TR-IIS-06-009    Fulltext


Analysis of Sampling-based Texture Synthesis as a Generalized EM Algorithm

Yung-Chih Chen, Paruvelli Sreedevi, Kuan-Ta Chen, and Ling-Jyh Chen

Abstract

Opportunistic network is a type of Delay Tolerant Networks (DTN) where network communication opportunities appear opportunistic, an end-to-end path between source and destination may have never existed, and disconnection and reconnection is common in the network. With numerous emerging opportunistic networking applications, strategies that can enable effective data communication in such challenged networking environments have become increasingly desirable. In particular, knowing fundamental properties of opportunistic networks will soon become the key for the proper design of opportunistic routing schemes and applications. In this study, we investigate opportunistic network scenarios based on two public network traces, namely UCSD and Dartmouth network traces. In this paper, our contributions are the following: First, we identify the censorship issue in network traces that usually leads to strongly skewed distribution of the measurements. Based on this knowledge, we then apply the Kaplan-Meier Estimator to calculate the survivorship of network measurements, which is used in designing our proposed censorship removal algorithm (CRA) that is used to recover censored data. Second, we perform a rich set of analysis illustrating that UCSD and Dartmouth network traces shows strong self-similarity, and can be modeled as such. Third, we pointed out the importance of these newly revealed characteristics in future development and evaluation of opportunistic networks.