College of Technology, University of Houston, Houston, TX 77204, USA.
Sensors (Basel). 2022 Dec 28;23(1):310. doi: 10.3390/s23010310.
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.
由于万物互联和智能设备使用量的指数级增长，卫星通信是不可避免的。卫星已经在许多应用中被用于使人类生活更安全、更可靠、更复杂和更高效。受益于卫星通信的应用有地球观测（EO）、军事任务、灾害管理和 5G/6G 集成等。这些应用依赖于将空间数据及时准确地传送到地面站。然而，卫星和地面站之间的信道会受到不确定的天气条件和由于视距限制、拥塞和物理距离导致的长延迟的衰减。虽然星间链路（ISLs）和轨道间链路（IOLs）在卫星节点之间创建了多条路径，但 ISLs 和 IOLs 都存在相同的问题。一些关键应用，如 EO，依赖于对时间敏感且无错误的数据传输，这需要更好的吞吐量连接。通过利用 ISLs 和 IOLs，将空间数据路由到地面站并提供更好的服务质量是具有挑战性的。通过使用最短路径来优化延迟的路由方法可能会导致根据信道条件造成数据包丢失和吞吐量降低，而试图避免数据包丢失的路由方法最终可能会导致数据延迟很长时间才送达。现有的使用多优化目标的路由算法往往使用基于优先级的优化来优化其中一个指标。然而，依赖于高吞吐量和低延迟数据传输的关键卫星任务需要同时优化这两个指标的路由方法。我们使用了 Kleinrock 的功率度量的修改版本来减少延迟和数据包丢失，并通过实验评估进行了验证。我们使用了一种认知空间路由方法，该方法使用基于强化学习的尖峰神经网络在 NASA 的高数据速率延迟容忍网络（HDTN）项目中实现路由策略。
