Epidemic models have been used in recent times to model the dynamics of malicious codes in wireless sensor network (WSN). This is due to its open nature which provides an easy target for malware attacks aimed at disrupting the activities of the network or at worse, causing total failure of the network. The Susceptible-Exposed-Infectious-Quarantined-Recovered–Susceptible with a Vaccination compartment (SEIQR-V) model by Mishra and Tyagi is one of such models that characterize worm dynamics in WSN. However, a critical analysis of this model and WSN epidemic literature shows that it is absent essential factors such as communication range and distribution density. Therefore, we modify the SEIQR-V model to include these factors and to generate better reproduction ratios for the introduction of an infectious sensor into a susceptible sensor population. The symbolic solutions of the equilibriums were derived for two topological expressions culled from WSN literature. A suitable numerical method was used to solve, simulate and validate the modified model. Simulation results show the effect of our modifications.

Sensor networks are appealing targets for malicious attacks that invade the network with the aim of depleting the confidentiality, availability and integrity (CIA) features/parameters of neighboring sensor nodes. This is due to its open communication, minimal resources and its deployment in un-trusted, unguarded and unfriendly terrains. To restrict illegitimate users or malicious attackers (such as worms) network analysts have suggested network access control (NAC). Specifically, we apply NAC to wireless sensor network epidemic models in order to investigate distribution density, transmission range and sensor area/field. Our analyses involved analytical expressions of two sensor fields gleaned from literature. Additionally, we explored the possibilities of infectivity of sensor nodes at the exposed class using the two expressions for sensor field topologies. We also derived the reproduction ratios and solutions at several equilibrium points for the models. It is our hope that that our work herein would impact sensor deployment decisions for organizations that utilize wireless sensor networks for meaningful daily activities.