Percolation thresholds on tree-based communities of wireless sensor networks

Many efficient deployments of large-scale wireless sensor networks based on the tree-based community rise into view recently. Sensor nodes are severely resource constrained, and lack sophisticated defense mechanisms to fight virus attacks. Cyber viruses spread through node populations over the networks, and a number of results about the prevalence have been derived in recent years by exploiting epidemic behaviors and the percolation processes on networks. A network model based on the Cayley tree is proposed to depict the underlying tree-based architectures of the network and the community. The percolation thresholds are calculated and analyzed in two cases. Due to random links in the communities, the sensor virus extends drastically on the network. The analysis and evaluation shows that the percolation threshold keeps decreasing with the increase of the shortcut probability. There is the smallest percolation threshold in a random network, where the virus easily attacks the network from one side to another. The conclusions can further our understanding of epidemic dynamics on tree-based communities of wireless sensor networks.