Energy Harvesting Wireless Sensor Networks With Channel Estimation: Delay and Packet Loss Performance Analysis
Bibliographic record
Abstract
This paper considers a single-source status monitoring system with energy harvesting in the context of applications where keeping information up-to-date is of primary interest. Specifically, a sensor node, relying on harvested energy and operating according to a harvest-then-use protocol, estimates the channel state to decide whether to perform or defer data delivery to a sink. The sink keeps track of the system status through the successfully delivered updates. To comprehensively evaluate the performance of the proposed scheme, we analytically derive the exact-closed form expressions of the packet loss probability, the age of information and the update interval metric statistics considering both constant-rate and random energy arrival processes and taking into account both the time and energy costs of sensing, transmitting and estimating the channel state. We asymptotically obtain the necessary conditions under which estimating the channel state before transmitting, despite the associated time and energy costs, allow to efficiently manage the harvested energy by avoiding erroneous transmissions and performs strictly better than transmitting without estimating the channel state. Numerical results demonstrate that in most cases, estimating the channel state before transmitting significantly reduces the packet loss probability, the age of information and the update interval of node-sink transmission.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 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".