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Record W4250241856 · doi:10.32920/ryerson.14647176.v1

A secure multiparty micropayment protocol for internet access over WLAN mesh networks

2021· preprint· en· W4250241856 on OpenAlexaff
Nitish Biswas

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEngineering
TopicIPv6, Mobility, Handover, Networks, Security
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsRoamingComputer networkComputer scienceBloom filterThe InternetComputer securityHash functionPaymentScheme (mathematics)

Abstract

fetched live from OpenAlex

<p>Presently, multi-hop WLAN mesh networks have become an alternative to wired networks for last-mile user access enabling numerous internet-based services. Thus, we have proposed MMPay, a secure multiparty micropayment protocol for internet access over WLAN mesh networks, enabling: a secure network access anywhere and anytime according to user desire; seamless user roaming across the independent operator’s networks; and lightweight real-time payments to all involved parties that eliminate huge user trust relationships, online remote user authentications and mutual roaming agreements among the participating parties. The incontestable MMPay scheme has been devised from existing micropayment schemes emulating their good attributes and eliminating security vulnerabilities and difficulties, which includes: hash-chain based variable length payment instrument, user’s payment certificate at smartcard, shared and mixed signature scheme, and an efficient redemption approach. The OPNET-simulation results show that the pricing contract response-times are little lengthy but have no effect on data communication; payments and hand-offs are efficient; and the scheme has no effect on data communication ETE-delay and throughput. Thus, the scheme is secure, efficient and lightweight, and will be a practical solution for future small to large-scale WLAN mesh networks enabling faster hand-off. </p>

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Protocol · Consensus signal: none
Teacher disagreement score0.538
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0020.000

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 head0.029
GPT teacher head0.315
Teacher spread0.285 · 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
GenreProtocol

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

Citations0
Published2021
Admission routes1
Has abstractyes

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