Epidemics on tree-based communities of wireless sensor networks

Many efficient deployments of wireless sensor networks based on the community rise into view recently. The energy efficiency is considered as the key design problem of sensor nodes, which lacks sophisticated immune mechanisms for virus attacks. The epidemic on tree-based communities of wireless sensor networks is studied in this paper. Due to random links in the community, the sensor virus extends drastically on the network. Random links add the average number of neighbors to the relevant population and accelerate the virus propagation. The Cayley tree is proposed to analyze the underlying tree-based architecture of the network. The mathematical analysis of the virus prevalence on the abstract tree-based communities is presented in this paper. The analysis and evaluation shows that the number of infected nodes increases exponentially with the prevalence time as the infection spreads. The larger the infection probability is, the higher the speed of the prevalence will be. The research results can further our understanding of epidemics on the tree-based communities of wireless sensor networks.