# A Cognitive Anycast Routing Method for Delay-Tolerant Networks
## 2 Related Works  
Prior works have investigated anycast for regular networks, i.e., holding the original Internet-design assumptions. Anycasting is supported by both versions 4 and 6 of the Internet Protocol (IP) as documented in RFCs 1546 and 4291, and the Border Gateway Protocol (BGP-4, RFC 4271). It plays a vital role for DNS and CDN and services, but different issues have been noted, including the detection latency of IP anycast prefixes [20], AS-level anycast path inflation [21], and client-server mapping limitations that arise in CDN routing [2]. Several enhancements have been discussed focusing on either performance, see, e.g., [22; 23], or security, e.g., to mitigate distributed denial-of-service (DDoS) attacks [5]. It has been shown that anycast routing can achieve near-optimal response times and that it can be tailored to suit the QoS requirements of DiffServ networks [24], but these techniques often require knowledge about the global network state (i.e., router loads) [25; 26]. Anycast services can also be used at other layers of the protocol stack. Representative examples include methods to achieve low-latency access for edge computing services [27], to overcome the high packet loss rates of Internet-of-Things (IoT) environments [6], to optimize the selection of replicated web servers [28], to improve the reservation system for electric vehicles and reduce the wait time [29], and to reduce the energy consumption of data centers [30] and wireless sensor networks [31; 32].  
However, DTNs and space networks are characterized by the lack of Internet-design assumptions, and therefore the works discussed above may not be directly applicable. One of the issues is the lack of a permanent end-to-end connectivity among all nodes [33]. Currently, the literature on anycast DTN routing is sparse. A group of works has addressed the probabilistic DTN case with random contacts. Along that line of research, the anycast social distance metric (ASDM) was introduced [34]. The ASDM helps to direct routing messages in the direction of the location where most group members are expected to be present. In the time constrained anycast (TCA) method [35], the one-hop and two-hop delivery probability to the anycast group are determined from the distribution of inter-contact times (ICT). Routing decisions are then made based on an exponential distribution of ICTs. An extension to CGR has also been proposed [36], which considers the history of the observed contacts.  
Theoretical studies of the problem include the application of epidemic routing to DTN anycasting, which has been studied under a Markovian assumption [37]. The semantics related to the address identifier to be used in DTNs have been examined at least for the case without recurrent network partitioning [38]. Larger anycast groups increase the chances of delivering the information quickly to any of the group members. This case was studied in simulation for a sparse mobile network communicating ad hoc and using the receiver-based forwarding (RBF) approach [39]. A genetic algorithm (GA) has been applied to DTN anycast routing for deterministic networks [40], showing reduced average delays compared to the shortest path but mainly with a small number of concurrent sessions, possibly because of the slow convergence time of GAs.