Energy-delay analysis in advection-diffusion-based wireless body area networks
Bibliographic record
Abstract
Molecular communication (MC) emerges as an encouraging concept in wireless body area nanonetworks ([Formula: see text]), which utilizes molecules as information carriers for communication between nanomachines. In this paper, we aim to define an electrical model of a molecular-based nano-transmitter to analyze the effect of the remained transmitted molecules in a fluidic medium. To this end, we will address an advection-diffusion equation with a non-zero initial condition to analyze the residual molecules' influence the medium. Moreover, considering the energy consumption limitations of nanomachines, we will employ the derived electrical model to further investigate how nanomachines consume the energy in presence of residual molecules. Following this, to enhance the energy consumption of the nano-transmitters, the settle-time method will be proposed to tackle the negative impact of the residual molecules on energy consumption. Nevertheless, since the proposed method increases the delay at nano-transmitters, the energy-delay trade-off relation at nano-transmitters will be investigated. Then, by introducing an interruption period and a control coefficient, we control the trade-off between the energy consumption and the created delay. Finally, by considering insulin molecules as messenger molecules in our simulations, we will demonstrate that implementing short interruption periods significantly enhances energy consumption, while introducing a small amount of delay to the system. Particularly, the energy consumption is reduced by 15% and the latency is increased by 2.2 ms when 1 ms interrupt period is used for 20 mol of insulin molecule.
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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 | 0.002 |
| 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".