IoT mobile device Data Offloading by Small-Base Station Using Intelligent Software Defined Network
Bibliographic record
Abstract
The growing number of IoT devices and the different computing and communication capabilities of IoT devices require an efficient offloading scheme. This offloading scheme need to consider the mobility of IoT devices and helps to intelligently select the optimal server for offloading. An efficient offloading scheme need to take in consideration important factors such as mobility of the IoT user device, speed and direction of the IoT user device as well as the computational capabilities of the user mobile device and the load of nearby servers. Unbalanced load of data or task offloading lead to high latency and poor services. An optimal selection of offloading server will clearly improve latency and QoS. Some new architecture of cellular network suggest the deployment of small-cell base stations (SBS) [1], [2] with a certain computing capabilities which can help offloading task of IoT mobile device or of their nearby SBS. In smart city environment, the mobile IoT device user needs to choose an SBS from several available SBSs within the its communication proximity. In this paper, we propose a Smart Ranking based Task Offloading approach for selecting an SBS and to improve the Quality of Service. This approach uses Q-Learning for SBS selection which will be modelled in Software Defined Network controller to deal with the problem of choosing the SBS in an intelligent way for Task offloading.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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".