  We explore the benefits of using fine-grained statistics in small world DTNs
to achieve high throughput without the aid of external infrastructure. We first
design an empirical node-pair inter-contacts model that predicts meetings
within a time frame of suitable length, typically of the order of days, with a
probability above some threshold, and can be readily computed with low
overhead. This temporal knowledge enables effective time-dependent path
planning that can be respond to even per-packet deadline variabilities. We
describe one such routing framework, REAPER (for Reliable, Efficient and
Predictive Routing), that is fully distributed and self-stabilizing. Its key
objective is to provide probabilistic bounds on path length (cost) and delay in
a temporally fine-grained way, while exploiting the small world structure to
entail only polylogarithmic storage and control overhead. A simulation-based
evaluation confirms that REAPER achieves high throughput and energy efficiency
across the spectrum of ultra-light to heavy network traffic, and substantially
outperforms state-of-the-art single copy protocols as well as sociability-based
protocols that rely on essentially coarse-grained metrics.
