A secure multiparty micropayment protocol for internet access over WLAN mesh networks
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".