Impact of Deployment Strategies and Mobility Models on MANET Routing Protocols: A Performance Evaluation
Bibliographic record
Abstract
Mobile Ad Hoc Networks (MANETs) have become dominant networks in the current technological era due to their importance in the Internet of Things (IoT) and the future of smart cities.The problem with MANET networks is that they are not stable in performance because many factors can be involved (i.e., deployment of mobile nodes, movements of nodes, the nature of the environment, etc.).For instance, selecting a routing protocol is considered a challenging task because it is not the only factor affecting network performance.Despite extensive studies on MANET routing, the combined impact of deployment strategies and mobility models remains underexplored.Hence, in this paper, two routing protocols are developed, designed, and implemented.Moreover, to understand MANET network performance, using OMNeT++, we simulated 105 scenarios combining 3 deployment strategies (Normal, Uniform, Exponential) and 5 mobility models (Correlated Direction, Cauchy Flight, Exponential, Levy Flight, Individual Mobility).Statistical significance was validated via ANOVA (p < 0.03).These experiments include combinations of deployment strategies (i.e., Normal, Uniform, and Exponential deployment strategies).Five mobility models are also implemented and incorporated into the design of the simulator and experiments, such as the Correlated Direction mobility model, the Cauchy flight mobility model, the Exponential mobility model, the Levy Flight mobility model, and the Individual Mobility model.Also, two evaluation metrics are involved, namely, coverage area and data spreading.The findings show that the proposed routing protocols outperform the benchmarking, and their results are statistically significant.
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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 | 0.005 | 0.017 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| 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".