Mobile network analysis using probabilistic connectivity matrices

Researchers use random graph models to analyze complex networks that have no centralized control such as the Internet, peer-to-peer systems, and mobile ad hoc networks. These models explain phenomena like phase changes, clustering, and scaling. It is necessary to understand these phenomena when designing systems where exact node configurations cannot be known in advance. This paper presents a method for analyzing random graph models that combine discrete mathematics and probability theory. A graph connectivity matrix is constructed where each matrix element is the Bernoulli probability that an edge exists between two given nodes: We show how to construct these matrices for many graph classes, and use linear algebra to analyze the connectivity matrix. We present an application that uses this approach to analyze network cluster self-organization for sensor network security. We conclude by discussing the use of these concepts in mobile systems design.