Sensor network plays a significant role in determining the performance of network inference tasks. A wireless sensor network with a large number of sensor nodes can be used as an effective tool for gathering data in various situations. One of the major issues in WSN is developing an efficient protocol which has a significant impact on the convergence of the network. Parameter estimation is one of the most important applications of sensor network. In order to model such large and complex networks for estimation, efficient strategies and algorithms which take less time to converge are being developed. To deal with this challenge, an approach of having multilayer network structure to estimate parameter and reach convergence in less time is estimated by comparing it with known gossip distributed algorithm. Approached Multicast multilayer algorithm on a network structure of Gaussian mixture model with two components to estimate parameters were compared and simulated with gossip algorithm. Both the algorithms were compared based on the number of iterations the algorithms took to reach convergence by using Expectation Maximization Algorithm.Finally a series of theoretical and practical results that explicitly showed that Multicast works better than gossip in large and complex networks for estimation in consensus building strategies.

abstract: Analysis of political texts, which contains a huge amount of personal political opinions, sentiments, and emotions towards powerful individuals, leaders, organizations, and a large number of people, is an interesting task, which can lead to discover interesting interactions between the political parties and people. Recently, political blogosphere plays an increasingly important role in politics, as a forum for debating political issues. Most of the political weblogs are biased towards their political parties, and they generally express their sentiments towards their issues (i.e. leaders, topics etc.,) and also towards issues of the opposing parties. In this thesis, I have modeled the above interactions/debate as a sentimental bi-partite graph, a bi-partite graph with Blogs forming vertices of a disjoint set, and the issues (i.e. leaders, topics etc.,) forming the other disjoint set,and the edges between the two sets representing the sentiment of the blogs towards the issues. I have used American Political blog data to model the sentimental bi- partite graph, in particular, a set of popular political liberal and conservative blogs that have clearly declared positions. These blogs contain discussion about social, political, economic issues and related key individuals in their conservative/liberal view. To be more focused and more polarized, 22 most popular liberal/conservative blogs of a particular time period, May 2008 - October 2008(because of high intensity of debate and discussions), just before the presidential elections, was considered, involving around 23,800 articles. This thesis involves solving the questions: a) which is the most liberal/conservative blogs on the web? b) Who is on which side of debate and what are the issues? c) Who are the important leaders? d) How do you model the relationship between the participants of the debate and the underlying issues? / Dissertation/Thesis / M.S. Computer Science 2012