Multicast Routing Protocols in Mobile Ad Hoc Networks: A Comparative Survey and Taxonomy
Bibliographic record
Abstract
Multicasting plays a crucial role in many applications of mobile ad hoc networks (MANETs). It can significantly improve the performance of these networks, the channel capacity (in mobile ad hoc networks, especially single-channel ones, capacity is a more appropriate term than bandwidth , capacity is measured in bits/s and bandwidth in Hz) and battery power of which are limited. In the past couple of years, a number of multicast routing protocols have been proposed. In spite of being designed for the same networks, these protocols are based on different design principles and have different functional features when they are applied to the multicast problem. This paper presents a coherent survey of existing multicasting solutions for MANETs. It presents various classifications of the current multicast routing protocols, discusses their operational features, along with their advantages and limitations, and provides a comparison of their characteristics according to several distinct features and performance parameters. Moreover, this paper proposes classifying the existing multicast protocols into three categories according to their layer of operation, namely, the network layer, the application layer, and the MAC layer. It also extends the existing classification system and presents a comparison between them.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.007 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".