Named Data Networking Enabled Power Saving Mode Design for WLAN
Bibliographic record
Abstract
The energy consumption of wireless interface is critical to power-constrained mobile devices. To improve energy efficiency for WLAN stations, power saving mode (PSM) is proposed, to manage the time spent in idle listening (IL) state. The hurdle is that the receiver has no knowledge about when the pending data will arrive under end-to-end communication protocols (TCP/IP), making each station spend enormous time in IL to wait for the pending data. To overcome this limitation, in this paper, we propose a named data networking (NDN) enabled PSM, namely, NDN-PSM, which leverages NDN communication architecture to cut down unnecessary IL time. In particular, we devise two new power states in NDN-PSM, i.e., light doze and deep doze, to precisely map to the underlying data arriving states. The inherent receiver-driven pattern of NDN can effectively drive the stations to the deep doze state for power saving, and to light doze state for timely data reception. Considering the IL time waste during channel contention, we further design a channel contention control mechanism in NDN-PSM, in which stations will switch to the light doze state if the channel is perceived to be busy. The power consumption model of the proposed NDN-PSM is theoretically analyzed and verified via numerical results. At last, we implement NDN-PSM in NS-3 by adopting the ndnSIM module and conduct extensive simulations to demonstrate the efficacy of NDN-PSM. Specifically, compared to the existing PSM mechanism, NDN-PSM reduces 56% power consumption by cutting down unnecessary IL time, and meanwhile enables low-delay transmission.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".