WiFi Overcast: Enabling True Mobility for Realtime Applications in the Enterprise
Bibliographic record
Abstract
Enterprises are increasingly deploying Wireless LANs to provide mobile access to users in corporate offices. However, existing enterprise WLANs are far from being truly mobile. In particular, they do not adequately support continuous mobility, where users access the network on-the-go. Furthermore, WLANs that do provide continuous mobility support require client modifications, making them hard to deploy in practice [20]. In addition, with the growing interest in realtime applications such as voice and video, users are increasingly placing additional (QoS) demands on the network, which for inadequately designed WLANs, does not scale to large numbers of users [10]. In this paper, we propose Overcast, a novel WLAN architecture that targets scenarios demanding continuous mobility and real-time support for 802.11 clients. Overcast does not require client modifications and supports all 802.11 standards. Though Overcast borrows some features from prior WLAN designs, it improves on them by incorporating a novel RF mapping framework (proposed in [3]) for accurate online detection of RF interference. We describe the architecture of Overcast in detail and discuss our current efforts in realizing such a system on off-the-shelf commodity hardware. We also describe an example application of Overcast to highlight it’s usefulness in supporting realtime applications in continuously mobile user environments.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| 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".