A dynamic context-aware access network selection for handover in heterogeneous network environments
Bibliographic record
Abstract
Context-awareness is a key ingredient in any ubiquitous and pervasive system and provides intelligence to the system, allowing computing devices to make appropriate and timely decisions on behalf of users. One of the important aspects of mobility management is the dynamic selection of the best access network for a multimodal device when there is a need to perform a handover. Multi Attribute Decision Making (MADM) is one of the successfully used methods in the literature to solve decision making problems. The problem of access network selection has been addressed by decision making methods based on available network information. However, the quality of information is not considered. Weighted Product Method (WPM) is an MADM method that penalizes the unreliable attributes in making a decision. It does not suffer from ranking abnormalities and its cheaper computational cost makes it a suitable candidate for decision making in a dynamic situation. In this paper, an algorithm for a context-aware network selection is proposed that is based on a modified WPM for access network selection. We use a weight distribution method based on sensitivity analysis of WPM for the most influential criteria based on the state of user at a given time. Our evaluation is based on comparing WPM with TOPSIS that is successfully used in many decision making problems.
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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) | 0.001 | 0.001 |
| 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 | 0.001 | 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".