Analysis of the Interdelivery Time in IoT Energy Harvesting Wireless Sensor Networks
Bibliographic record
Abstract
In this article, we investigate an energy harvesting (EH) wireless sensor network for the Internet of Things (IoT) where monitoring applications require a continuous update of sensing information. The considered system consists of independent EH sensor nodes equipped with capacitors and providing, through unreliable channels, status updates to a non EH sink. The distribution of the interdelivery time, i.e., the time elapsed between two successive and successful status update deliveries, is derived in the closed-form expression considering a random EH arrival process. Moreover, the interdelivery violation probability metric, defined as the probability to exceed a predetermined interdelivery threshold, is analyzed. Our analysis reveals that the violation probability is highly dependent on the size of the capacitor. Both analytical and simulation results demonstrate the existence of an optimal capacitor size that achieves the minimum violation probability. Moreover, our findings reveal an interesting tradeoff in the system design. On one hand, a small capacitor charges quickly and thus status updates are sent more frequently but with lower transmit power and thus a high error rate. On the other hand, a large capacitor increases the transmit power and boosts the successful data transmission probability, at the expense of a higher waiting time before filling the capacitor and transmitting sensed data.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".