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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".