Estimating the energy cost of communication on portable wireless devices
Bibliographic record
Abstract
Software applications running on portable wireless devices communicate with the rest of the network over a wireless link. In these portable devices, the communication cost is a large fraction of the total energy consumption. The amount of energy consumed by the communication component of a portable device mostly depends on different parameters such as packet size and packet rate (or, bit rate). In this paper, we present the results of our investigation of the impacts of these communication parameters on energy consumption. First we build a simple analytic model to estimate the energy consumption due to receiving and transmitting data packets, and then we validate our model by conducting experiments. Results show that the analytical model is effective and gives accurate results. By varying data packet lengths, a communication device consumes different levels of energy to achieve the same data rate. When the packet size is very small compared to the maximum transmission unit (MTU), the device consumes more energy. However, large packets do not necessarily save energy. They rather add some other types of overheads, such as segmentation, recombination, and packet drop. Thus, for a given set of network parameters, an application can choose a suitable data packet length to minimize energy consumption. We also present the impact of data rate and packet delays on energy consumption. These results help us in understanding the energy consumption behavior of a communication device. They also facilitate us in optimizing the energy cost while designing a wireless application.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".