Dynamical analysis of clustering-based wireless sensor networks

Abstract

In wireless sensor networks (WSNs), clustering-based algorithms have been proved to be effective techniques to save energy and improve fault tolerance. In this paper, a scale-free evolving model for clustering-based WSNs with exponential growth behavior is presented, in which the nodes arrive as a Poisson process with rate λ. This model takes into account four types of evolving events: selection of cluster-head nodes, preferential attachment of non cluster-head nodes, failure of non cluster-head nodes, transition of non cluster-head nodes. By using continuum theory, the theoretical computing and simulated results show that the size distribution of cluster in this model follows a power law. The scale-free properties revealed in this model display a tempting application foreground. Key words: Wireless sensor networks, continuum theory, scale-free, exponential growth behavior.