Bibliographic record
Abstract
For applications requiring frequent data collections from a remote wireless sensor network, it is a challenging problem to design an efficient routing scheme for comprehensively, accurately and timely delivering data packets in each round of data collection over a long period of time. Unlike previous work targeting at maximizing energy efficiency and network lifetime, we propose and analyze in this paper a new routing scheme, called Minimum Energy Spanning Tree for Efficient Routing (MESTER), which is developed under the design objective of maintaining a high quality in data collection for as long as possible. Compared with the existing Minimum Spanning Tree (MST) based schemes like PEDAP and PEDAP-PA, MESTER can achieve comparable but more balanced performance at a much lower complexity. In addition, we define "throughput efficiency" to characterize our quality-oriented design objective. As a new concept with low granularity (packet level), throughput efficiency is found a fair and stable performance metric to different network sizes, node densities and routing schemes. It provides us an additional insight into the network behavior under different resource and capability constraints.
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 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".