Communication in 4G is expected to live with frequent disconnections due to wide ranging mobility patterns supported including vehicular, user planned disconnection, cellular, short-range and delay tolerant networks. Hence, 4G devices should always be on the lookout to find the best alternative for message delivery depending on availability and application requirements.
Both epidemic and Ant models are leading models for dealing with these challenges by continuously adapting and self organizing. The epidemic approach has proven efficiency for spreading information to all devices with high delivery rates. It nonetheless suffers from some QoS restrictions that could be reduced through buffer management and the use of adequate traffic engineering and routing policies. The probabilistic, random and social metrics can be used to decide whether and when a node must send some information.
On the other hand, the Ant model mimics the search for food, providing a mechanism for finding interesting routing paths according to QoS, security and others requirements. Moreover, the Ant model can increase or decrease the update process of the routing table according to traffic and network stability. When the scenario at hand is unstable, then the algorithm sends many "Forward Ants" to find new paths. Hence, the Ant model may then increase throughput while decreasing delay.
Given that a 4G device may be part of several networks, then it can execute a vertical handoff in order to obtain and exchange information from these different environments, improve its routing and also collaborate with other network users.
In the social routing approach, the nodes can exchange messages in order to identify popular ones and similarity behavior and consequently self-organize to improve their security and performance levels as seen in sections about social routing and social overlay networks.
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