Analysis of the impact of swarm mobility on performance of routing protocols in MANETs
Bibliographic record
Abstract
In a mobile ad-hoc network (MANET), node mobility has a significant impact on the performance of routing protocols. Most of the previous research has been focused on entity mobility models, i.e., movements of the mobile nodes are independent of each other. In this paper, we investigate the impact of swarming behavior of mobile nodes, as observed in many mobile networks, on the performance of MANET routing protocols. The effects of coordinated movements of mobile nodes are characterized by using a Markov chain, through which a quantized collaboration degree is defined. Based on the swarm mobility model, we analyze the probabilistic properties of hop count as a complement to those analytical studies on packet delay performance. With a medium access control model, we derive an upper and a lower bound of routing overhead for MANET proactive routing protocols. Simulations are used to demonstrate the validity of the derived analytical expressions. Numerical and simulation results show that more coordinated movements of the nodes reduce the number of control packets required to be disseminated over the network, and in turn the routing overhead.
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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 | 0.002 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| 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) | 0.000 | 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 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".