| Summary: | Disruption-Tolerant Networks (DTNs) are the networks comprised of a set of wireless nodes, and they experience unstable connectivity and frequent connection disruption because of the limitations of radio range, power, network density, device failure, and noise. DTNs are characterized by their lack of infrastructure, device limitation, and intermittent connectivity. Such characteristics make conventional wireless network routing protocols fail, as they are designed with the assumption the network stays connected. Thus, routing in DTNs becomes a challenging problem, due to the temporal scheduling element in a dynamic topology. One of the solutions is prediction-based, where nodes mobility is estimated with a history of observations. Then, the decision of forwarding messages during data delivery can be made with that predicted information. Current prediction-based routing protocols can be divided into two sub-categories in terms of that whether they are probability related: probabilistic and non-probabilistic. This dissertation focuses on the probabilistic prediction-based (PPB) routing schemes in DTNs. We find that most of these protocols are designed for a specified topology or scenario. So almost every protocol has some drawbacks when applied to a different scenario. Because every scenario has its own particular features, there could hardly exist a universal protocol which can suit all of the DTN scenarios. Based on the above motivation, we investigate and divide the current DTNs scenarios into three categories: Voronoi-based, landmark-based, and random moving DTNs. For each category, we design and implement a corresponding PPB routing protocol for either basic routing or a specified application with considering its unique features. === Specifically, we introduce a Predict and Relay routing protocol for Voronoi-based DTNs, present a single-copy and a multi-copy PPB routing protocol for landmark-based DTNs, and propose DRIP, a dynamic Voronoi region-based publish/subscribe protocol, to adapt publish/subscribe systems to random moving DTNs. New concepts, approaches, and algorithms are introduced during our work. === by Quan Yuan. === Vita. === Thesis (Ph.D.)--Florida Atlantic University, 2009. === Includes bibliography. === Electronic reproduction. Boca Raton, Fla., 2009. Mode of access: World Wide Web.
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