SPRINT: Social prediction-based opportunistic routing

TLDR

Opportunistic networks rely on store‑carry‑and‑forward and predictable contacts, making traditional routing mechanisms inadequate. The study proposes SPRINT, an opportunistic routing algorithm that adds online social information to increase message delivery success. SPRINT augments routing with online social data and a prediction component derived from Poisson‑modeled human mobility. Using Poisson‑based mobility predictions, SPRINT outperforms traditional social‑based routing in both real‑world and synthetic scenarios.

Abstract

Opportunistic networks are mobile networks that rely on the store-carry-and-forward paradigm, using contacts between nodes to opportunistically transfer data. For this reason, traditional routing mechanisms are no longer suitable. To increase the probability of successfull message delivery, we propose SPRINT, an opportunistic routing algorithm that introduces an additional routing criterion: online social information about nodes. Furthermore, previous results show that, for particular environments, contacts between devices in opportunistic networks are highly predictable. When users follow rare events-based mobility patterns, we show that human mobility can be approximated as a Poisson distribution. Based on this result, we add an additional prediction component into our routing algorithm. Our solution delivers better results compared to traditional social-based routing approaches, for different real-world and synthetic mobility scenarios.