Bibliographic record
Abstract
Sensor nodes are tiny, low-powered and multi-functional devices operated by lightweight batteries. Replacing or recharging batteries of sensor nodes in a network is usually not feasible so that a sensor network fails when the battery power in critical node(s) is depleted. The limited transmission range and the battery power of sensor nodes affect the scalability and the lifetime of sensor networks. Recently, relay nodes, acting as cluster heads, have been proposed in hierarchical sensor networks. The placement of relay nodes in a sensor network, such that all the sensor nodes are covered using a minimum number of relay nodes is a NP-hard problem. We propose a simple strategy for the placement of relay nodes in a two-tiered network that ensures connectivity and fault tolerance. We also propose two ILP formulations for finding the routing strategy so that the lifetime of any relay node network may be maximized.Dept. of Computer Science. Paper copy at Leddy Library: Theses & Major Papers - Basement, West Bldg. / Call Number: Thesis2006 .B37. Source: Masters Abstracts International, Volume: 45-01, page: 0348. Thesis (M.Sc.)--University of Windsor (Canada), 2006.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".