Routing is one of the main drivers of the end-to-end performance of bundle transmissions over a disruption tolerant network given the potentially large impact of the temporary but long-term partitioning that can occur at different sections of the network. A neuromorphic networking approach that defines an adaptive bundle routing for disruption-tolerant networks (DTN) is proposed where spiking neuronal networks (SNN) are used to determine the routing decisions of autonomous agents. The event-driven information encoding of spiking neurons involves very low energy consumption, which makes this approach attractive for challenging DTN applications with limited access to energy sources. The SNNs are continually updated within an autonomic loop, which produces synapse strength updates that are proportional to the expected communication costs of the routing decisions. A reward shaping procedure and a delay-tolerant mechanism for finding the local link-state is proposed, which allows determining instantaneous learning rewards for the agents. The method was tested on an emulated space communications network with scheduled disruptions. The results show that the proposed cognitive routing approach offers improved bundle delivery performance under network congestion compared to the standard Contact Graph Routing.

中文翻译：

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自适应 DTN 路由：神经形态网络视角

考虑到可能发生在网络不同部分的临时但长期分区的潜在巨大影响，路由是通过中断容忍网络实现捆绑传输端到端性能的主要驱动因素之一。提出了一种神经形态网络方法，该方法定义了用于容错网络 (DTN) 的自适应束路由，其中​​使用尖峰神经元网络 (SNN) 来确定自主代理的路由决策。尖峰神经元的事件驱动信息编码涉及非常低的能量消耗，这使得这种方法对于能源获取有限的具有挑战性的 DTN 应用具有吸引力。SNN 在自主循环中不断更新，它产生与路由决策的预期通信成本成正比的突触强度更新。提出了一种用于寻找本地链接状态的奖励整形程序和延迟容忍机制，它允许确定代理的瞬时学习奖励。该方法在具有预定中断的模拟空间通信网络上进行了测试。结果表明，与标准的联系图路由相比，所提出的认知路由方法在网络拥塞下提供了改进的捆绑交付性能。