MétaCan
Menu
Back to cohort
Record W2292416797 · doi:10.1109/glocom.2015.7417595

Zoning Based MAC with Support for Recharging Process in WSN

2015· article· en· W2292416797 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

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.

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)0.001
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: none
Teacher disagreement score0.747
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 science0.0010.000
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 head0.079
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