Energy Constrained Positioning in Mobile Wireless Ad hoc and Sensor Networks
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The positioning of wireless nodes has been an active research area over the last decade. Many network positioning algorithms have been proposed to solve this problem. The vast majority assume that a fixed set of nodes (called seeds) have a mechanism to position themselves at all times (using GPS or other means). The other nodes estimate their positions based on positional information exchanged between nodes using wireless communication. The monetary cost of a GPS unit is decreasing so systems where most nodes are equipped with a GPS unit is foreseeable. A problem with this assumption is that the task of self-positioning via a GPS unit is expensive in terms of energy consumption which results in faster battery decay. In this paper, we assume that every node is capable of self-positioning but activates that module selectively. This allows for balancing the energy costs of self-positioning among all nodes and results in reduced positional error. We investigate several different strategies for governing the self-positioning module and report on their performance.
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.
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 | 0.001 |
| 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 it