Distributed Routing and Data Scheduling in IPNs With GNN-Based Multiagent DRL
Bibliographic record
Abstract
As deep space exploration missions grow in complexity, efficient data transfer in interplanetary networks (IPNs) becomes paramount. However, the vast distances, limited bandwidth, and dynamic nature of IPNs pose significant challenges for the routing and data scheduling of interplanetary data transfers (IP-DTs). To address these challenges, this work proposes a novel distributed, graph neural network (GNN) based multiagent deep reinforcement learning (DRL) approach that can jointly optimize the routing and scheduling of IP-DTs. Our proposal is based on the proximal policy optimization (PPO) framework along with the graph attention networks (GATs). We make the DRL agents for IPN nodes in each subnetwork around a celestial body learn and operate independently, for making intelligent routing and scheduling decisions to properly tradeoff between average end-to-end (E2E) latency and delivery ratio of IP-DTs while ensuring good scalability. Extensive simulations confirm that our proposal handles the routing and scheduling of IP-DTs much better than existing benchmarks. Further, by modifying the interplanetary overlay network (ION) software platform developed by NASA, we build a semi-physical IPN emulator based on Raspberry Pi boards, implement our proposal in it, and conduct experiments with real data transfers between IPN nodes. Experimental results verify that our proposal can work for practical IPNs without causing excessive overheads and prove its advantages.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 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 teacher head, 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".