Multipath routing for mobile ad hoc networks
Why this work is in the frame
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Bibliographic record
Abstract
A Mobile Ad hoc NETwork(MANET) is a collection of wireless mobile computers forming a temporary network without any existing wire line infrastructure. Due to the dynamic nature of network topologies and the resource constraints, routing in MANETs is a challenging task. Multipath routing can increase end-to-end throughput and provide load balancing in wired networks. However, its advantage is not obvious in mobile ad hoc networks because the traffic flows along the multiple paths may interfere with each other. In addition, without accurate knowledge of topology, finding multiple node-disjoint paths is difficult. In this paper, we propose two on-demand methods to effectively search for multiple node-disjoint paths and present the path selection criteria. Compared with Dynamic Source Routing (DSR) and the Diversity Injection method, our methods can find more node-disjoint paths and thus provide source nodes with more choices to select good quality multiple paths. We also perform simulation studies on the proposed approaches. The simulation results show that our multipath routing methods can reduce the frequency of route discoveries and balance network loads. In addition, our Heuristic Redirection multipath routing method can reduce control overheads, improve end-to-end delay, and provide fair energy consumption among mobile hosts. The purpose of this paper is to present the advantages as well as the challenges of deploying multipath routing in mobile ad hoc networks.
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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 | 0.002 | 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 it