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Record W884408679 · doi:10.1016/j.procs.2015.05.021

Ad-ATMA: An Efficient MAC protocol for Wireless Sensor and Ad Hoc Networks

2015· article· en· W884408679 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueProcedia Computer Science · 2015
Typearticle
Languageen
FieldComputer Science
TopicEnergy Efficient Wireless Sensor Networks
Canadian institutionsYork University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsComputer scienceComputer networkWireless ad hoc networkMultiple Access with Collision Avoidance for WirelessNetwork packetWireless sensor networkThroughputAd hoc wireless distribution serviceLatency (audio)WirelessWireless networkVehicular ad hoc networkOptimized Link State Routing ProtocolRouting protocolTelecommunications

Abstract

fetched live from OpenAlex

Efficient medium access control (MAC) algorithms are needed for nodes to share a transmission medium and achieve a high throughput. A MAC algorithm schedules packet transmissions so as to that minimize the time taken to send the packets without collisions. In wireless ad hoc and sensor networks, a MAC algorithm must conserve energy as well as provide good throughput. Most existing MAC algorithms for wireless networks are designed to work well under low traffic rates. In this paper we propose a new distributed algorithm Ad-ATMA for wireless ad hoc and sensor networks under relatively high traffic rates. We demonstrate using simulations that Ad-ATMA outperforms the best existing algorithms designed for higher traffic rates in terms of packet delivery ratio and latency while consuming almost identical energy as them.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Scholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.283
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0030.001
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 head0.030
GPT teacher head0.287
Teacher spread0.256 · 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