MobiRate: Mobility-Aware Rate Adaptation Using PHY Information for Backscatter Networks
Bibliographic record
Abstract
In the past few years, various backscatter nodes have been invented for many emerging mobile applications, such as sports analytics, interactive gaming, and mobile healthcare. Backscatter networks are expected to provide a high-throughput and stable communication platform for those interconnected mobile nodes. Yet, through experiments, we find state-of-the-art rate adaptation methods for backscatter networks share a fundamental limitation of accommodating the hardware diversity of nodes because the common mapping paradigm that chooses the optimal rate based on the radio signal strength indicator (RSSI) or the like is hardly adaptable to hardware-dependent RSSIs. To address this issue, we propose MobiRate (Mobility-aware Rate adaptation) that fully exploits the mobility hints from PHY information and the characteristics of backscatter systems. The key insight is that mobility-hints, like velocity and position, can greatly benefit rate selection and channel probing. Specifically, we introduce a novel velocity-based loss rate estimation method that dynamically re-weighs packets based on time and mobility. In addition, we design a mobility-assisted probing trigger and a new selective-probing mechanism, significantly saving probing time. As MobiRate is fully compatible with the current standard, it is prototyped using a COTS RFID reader and a variety of commercial tags. Our extensive experiments demonstrate that MobiRate achieves up to 3.8x throughput gain over the state-of-the-art methods across a wide range of mobility, channel conditions, and tag types.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".