MétaCan
Menu
Back to cohort
Record W4306763705 · doi:10.1145/3551659.3559065

A non-Hidden Markovian Modeling of the Reliability Scheme of the Constrained Application Protocol in Lossy Wireless Networks

2022· preprint· en· W4306763705 on OpenAlexaff
Nabil Makarem, Wafaa Bou Diab, Imad Mougharbel, Naceur Malouch

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEngineering
TopicGreen IT and Sustainability
Canadian institutionsÉcole de Technologie Supérieure
Fundersnot available
KeywordsComputer scienceComputer networkMarkov processGoodputReliability (semiconductor)Wireless sensor networkLossy compressionWirelessWireless networkDistributed computingThroughput

Abstract

fetched live from OpenAlex

The Constrained Application Protocol (CoAP) is a lightweight communication protocol designed by the Internet Engineering Task Force (IETF) for wireless sensor networks and Internet-of-Things (IoT) devices. The reliability mechanism in CoAP is based on retransmissions after timeout expiration and on an exponential backoff procedure which is designed to be simple and adapted to constrained devices. In this research work, we propose a new exact analytical model to analyze the performance of CoAP in lossy wireless networks modeled by the well-known Gilbert-Elliott two-state Markov process. We also show how to compute several performance metrics using closed form expressions such as the observed loss ratio, goodput, and the delay before success with a time complexity no more than O(r) with r is the maximum re-transmission limit. This study provides insights about improving CoAP recovery mechanism and highlights the properties -- including the limitations -- of CoAP. Also, it presents guidelines to tune CoAP parameters dynamically in order to adapt to network losses caused by interference and mobility. The model is validated using the realistic environment Cooja/Contiki OS where theoretical and experimental results match very well.

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)0.001
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: Empirical
Teacher disagreement score0.089
Threshold uncertainty score0.678

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
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 head0.007
GPT teacher head0.235
Teacher spread0.228 · 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

Citations1
Published2022
Admission routes1
Has abstractyes

Explore more

Same topicGreen IT and SustainabilityFrench-language works237,207