Enhancing Reliability of Tactical MANETs by Improving Routing Decisions
Bibliographic record
Abstract
Mobile ad-hoc networks (MANETs) have been primarily designed to enhance tactical communications in a battlefield. They provide dynamic connectivity without requiring any pre-existing infrastructure. Their multi-hop capabilities can improve radio coverage significantly. The nature of tactical MANET operations requires more specialized routing protocols compared to the ones which are used in commercial MANET. Routing decisions in MANETs are usually conditioned on signal-to-interference-plus-noise ratio (SINR) measurements. In order to improve routing decisions for use in highly dynamic tactical MANETs, this paper proposes to combine two different metrics to achieve reliable multicast in multi-hop ad hoc networks. The resulting protocol combining received signal strength (RSS) with SINR to make routing decisions is referred to as Link Quality Aware Ad-hoc On-Demand Distance Vector (LQA-AODV) routing. The proposed routing protocol can quickly adapt to dynamic changes in network topology and link quality variations often encountered in tactical field operations. Using computer simulations, the performance of proposed protocol is shown to outperform other widely used reactive routing protocols assuming several performance metrics.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".