## 2 Related Work  
In the past several years, several approaches have been proposed to address the routing problem in VDTN, but due to the particular characteristics of vehicular environments, and especially the lack of an end-to-end connection between nodes in a vehicular network, non-deterministic approaches must be used [10; 11]. Some routers for delay-disruption tolerant networks, like the epidemic router [15] and the spray and wait router [16], use a flooding-based principle of spreading copies of the messages to newly discovered contacts. The epidemic router is one of the most popular routers in this category [7; 15], whose approach is to distribute messages to other hosts within connected portions of the network, relying upon such carriers coming into contact with another connected portion of the network through node mobility, hoping that through that transitive transmission of data, messages will eventually reach their destination. This routing protocol provides an acceptable delivery rate and delay but at the expense of using too many resources in the network. In the same way, the spray and wait router [16] uses a similar (flooding-based) but more controlled mechanism, \"spraying\" a number of copies into the network, and \"waiting\" until one of these nodes meets the destination. More particularly, this router passes \(L\) copies from the source node (phase 1--spray), and then each of the \(L\) copies waits in their temporal host until there is a contact, if any, with the destination (phase 2--wait), to whom they are only then forwarded. Other routers use probabilistic approaches to increase the chances of packet delivery. MaxProp [22] is one of the first routers proposed in this category. This router uses what the authors call an estimated _delivery likelihood_ for each node in the network, updated through incremental averaging, so nodes that are seen infrequently obtain lower values over time, and packets that are ranked with the highest priority are the first to be transmitted during a transfer opportunity, whereas those ranked with the lowest priority are the first to be deleted to make room for incoming packets. On the other hand, the PRoPHET Router [17] is perhaps the most popular router in the probabilistic routing category. Based on the history of encounters between the nodes, this router introduces a metric called _delivery predictability_, a set of probabilities for successful delivery to known destinations in the network and established at each node for each known destination. This way, when nodes meet, they exchange information about the delivery predictabilities and update their own information accordingly, and the final forwarding decision is made based on these values to whether or not pass the current message to particular nodes. In recent years, the use of artificial intelligence techniques has gained tremendous popularity because of the successful application to many practical optimization, prediction, and classification problems that include image processing (facial recognition, cancer detection, etc. ), forecasting (stock prediction, weather forecasting, etc. ), and others [23; 24]. The application of AI-based algorithms to the routing problem in VDTN, however, is still not fully explored, although some efforts have been conducted towards this direction. In this category, SeeR is one of the most efficient routers [18]. This router uses the simulated annealing algorithm to evaluate which messages are best to be transferred in each contact opportunity. Each message is associated with a cost function in terms of the hop count and the average intercontact time of the current node, and one node transfers a message to another node if the second node offers lower cost value. Otherwise, the messages are forwarded, first decreasing their probability. Their experiment results show considerable gains in the average delivery ratio and improvements in delivery delays with respect to flooding algorithms like epidemic routing and spray and wait.