However, when instantaneous end-to-end paths are difficult or impossible to establish, routing protocols must take to a "store and forward" approach[citation needed], where data is incrementally moved and stored throughout the network in hopes that it will eventually reach its destination.
[7] On the contrary, in disaster recovery networks the future location of communicating entities, such as emergency responders, may not be known.
For instance, in interplanetary networks, large objects in space, such as planets, can block communicating nodes for a set period of time.
Many nodes, such as mobile phones, are limited in terms of storage space, transmission rate, and battery life.
Routing protocols can utilize this information to best determine how messages should be transmitted and stored to not over-burden limited resources.
As of April 2008, only recently has the scientific community started taking resource management into consideration, and this is still an active area of research.
This eliminates the need for the destination to provide feedback to the network (except for, perhaps, an acknowledgments sent to the sender), to indicate outstanding copies can be deleted.
[11] Replication-based protocols, on the other hand, allow for greater message delivery rates,[3] since multiple copies exist in the network, and only one (or in some cases, as with erasure coding, a few) must reach the destination.
It is important to note that the vast majority of DTN routing protocols are heuristic-based, and non-optimal.
In the most simple case, epidemic routing is flooding; however, more sophisticated techniques can be used to limit the number of message transfers.
Epidemic routing is particularly resource hungry because it deliberately makes no attempt to eliminate replications that would be unlikely to improve the delivery probability of messages.
The delivery predictabilities used by each Mule are recalculated at each opportunistic encounter according to three rules: The protocol has been incorporated into the reference implementation maintained by the IRTF DTN Research Group and the current version is documented in RFC 6693.
MaxProp[3] was developed at the University of Massachusetts, Amherst and was, in part, funded by DARPA and the National Science Foundation.
In conjunction with the core routing described above, MaxProp allows for many complementary mechanisms, each helping the message delivery ratio in general.
RAPID,[10] which is an acronym for Resource Allocation Protocol for Intentional DTN routing, was developed at the University of Massachusetts, Amherst.
The authors of RAPID argue as a base premise that prior DTN routing algorithms incidentally effect performance metrics, such as average delay and message delivery ratio.
RAPID, like MaxProp, is flooding-based, and will therefore attempt to replicate all packets if network resources allow.
It was first presented at the 2005 ACM SIGCOMM conference, under the publication "Spray and Wait: An Efficient Routing Scheme for Intermittently Connected Mobile Networks".
Spray and Wait achieves resource efficiency by setting a strict upper bound on the number of copies per message allowed in the network.
The simplest way to achieve this, known as the vanilla version, is for the source to transmit a single copy of the message to the first
They study the social structures of the between devices and leverage them in the design of forwarding algorithms for Pocket Switched Networks(PSNs).
With experiments of real world traces, they discover that human interaction is heterogeneous both in terms of hubs and groups or communities.
This algorithm also shows how it can be implemented in a distributed way, which demonstrates that it is applicable in the decentralized environment of PSNs.
CafRep[17] is a fully localised adaptive forwarding & replication protocol with congestion control and avoidance to enable congestion-aware mobile social framework in heterogeneous DTNs.
CafRep uses a combined social, buffer and delay metrics for congestion-aware message forwarding and replication that maximises message delivery ratio and availability of nodes while minimising latency and packet loss rates at times of increasing congestion levels.
At the core of CafRep is a combined relative utility driven heuristics that allow highly adaptive forwarding and replication policies by managing to detect and offload congested parts of the network and adapting the sending/forwarding rates based on resource and contact predictions.
SABR then makes forwarding decisions based on an earliest-arrival-time metric where bundles are routed over the time-varying connectivity graph.
SABR uses historical contact information and neighbor discovery to address routing over non-scheduled links.
Nevertheless, rational nodes in real-world scenarios have strategic interactions and may exhibit selfish behaviours due to various reasons (such as resource limitations, the lack of interest in data, or social preferences).
Meanwhile, malicious nodes may attack the network in different ways to disturb the normal operation of the data transmission process.