Ravine streams: Persistent data streams in disruptive sensor networks

Opportunistic network coding has been developed and applied in disruptive networks to provide optimal data delivery. Though network coding system utilizes coding opportunities among multiple paths, its application in data collection suffers from a disconnected sink node and the limited storage space available for data cache. The state-of-the-art approach has studied preserving data persistence as an optimization problem under storage and energy constraints, without considering disruptive network dynamics during data redistribution. In this paper, we propose Ravine Streams (RS) to maximize data preservation under the constraints of limited storage and probabilistic node failure throughout data redistribution. Our RS approach leverages adaptive power control to achieve ensured storage of each redistribution data. Meanwhile, in the course of data redistribution, distributed coding-based rebroadcast strategy not only reduces the data duplication, but also improves the statistical property of symbol randomness. We show that the performance of preserving data persistence of proposed RS is approximately bounded by the optimal solutions. The experimental evaluations demonstrate that RS increases data delivery ratio, consumes even less communication energy with only comparable storage cost, when compared with existing data preserving algorithms.