Epidemic spreading in wireless sensor networks with node sleep scheduling

TL;DR

A novel epidemic spreading model for WSNs is proposed, integrating the susceptible-infected-susceptible (SIS) epidemicSpread model and node probabilistic sleep scheduling--a critical mechanism for optimizing energy efficiency.

Abstract

Wireless Sensor Networks (WSNs) have become widely used in various fields like environmental monitoring, smart agriculture, and health care. However, their extensive usage also introduces significant vulnerabilities to cyber viruses. Addressing this security issue in WSNs is very challenging due to their inherent limitations in energy and bandwidth to implement real-time security measures. To tackle the virus issue, it is crucial to first understand how it spreads in WSNs. In this brief, we propose a novel epidemic spreading model for WSNs, integrating the susceptible-infected-susceptible (SIS) epidemic spreading model and node probabilistic sleep scheduling--a critical mechanism for optimizing energy efficiency. Using the microscopic Markov chain (MMC) method, we derive the spreading equations and epidemic threshold of our model. We conduct numerical simulations to validate the theoretical results and investigate the impact of key factors on epidemic spreading in WSNs. Notably, we discover that the epidemic threshold is directly proportional to the ratio of node sleeping and node activation probabilities.

Figures (5)

• Figure 1: The state transition diagram of the proposed spreading model, where US, UI, AS and AI represent the sensor nodes are in inactive susceptible state, inactive infected state, active susceptible state and active infected state, respectively. Arrow lines indicate the transition directions between different states.
• Figure 2: (Color online) The temporal evolution of the fractions of nodes of different states. The parameter settings are $\beta=0.5$, $\gamma = 0.3$, $u=0.3$, and $v = 0.7$. The solid and marked lines are the theoretical and simulation results, respectively. All results are obtained by averaging over 50 independent runs.
• Figure 3: (Color online) (a): The fraction of nodes of each state $\rho$ vs. infection rate $\beta$. The black arrow line points to the epidemic threshold $\beta_c$. (b): The stacked bar chart of the fraction of nodes of each state $\rho$ for different values of $\beta$. The parameter settings are $\gamma = 0.5$, $u = 0.3$ and $v = 0.7$. All the results are obtained by averaging over 50 independent runs.
• Figure 4: (Color online) The epidemic threshold $\beta_c$ vs. infection recovery rate $\gamma$ for different values of $u$ and $v$. The lines and symbols are the theoretical and simulation results, respectively. All the results are obtained by averaging over 50 independent runs.
• Figure 5: (Color online) The epidemic threshold $\beta_c$ vs. the ratio $u/v$ with $u =\{0.2:0.1:0.7\}$ and $v =\{0.2:0.1:0.7\}$. The parameter setting is $\gamma = 0.5$. The lines and symbols are the theoretical and simulation results, respectively. All results are obtained by averaging over 50 independent runs.

Theorems & Definitions (2)

• Theorem 1
• proof