  Opportunistic routing is being investigated to enable the proliferation of
low-cost wireless applications. A recent trend is looking at social structures,
inferred from the social nature of human mobility, to bring messages close to a
destination. To have a better picture of social structures, social-based
opportunistic routing solutions should consider the dynamism of users' behavior
resulting from their daily routines. We address this challenge by presenting
dLife, a routing algorithm able to capture the dynamics of the network
represented by time-evolving social ties between pair of nodes. Experimental
results based on synthetic mobility models and real human traces show that
dLife has better delivery probability, latency, and cost than proposals based
on social structures.
