TIEGeR: An Energy-Efficient Multi-Parameter Geographic Routing Algorithm
Bibliographic record
Abstract
Geographic routing algorithms conventionally use one-hop greedy forwarding as their primary routing technique, which might lead to routing voids. Secondary routing schemes used to circumnavigate such routing voids are unfortunately not efficient in terms of throughput and energy consumption. Moreover, node residual energy and link quality are not considered during the routing process. This paper presents Two-hop Information based Energy- efficient Geographic Routing (TIEGeR) scheme to achieve effective energy balancing throughout the network, while preventing routing voids by proactively avoiding "local maxima" nodes. Distance to reach destination, node connectivity, link quality, and node residual energy are employed to formulate the routing metric for the TIEGeR. Besides, secondary routing scheme dealing with routing voids is supplemented by the reverse progress mode. Simulations verify the advantages of TIEGeR against conventional geographic routing schemes.
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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) | 0.000 | 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".