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Delay-Packet-Loss-Optimized Distributed Routing Using Spiking Neural Network in Delay-Tolerant Networking
Gandhimathi Velusamy1, Ricardo Lent1
1College of Technology, University of Houston, Houston, TX 77204, USA.
Sensors (Basel, Switzerland)
|January 8, 2023
Summary
Satellite communication faces challenges with data delivery delays and packet loss. This study introduces a cognitive space routing approach using a spiking neural network to optimize both latency and throughput for critical space missions.
Area of Science:
- Space communication networks
- Network routing algorithms
- Machine learning in space systems
Background:
- Satellite communication is crucial for the Internet of Everything and smart devices, supporting applications like Earth observation and 5G/6G.
- Challenges in satellite-to-ground links include weather attenuation, long delays, and congestion, impacting timely data delivery.
- Existing routing methods often prioritize latency or packet loss, failing to concurrently optimize both for demanding applications.
Purpose of the Study:
- To develop a novel routing approach for satellite networks that concurrently optimizes for low latency and high throughput.
- To address the limitations of current routing algorithms in meeting the Quality of Service (QoS) demands of critical satellite missions.
- To enhance data delivery efficiency for applications like Earth observation that require time-sensitive and error-free transmission.
Main Methods:
- A modified Kleinrock's power metric was employed to simultaneously minimize delay and packet loss.
- A cognitive space routing strategy was implemented using a reinforcement-learning-based spiking neural network.
- The proposed routing approach was evaluated within NASA's High Rate Delay Tolerant Networking (HDTN) project framework.
Main Results:
- The modified Kleinrock's power metric effectively reduced both delay and packet loss in simulated satellite communication channels.
- Experimental evaluations demonstrated the viability of the cognitive space routing approach in improving data delivery performance.
- The reinforcement-learning-based spiking neural network enabled adaptive routing strategies tailored to dynamic space network conditions.
Conclusions:
- Concurrent optimization of latency and throughput is achievable and necessary for advanced satellite communication applications.
- Cognitive space routing, powered by spiking neural networks, offers a promising solution for enhancing satellite network performance.
- The developed routing method contributes to more reliable and efficient data transmission in delay-tolerant space networks.
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