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Abstract
There are two main focuses of this thesis. The first is UbiStore, a novel distributed and opportunistic backup architecture, where mobile devices backup their data over short-range, ad-hoc wireless links to other devices encountered as a result of user mobility. We base our design on the assumption that typical user mobility patterns will incur some repetitive encounters in the course of daily life (e.g. public transports, home/office), which are sustained on large time scales and can therefore facilitate the recovery of data in case of a lost data or device failure. We set out to design a data back-up architecture which exploits both this repetitiveness and diversity of human inter-contacts on the day to week timescales. In this thesis, we present the UbiStore design, architecture and adaptive back-up algorithms.
The second focus of this thesis is EMO, a mobility model to evaluate Delay Tolerant Networks (DTNs) and opportunistic systems, which focuses on simulating encounter events between mobile radios, rather than node locations as done in existing models and simulators. Our approach introduces a novel way to perform simulations of DTNs through an abstraction of radio propagation simulation. To design EMO, we extract and characterize the necessary parameters from experimental data and propose a method to generate synthetic node encounter traces based on this characterization. The output of the model is validated using hold-out cross-validation method. Our validation results indicate that EMO is able to maintain the statistical properties of experimental data over a wide range of time (simulation duration) and space (number of nodes) scales. We also demonstrate EMO's performance benefit, both in time (CPU) and space (memory) required for the simulation.