  Since users move around based on social relationships and interests, the
resulting movement patterns can represent how nodes are socially connected
(i.e., nodes with strong social ties, nodes that meet occasionally by sharing
the same working environment). This means that social interactions reflect
personal relationships (e.g., family, friends, co-workers, passers-by) that may
be translated into statistical contact opportunities within and between social
groups over time. Such contact opportunities may be exploited to ensure good
data dissemination and retrieval, even in the presence of intermittent
connectivity. Thus, in the last years, a new trend based on social similarity
emerged where social relationships, interests, popularity and among others, are
used to improve opportunistic routing. In this chapter, the reader will learn
about the different approaches related to opportunistic routing focusing on the
social-aware approaches and how such approaches make use of social information
derived from opportunistic contacts to improve data forwarding. Additionally, a
brief overview on the existing taxonomies for opportunistic routing as well as
an updated one are provided along with a set of experiments in scenarios based
on synthetic mobility models and human traces in order to show the potential of
social-aware solutions.
