QoS multilayered multicast routing protocol for video transmission in heterogeneous wireless ad hoc networks
Bibliographic record
Abstract
Abstract:- In wireless ad hoc networks, nodes are expected to be heterogeneous with a set of multicast destinations greatly differing in their end devices and QoS requirements. This paper proposes two algorithms for multilayered video multicast over heterogeneous wireless ad hoc networks. The two algorithms are, Multiple Shortest Path Tree (MSPT) and Multiple Steiner Minimum Tree (MSMT). In this paper, we assume that each destination has a preference number of video layers; which is equal to its capacity. Moreover, we do not consider only the capacities of nodes in the network but also the bandwidth of each link. In order to increase user satisfaction for a group of heterogeneous destinations, we exploit different types of multiple multicast trees policy. Simulations show that the proposed schemes greatly improve the QoS requirements (increase user satisfaction) for a set of destinations. In addition, simulations show that multiple Hybrid-II multicast trees offer higher user satisfaction than multiple Hybrid-I multicast trees and multiple node-disjoint trees. The cost of that is the robustness against link failure. Therefore, it is a trade off between providing robustness against path breaks and increasing user satisfaction. Key-Words:- Multilayered multicast; MDC; LC, Heterogeneous wireless ad hoc networks.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".