A Cooperative Routing Algorithm for Data Downloading in LEO Satellite Network

Yang, Zijian; Liu, Huijie; Jin, Jifeng; Tian, Feng

doi:10.1109/icct52962.2021.9657862

Cited by 4 publications

(1 citation statement)

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“…A load-balancing routing method based on the extended link states (LRES) algorithm, which uses a congestion avoidance mechanism, has been proposed to select a path from multiple paths between a source and a destination node in satellite constellations [ 29 ]. A cooperative data downloading (CDD) routing algorithm for LEO networks that use the inter-satellite laser link between the visible satellites to schedule feeder links’ bandwidth resources has been proposed to optimize remote sensing data downloading [ 30 ]. An autonomous on-board routing algorithm that makes routing decisions on each hop using the link state information of the ISLs between neighboring satellites was proposed for LEO satellite networks [ 31 ].…”

Section: Related Workmentioning

confidence: 99%

Delay-Packet-Loss-Optimized Distributed Routing Using Spiking Neural Network in Delay-Tolerant Networking

Velusamy

Lent

2022

Sensors

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Satellite communication is inevitable due to the Internet of Everything and the exponential increase in the usage of smart devices. Satellites have been used in many applications to make human life safe, secure, sophisticated, and more productive. The applications that benefit from satellite communication are Earth observation (EO), military missions, disaster management, and 5G/6G integration, to name a few. These applications rely on the timely and accurate delivery of space data to ground stations. However, the channels between satellites and ground stations suffer attenuation caused by uncertain weather conditions and long delays due to line-of-sight constraints, congestion, and physical distance. Though inter-satellite links (ISLs) and inter-orbital links (IOLs) create multiple paths between satellite nodes, both ISLs and IOLs have the same issues. Some essential applications, such as EO, depend on time-sensitive and error-free data delivery, which needs better throughput connections. It is challenging to route space data to ground stations with better QoS by leveraging the ISLs and IOLs. Routing approaches that use the shortest path to optimize latency may cause packet losses and reduced throughput based on the channel conditions, while routing methods that try to avoid packet losses may end up delivering data with long delays. Existing routing algorithms that use multi-optimization goals tend to use priority-based optimization to optimize either of the metrics. However, critical satellite missions that depend on high-throughput and low-latency data delivery need routing approaches that optimize both metrics concurrently. We used a modified version of Kleinrock’s power metric to reduce delay and packet losses and verified it with experimental evaluations. We used a cognitive space routing approach, which uses a reinforcement-learning-based spiking neural network to implement routing strategies in NASA’s High Rate Delay Tolerant Networking (HDTN) project.

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Section: Related Workmentioning

confidence: 99%