operation of nodes closer to the master. This deteriorates the throughput of the nodes close to the master. To solve this problem we have designed location aided MAC protocol which supports recharging such that all nodes deplete their batteries at approximately the same time so that recharging pulse comes on time for all the nodes. To achieve that we have partitioned network nodes into circular zones around the master and assigned implicit priorities among the zones. Priorities decrease towards the edge of the network and regulate relative throughput among the zones. We have built probabilistic performance model to evaluate the impact of the recharging process on data communication of different zones by varying traffic load and network size.

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)0.003
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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.004
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.079
GPT teacher head0.324
Teacher spread0.245 · 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

Citations4
Published2015
Admission routes1
Has abstractyes

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Same venue2015 IEEE Global Communications Conference (GLOBECOM)Same topicEnergy Harvesting in Wireless NetworksFrench-language works237,207