Optimized Flow Assignment in a Multi-Interface IoT Gateway
Bibliographic record
Abstract
The last few years have witnessed a significant increase in the deployment of heterogeneous Internet of Things (IoT) networks. IoT devices send data with different requirements such as tolerated delay and data rates. Emerging multi-interface IoT devices bring the flexibility of connecting to multiple heterogeneous access networks, which thus improves the network capacity. However, each network interface has its own constraints in terms of network coverage, capacity, packet loss rates, etc. An efficient utilization of the available multiple interfaces in IoT gateways would improve the network performance. Therefore, it is crucial to design a flow assignment mechanism to select the appropriate interface that best satisfies the flow's requirements and maximizes the amount of data accepted by an IoT gateway. In this work, we model and formulate the optimized flow assignment problem (OFAP) in a multi-interface IoT gateway. Then, we develop two heuristic algorithms to find a feasible solution for OFAP. The first algorithm is based on the greedy approach and the second uses dynamic programming to assign flows to interfaces. We provide simulation results that show the effectiveness of our algorithms.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".