HMS: Holistic MPR selection and network connectivity for tactical edge networks
Bibliographic record
Abstract
MANET is seen as a promising technology that helps facilitate range extension in tactical edge networks (TENs) where both realtime broadcast and unicast are critical communications. These services are usually provided by proactive routing protocols with Multipoint Relay (MPR) technology. The standardized MPR selection method produces redundant MPR nodes which can cause repeated transmissions of broadcast traffic in TENs where bandwidth and power are scarce. Efficient minimum MPR set optimization remains a challenge. In addition to MPR selection, network connectivity is another important feature for how to best deploy connected MANETs at the tactical edge and ensure its reliable connectivity. To the best of our knowledge, little research has been done on this front. In this paper, we propose a holistic MPR selection (HMS) strategy that selects nearly-optimized MPR sets for a MANET in a pre-defined area at varying radio transmission ranges. We show through simulations that HMS is very close to the lower bound of the optimal MPR number and reduces over 50% MPRs compared to the greedy heuristic method in most tactical edge scenarios. We introduce a method to arrive at the minimum MPR size for fully covering a pre-defined area with different radio transmission ranges and investigate the relationship of MPR set with network connectivity. We developed the algorithm with simple geometry and found that our MPR comparison results among HMS, the minimum MPR size, and the lower bound of the optimal MPR number deliver radio range boundaries that differentiate three different grades of connectivity. The range boundaries can be used as an advanced feature for how to best deploy a MANET at the tactical edge for reliable connectivity.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 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".