Enregistré dans:
| Auteurs principaux: | , |
|---|---|
| Format: | Preprint |
| Publié: |
2025
|
| Sujets: | |
| Accès en ligne: | https://arxiv.org/abs/2512.20394 |
| Tags: |
Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
|
| _version_ | 1866908730489044992 |
|---|---|
| author | Charrwi, Mohammad Walid Hussain, Zaid |
| author_facet | Charrwi, Mohammad Walid Hussain, Zaid |
| contents | As Network-on-Chip (NoC) and Wireless Sensor Network architectures continue to scale, the topology of the underlying network becomes a critical factor in performance. Gaussian Interconnected Networks based on the arithmetic of Gaussian integers, offer attractive properties regarding diameter and symmetry. Despite their attractive theoretical properties, adaptive routing techniques in these networks are vulnerable to node and link faults, leading to rapid degradation in communication reliability. Node failures (particularly those following Gaussian distributions, such as thermal hotspots or physical damage clusters) pose severe challenges to traditional deterministic routing. This paper proposes a fault-aware Reinforcement Learning (RL) routing scheme tailored for Gaussian Interconnected Networks. By utilizing a PPO (Proximal Policy Optimization) agent with a specific reward structure designed to penalize fault proximity, the system dynamically learns to bypass faulty regions. We compare our proposed RL-based routing protocol against a greedy adaptive shortest-path routing algorithm. Experimental results demonstrate that the RL agent significantly outperforms the adaptive routing sustaining a Packet Delivery Ratio (PDR) of 0.95 at 40% fault density compared to 0.66 for the greedy. Furthermore, the RL approach exhibits effective delivery rates compared to the greedy adaptive routing, particularly under low network load of 20% at 0.57 vs. 0.43, showing greater proficiency in managing congestion, validating its efficacy in stochastic, fault-prone topologies |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_20394 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Resilient Packet Forwarding: A Reinforcement Learning Approach to Routing in Gaussian Interconnected Networks with Clustered Faults Charrwi, Mohammad Walid Hussain, Zaid Distributed, Parallel, and Cluster Computing As Network-on-Chip (NoC) and Wireless Sensor Network architectures continue to scale, the topology of the underlying network becomes a critical factor in performance. Gaussian Interconnected Networks based on the arithmetic of Gaussian integers, offer attractive properties regarding diameter and symmetry. Despite their attractive theoretical properties, adaptive routing techniques in these networks are vulnerable to node and link faults, leading to rapid degradation in communication reliability. Node failures (particularly those following Gaussian distributions, such as thermal hotspots or physical damage clusters) pose severe challenges to traditional deterministic routing. This paper proposes a fault-aware Reinforcement Learning (RL) routing scheme tailored for Gaussian Interconnected Networks. By utilizing a PPO (Proximal Policy Optimization) agent with a specific reward structure designed to penalize fault proximity, the system dynamically learns to bypass faulty regions. We compare our proposed RL-based routing protocol against a greedy adaptive shortest-path routing algorithm. Experimental results demonstrate that the RL agent significantly outperforms the adaptive routing sustaining a Packet Delivery Ratio (PDR) of 0.95 at 40% fault density compared to 0.66 for the greedy. Furthermore, the RL approach exhibits effective delivery rates compared to the greedy adaptive routing, particularly under low network load of 20% at 0.57 vs. 0.43, showing greater proficiency in managing congestion, validating its efficacy in stochastic, fault-prone topologies |
| title | Resilient Packet Forwarding: A Reinforcement Learning Approach to Routing in Gaussian Interconnected Networks with Clustered Faults |
| topic | Distributed, Parallel, and Cluster Computing |
| url | https://arxiv.org/abs/2512.20394 |