Impact of packet aggregation on energy consumption in smartphones
Bibliographic record
Abstract
With the tremendous growth in mobile applications, communication accounts for a significant portion of a smartphone's total energy consumption. We studied the traffic pattern of smartphones and observed that a good portion of the packets are of small size and the generated traffic is bursty in nature. Motivated by these observations, we propose a Low Energy Data-packet Aggregation Scheme (LEDAS) in this paper. It accumulates a number of upper layer packets into a burst at medium access control (MAC) level, based on accumulation time, size, and number of packets. With this scheme, larger bursts lead to longer inactivity periods during which the communication module can be kept in doze mode. In addition, fewer MAC frames lead to less overheads and contentions in the wireless medium. However, the data packets incur delays due to the accumulation process. We have given a detail flowchart description of the technique. By means of analysis, we have derived expressions for the average values of burst size, burst inter-arrival times, and number of packets in a burst. We also evaluated the efficacy of the technique by simulations and showed the energy-delay trade-offs. Finally, we explained a test-bench to evaluate the energy saving potential of LEDAS on a smartphone.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".