# A Cognitive Anycast Routing Method for Delay-Tolerant Networks
## 1 Introduction  
With anycast routing, data can be delivered to (or from) any one of equally suitable sinks (or sources) by establishing a one to any-of-many relation. When correctly used, it can help to simplify network services while improving the data delivery performance. Practical uses of anycast routing today can be found mainly for the terrestrial domain. A common application involves information retrieval in content-delivery networks (CDN), where user requests can be served from the most appropriated data center hosting a geographically replicated service [1; 2; 3; 4]. This alternative to unicast routing helps to reduce service latency that can originate from normal or intended network congestion, e.g., as produced by a denial-of-service attack [5]. In addition, it helps improve service resiliency to partial network outages [6]. Looking beyond 5G, anycast routing can serve as an efficient data delivery mechanism for integrated local, cellular, and satellite networks to handle massive and high-frequency data collection and dissemination tasks. Despite not being previously explored in detail, anycast routing may help to mitigate some of the challenges involved in space networks [7; 8]. For instance, consider a constellation of LEO satellites [9] providing intermittent communication services for a large number of sensor nodes of a smart environment (e.g., smart ocean [10]). The data from the nodes could be forwarded to different sinks through diverse paths, which may be automatically selected by the anycast service based on the location, network congestion levels, and satellite link availability. Similarly, consider a high throughput satellite (HTS) system with site diversity [11; 12], where the ground gateway sites are interconnected via high-speed links. The use of the millimeter wave spectrum (mmWave) and free-space optical (FSO) in the feeder links makes the communication channels highly susceptible to atmospheric phenomena [13]. In this case, anycast routing could help to automate the traffic distribution among multiple feeder links and mitigate the impact of adverse weatheraffecting one or more gateway sites. As a third example, consider a space-ground sensor network with limited contact opportunities. In this scenario, it is permissible to expect that the data collected from the space sensors could be delivered to any ground station for processing or their central collection using ground channels. These are a few examples that show the potential benefits of the integration of anycast routing and delay-tolerant networking (DTN) [14]. This work explores how to achieve (near) optimal anycast routing on a space-ground DTN. The optimality of the routing is defined with respect to a metric of interest, commonly the response time of the data bundles. The main features of the network involve regular but mostly predictable contact opportunities, time-varying channel conditions that may not be observable network-wide by all nodes, and asymmetric and large propagation delays. The Consultative Committee for Space Data Systems (CCSDS) standard on space DTNs is Schedule-Aware Bundle Routing (CCSDS, 734.3-B-1), which uses Contact Graph Routing (CGR) to determine unicast paths [15]. However, the standard does not define anycast routing capabilities. Additionally, a subset of the network nodes may be geographically dispersed and subject to connectivity losses for extended periods, and therefore network partitions are possible. This feature conveys the general lack of knowledge about network-wide conditions, such as the actual buffer occupancies and the channel bit error rates, which creates additional challenges to the identification of the optimal paths from a bundle delivery time perspective. The proposed method builds on top of the Cognitive Space Gateway (CSG) [16] approach to unicast routing that has been proposed for space DTNs. A spiking neural network (SNN) and learning method are used to formulate the Cognitive Network Controller (CNC) [17; 18], which is used as the learning element of an autonomic loop.