Power-aware Depth First Search based georouting in ad hoc and sensor wireless networks
Bibliographic record
Abstract
Depth First Search (DFS) and position based routing algorithms were proposed in literature. These are localized algorithms that guarantee the delivery for connected ad hoc and sensor wireless networks modeled by arbitrary graphs, including inaccurate location information for a destination node. This paper first optimizes an existing DFS based routing scheme by eliminating from the candidate list neighbors whose messages to other nodes were overheard. We then introduce a new set of localized routing algorithms. The new DFS routing protocol is integrated with power metrics minimizing total power for routing of a message. These DFS Power Progress based algorithms are combinations of known greedy power ad DFS routing algorithms. All algorithms are further enhanced by applying the concept of connected dominating sets, which greatly reduced the search path without impacting significantly the length of effectively constructed path for real tragic. Experiments confirm the efficiency of the new enhanced DFS, power aware and connected dominating set based routing algorithms and ability to guarantee the delivery in arbitrary model due to the DFS routing framework.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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 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".