Title: Application Platforms, Routing Algorithms and Mobility Behavior in Mobile Disruption-Tolerant Networks
Description: Mobile disruption-tolerant networks (DTNs), experience frequent and long duration partitions due to the low density of mobile nodes. In these networks, traditional networking models relying on end-to-end communication cease to work. The topological characteristics of mobile DTNs impose unique challenges for the design and validation of routing protocols and applications. We investigate challenges of mobile DTNs from three different viewpoints: the application layer, a routing perspective, and by studying mobility patterns. In the application layer, we have built 7DS (7th Degree of Separation) as a modular platform to develop mobile disruption-tolerant applications. 7DS offers a class of disruption-tolerant applications to exchange data with other mobile users in the mobile DTN or with the global Internet. In the routing layer, we have designed and implemented PEEP as an interest-aware and energy efficient routing protocol which automatically extracts individual interests of mobile users and estimates the global popularity of data items throughout the network.

PEEP considers mobile users' interests and global popularity of data items in its routing decisions to route data toward the community of mobile users who are interested in that data content. Mobility of mobile users impacts the conditions in which routing protocols for mobile DTNs must operate and types of applications that could be provided for mobile networks in general. The current synthetic mobility models do not reflect real-world mobile users' behavior. Trace-based mobility models, also, are based on traces that either represent a specific population of mobile users or do not have enough granularities in representing mobility of mobile users for example cell tower traces. We use Sense Networks' GPS traces that are being collected by monitoring a broad spectrum of mobile users. Using these traces, we employ a Markovian approach to extract inherent patterns in human mobility. We design and implement a new routing algorithm for mobile DTNs based on our Markovian analysis of the human mobility.

We explore how the knowledge of the mobility improves the performance of our Markov based routing algorithm. We show that that our Markov based routing algorithm increases the rate of data delivery to popular destinations with consuming less energy than legacy algorithms.