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We explore the benefits of using fine-grained statistics in small world DTNsto achieve high throughput without the aid of external infrastructure. We firstdesign an empirical node-pair inter-contacts model that predicts meetingswithin a time frame of suitable length, typically of the order of days, with aprobability above some threshold, and can be readily computed with lowoverhead. This temporal knowledge enables effective time-dependent pathplanning that can be respond to even per-packet deadline variabilities. Wedescribe one such routing framework, REAPER (for Reliable, Efficient andPredictive Routing), that is fully distributed and self-stabilizing. Its keyobjective is to provide probabilistic bounds on path length (cost) and delay ina temporally fine-grained way, while exploiting the small world structure toentail only polylogarithmic storage and control overhead. A simulation-basedevaluation confirms that REAPER achieves high throughput and energy efficiencyacross the spectrum of ultra-light to heavy network traffic, and substantiallyoutperforms state-of-the-art single copy protocols as well as sociability-basedprotocols that rely on essentially coarse-grained metrics.

Achieving Throughput via Fine-Grained Path Planning in Small World DTNs