Multipath Routing Protocol Using Genetic Algorithm in Mobile Ad Hoc Networks
Bibliographic record
Abstract
Mobile ad hoc network (MANET) is a cluster of wireless mobile gadgets that creates a temporary network without seeking support from any infrastructure or central management. Energy consumption should be considered as one of the foremost vital limitations in MANETs because the mobile nodes do not possess a constant power supply and its shortage will minimize the network's lifetime. MANETs get energy from the batteries which get exhausted very quickly because of issues like node mobility, computation power, frequent data retransmissions needed in wireless communication, etc. Secondly, there is a data packet loss caused by different reasons such as traffic congestion or random loss as a result of nodes mobility or noise. This data loss, in turn, would delay packets delivery degrading data transmission in real-time applications. This paper provides management for this combination of major problems in MANETs. We present a new fitness function (FFn) used in the Genetic Algorithm (GA) to obtain the optimized route from those routes offered by the Ad hoc On-demand Multipath Distance Vector (AOMDV) routing protocol. Accordingly, we propose a routing protocol titled as AOMDV with FFn (AOMDV-FFn). We also integrate the AOMDV mechanism with the genetic algorithm (AOMDV-GA). These protocols provide an optimization process to select the efficient routes that have the highest fitness values implementing the shortest route, maximum residual energy, and less data traffic even if a random loss of data packets happens. In this regard, we introduce a mechanism where the TCP Congestion Control Enhancement for Random Loss (TCP CERL) can be utilized in the FFn to optimize the efficient route. The performance of the proposed mechanisms is compared with other preferred protocols proposed in this area.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".