Towards Mobile Collaborative Autonomous Networks Using Peer-to-Peer Communication
Bibliographic record
Abstract
Given the emerging technological development in the fields of telecommunication and smart mobile devices, the number of connected devices around the world is increasing rapidly. Moreover, the tendency to use these small devices is increasing steadily as their capabilities and efficiency increase. Progress in these areas has been an incentive and a reason for enhancing distributed systems, Internet of things and mobile collaborative computing. The advances in hardware and software technologies have necessitated the development of new communication standards adapted to devices constrained in resources in all areas, energy, computing power, memory and bandwidth. These communication standards have a crucial role in enhancing the IOT and other architectures of mobile collaborative computing such as Cloud, Fog, Edge, and Mobile Edge Computing. In this paper, we will review the current communication standards and protocols by showing its role in strengthening mobile communication networks in the different architectures of mobile collaborative computing and under the different circumstances. More specifically, we will illustrate, within a new proposed approach, how they can maintain an efficient connection even between mobile devices on the periphery and how they can establish autonomous mobile networks by using peer-to-peer communication via Wi-Fi Direct and other technologies.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".