Randomized energy aware routing algorithms in mobile ad hoc networks
Bibliographic record
Abstract
We consider the problem of energy aware localized routing in ad hoc networks. In localized routing algorithms, each node forwards a message based on the position information about itself, its neighbors and the destination. The objective of energy aware routing algorithms is to minimize the total power for routing a message from source to destination or to maximize the total number of routing tasks that a node can perform before its battery power depletes. In this paper we extend our previous work on randomized localized routing algorithms that achieve high packet delivery rates and show that they have good overall power consumption. We present two different variants of energy aware randomized routing, namely greedy and compass, and we study their performance using different cost metrics (e.g., forwarding power, remaining node energy, or a combination of both). We study their performance experimentally on different topologies and compare it with other existing algorithms. Our simulation results show that energy aware randomized algorithms achieve superior packet delivery rates and moderate energy consumption.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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 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".