Abstract
An improved SIRS model considering communication radius and distributed density of nodes is proposed. The proposed model captures both the spatial and temporal dynamics of worms spread process. Using differential dynamical theories, we investigate dynamics of worm propagation to time in wireless sensor networks (WSNs). Reproductive number which determines global dynamics of worm propagation in WSNs is obtained. Equilibriums and their stabilities are also found. If reproductive number is less than one, the infected fraction of the sensor nodes disappears and if the reproduction number is greater than one, the infected fraction asymptotically stabilizes at the endemic equilibrium. Based on the reproduction number, we discuss the threshold of worm propagation about communication radius and distributed density of nodes in WSNs. Finally, numerical simulations verify the correctness of theoretical analysis.
Highlights
A sensor network is composed of hundreds or even thousands of sensor nodes that are allowed random deployment in inaccessible terrains or disaster relief operations [1]
Based on the reproduction number, we discuss the threshold of worm propagation about communication radius and distributed density of nodes in Wireless sensor networks (WSNs)
We have proposed an improved SIRS model for analyzing dynamics of worm propagation in WSNs
Summary
A sensor network is composed of hundreds or even thousands of sensor nodes that are allowed random deployment in inaccessible terrains or disaster relief operations [1]. Numerical simulations are performed to observe the effects of the network topology and energy consumption of nodes on worm spread in WSNs. the authors have not performed mathematical analyses based on this model. To better portrait the features of worm propagation in WSNs, in this paper, we study the attacking behavior of possible worms in WSNs by constructing an improved SIRS epidemic model In this model, the following three factors are considered: (i) energy consumptions of nodes; (ii) communication radius of nodes; and (iii) distributed density of nodes in WSNs. In this model, the following three factors are considered: (i) energy consumptions of nodes; (ii) communication radius of nodes; and (iii) distributed density of nodes in WSNs Based on this model, we analyze the stability of worm prevalence through finding the equilibriums of model.
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