Efficient Inter-Cloud Authentication and Micropayment Protocol for IoT Edge Computing
Bibliographic record
Abstract
In this paper, we present a$\textsf {S}$ymmetric$\textsf {K}$ey$\textsf {I}$nter-$\textsf {C}$loud$\textsf {A}$uthentication and redeemable micropayment$\textsf {P}$rotocol ($\textsf {SKICAP}$) for IoT-Edge computing applications. The proposed protocol takes into account the mobile and limited resource nature of IoT devices by enabling them to register with their home cloud admin for computation offloading service provided by foreign Edge-Cloud instances. More precisely,$\textsf {SKICAP}$ensures that mobile IoT devices can be authenticated and serviced by edge nodes outside of their home cloud coverage, while guaranteeing the associated charge redemption. Additionally, since our protocol considers the mobile and anywhere deployable requirements of IoT devices, we utilize physical unclonable function and cryptographic hash function to make it privacy-preserving and resilient to hardware compromise. Under the assumption of an indistinguishably secure physical unclonable function, we provide a formal security analysis of our protocol. Finally, we report$\textsf {SKICAP}$performance on an ARM Cortex-A72 processor, and compare it with other closely related protocols.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".