Cellular IP address provisioning in a heterogeneous wireless network
Bibliographic record
Abstract
SUMMARY In this article, we propose a dynamic Internet Protocol (IP) address assignment architecture for heterogeneous wireless IP devices network. The IP device could be a sensor device, a laptop, a cell phone, or any wireless device using IP communications. The proposed architecture introduces security and service reliability to the consumer while reducing the operational expenditure for the service providers. According to the proposed scheme, each node maintains an IP address pool storing the current occupancy of each IP address. Each node advertises its database whenever the ratio of negative acknowledgments from the domain name server to the total number of requests at a given node exceeds a certain threshold. We evaluate our IP assignment scheme under two traffic intensity scenarios, namely the uniform traffic intensity and the heterogeneous traffic intensity. Performance evaluation is carried out with respect to blocking probability and average IP list utilization. We define three types of blocking probability for the user requests as follows: The real blocking, the unjustified acceptance, and the unjustified rejection. We observe that the proposed scheme outperforms the uniform assignment as long as the threshold is below 1.5% for the uniform intensity scenario and 1% for the heterogeneous scenario. Furthermore, this architecture considers the security aspect of the wireless network by allowing only registered devices to communicate with other registered devices. Copyright © 2012 John Wiley & Sons, Ltd.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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".