Routing Protocol in Mobile Ad Hoc Networks based on Energy Consumption
Bibliographic record
Abstract
Due to the network’s resource limitations and its dynamic nature, routing in mobile ad hoc networks (MANETs) is one of the most difficult jobs. Ad hoc infrastructure and node mobility add to the network’s complexity and instability. Finding the desired path from source to destination using routing techniques becomes even hazier due to limited radio range communication and dynamic topology. Numerous studies offer a wide range of techniques for transferring data from one place to another. In this paper, we have taken the energy of the nodes and the distance between the nodes as key components in deciding whether the route established with these components is appropriate or not. We are using the ad hoc on-demand multipath distance vector (AOMDV) protocol which is considered one of the most effective reactive routing techniques to deal with the unpredictability of the routing path in MANETs. Along with that, we propose a genetic algorithm-based new protocol that will provide the most energy-efficient path by considering the least distance between nodes with the help of mutation and crossover. Lastly, the proposed protocol results are compared with existing routing protocols like AOMDV and DSR. We have used routing overhead, end-to-end delay, throughput, energy consumption, and packet delivery ratio as key metrics for the performance evaluation of our proposed model.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".