Adaptive Multicast Routing Protocol Based on Reinforcement Learning
Bibliographic record
Abstract
Current Mobile Ad-hoc Network (MANET) multicasting approaches either suffer from low Packet Delivery Ratio (PDR) or high overhead.These methods rely on metrics like hop count to find the optimal path to the destination.Once the path is selected, all packets are sent over the same path as long as it is available.However, a path that is deemed optimal at a specific instance of time may not retain its optimality at a subsequent moment due to node mobility.Moreover, using a metric like hop count that does not consider link quality can lead to poor PDR, as it can favor an unreliable path over a reliable one just because it is the shortest.To tackle these concerns, Q-Learning Adaptive -Multicast Ad hoc On-Demand Distance Vector Routing (QLA-MAODV) protocol is proposed.It is an adaptive and bandwidthefficient multicast routing protocol based on Q-learning.Unlike traditional methods, QLA-MAODV prioritizes link reliability over simple metrics like hop count, aiming to build a more stable multicast tree.The protocol utilizes the periodic Group Hello (GRPH) messages to explore the environment, identifying alternative paths for use in case of path degradation.The protocol continuously updates path costs, facilitating proactive switching to more reliable paths.Simulations in Network Simulator 3 (NS-3) reveal the protocol's superiority over the traditional Multicast Ad-hoc On-demand Distance Vector (MAODV) protocol.Additionally, it outperforms a modified version, MAODV-Route Reliability (MAODV-RR), that uses link reliability as the routing metric, demonstrating improvement in PDR and reduced multicast-related overhead.I express my appreciation to my supervisors, Prof. Thomas Kunz and Prof. Marc St-Hilaire, for their support, guidance, and encouragement during challenging moments.Our discussions and your mentorship have been invaluable, contributing significantly to my intellectual growth and shaping my perspective in meaningful ways.To my parents, my life's biggest supporters.Their love and sacrifices are the cornerstone of my journey.I am grateful for the values they instilled in me.Thank you for being my pillars of strength and for shaping the person I
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".