Optimization of IoT slices in wifi enterprise networks
Bibliographic record
Abstract
The increasing number of differentiated IoT services introduced to wireless networks are coming with diverse and sometimes conflicting requirements. Network slicing in 5G is the key feature to address these requirements. However, slicing is mainly intended for cellular networks, and adopting it for WiFi networks is challenging due to the lack of wireless virtualization supports in hardware. This paper proposes a new slicing solution for WiFi enterprise networks that require no virtualization support. Our solution relies on a dynamic user association mechanism that takes into account different IoT requirements. We formulate an optimization problem that maximizes the total throughput of the network with respect to different IoT requirements. To solve this high-complexity problem, a stable matching mechanism algorithm has been proposed to obtain the optimized solution in near real-time. We also advocate a Reinforcement Learning algorithm that enables practical implementations and employs a learning framework to learn different network dynamics. Simulation results show that the proposed solutions approximate the optimal results and outperform the traditional RSSI approaches while guaranteeing the requirements of different IoT slices.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".