Enabling Sensor Data Exchanges in Unstable Mobile Architectures
Bibliographic record
Abstract
There is increasing interest from companies on the use of emerging mobile and cloud computing technologies to achieve enterprise specific tasks. Mobile devices such as smartphones, tablets, and notebooks can be used to access personalized and enterprise data. Moreover, recent launch of consumer devices such as wearable smart watches (e.g., Apple Watch) requires services and products re-alignment. More importantly, sensor capabilities of these mobile devices can be leveraged with desktops and cloud computing facilities to create next generation mobile applications that can be beneficial to enterprises such as health, agriculture, mining and so on. The challenge however is the limited and unstable wireless mediums through which these smart devices communicate. Due to this factor, data transfer can experience challenges such as high latency, data corruption, communication loss, and performance cost in terms of poor throughput. Hence, this work designed a prototypic system using the Constrained Application Protocol (CoAP) that seeks to provide speed-efficient data exchanges in a mobile-sensor-cloud ecosystem. Furthermore, a group of performance measurement experiments and conducted to understand the proposed system's performance for wireless networks. Preliminary results suggest CoAP can achieve quick I/O data response time.
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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) | 0.001 | 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".