Hybrid Position-Based Routing Algorithms for 3D Mobile Ad Hoc Networks
Bibliographic record
Abstract
Numerous routing algorithms have been proposed for routing efficiently in mobile ad hoc networks (MANETs) embedded in two dimensional (2D) spaces. But, in practice, such networks are frequently arranged in three dimensional (3D) spaces where the assumptions made in two dimensions, such as the ability to extract a planar subgraph, break down. Recently, a new category of 3D position-based routing algorithms based on projecting the 3D MANET to a projection plane has been proposed. In particular, the adaptive least-squares projective (ALSP) face routing algorithm (Kao et al., 2007) achieves nearly guaranteed delivery but usually discovers excessively long routes to the destination. Referencing the idea of hybrid greedy-face-greedy (GFG) routing in 2D MANETs, we propose a local hybrid algorithm combining greedy routing with ALSP Face routing on projection planes. We show experimentally that this hybrid ALSP GFG routing algorithm on static 3D ad hoc networks can achieve nearly guaranteed delivery while discovering routes significantly closer in length to shortest paths. The mobility of nodes is handled by introducing the concepts of active sole nodes and a limited form of flooding called residual path finding to the ALSP GFG routing algorithm. Under mobility simulations, we demonstrate that the mobility-adapted hybrid routing algorithm can maintain high delivery rates with decreases in the average lengths of the paths discovered compared to shortest paths, without generating a large amount of flooding traffic.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".