Differential Games of Rechargeable Wireless Sensor Networks against Malicious Programs Based on SILRD Propagation Model

Liu, Guiyun; Peng, Baihao; Zhong, Xiaojing; Lan, Xuejing

doi:10.1155/2020/5686413

Cited by 11 publications

(7 citation statements)

References 35 publications

(36 reference statements)

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“…Liu et al [19] considered the low-energy factor to construct a model of WRSNs, and the stability of the model was proved. According to Liu et al [19][20][21][22], the system is a good combination of an epidemic dynamic model and wireless sensor network. Thus, the system skillfully blends the information of the WRSNs with biological characteristics.…”

Section: Related Workmentioning

confidence: 99%

Dynamics Analysis of a Wireless Rechargeable Sensor Network for Virus Mutation Spreading

Liu

Peng

Liang

et al. 2021

Entropy

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Virus spreading problems in wireless rechargeable sensor networks (WSNs) are becoming a hot topic, and the problem has been studied and discussed in recent years. Many epidemic spreading models have been introduced for revealing how a virus spreads and how a virus is suppressed. However, most of them assumed the sensors are not rechargeable sensors. In addition, most of existing works do not consider virus mutation problems. This paper proposes a novel epidemic model, including susceptible, infected, variant, low-energy and dead states, which considers the rechargeable sensors and the virus mutation factor. The stability of the proposed model is first analyzed by adopting the characteristic equation and constructing Lyapunov functions methods. Then, an optimal control problem is formulated to control the virus spread and decrease the cost of the networks by applying Pontryagin’s maximum principle. Finally, all of the theoretical results are confirmed by numerical simulation.

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Section: Related Workmentioning

confidence: 99%

Dynamics Analysis of a Wireless Rechargeable Sensor Network for Virus Mutation Spreading

Liu

Peng

Liang

et al. 2021

Entropy

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“…However, we know that the time delay of the charging process in WRSNs has not been investigated previously. This paper mainly provides a kind of influence on time delay based on the SIS model combined with low-energy nodes (L) [23][24][25]. Thus, a novel epidemic model (1) of virus spreading in WRSNs is developed.…”

Section: Time Delay Of the Incubationmentioning

confidence: 99%

Analysis of Time-Delay Epidemic Model in Rechargeable Wireless Sensor Networks

Liu

Liang

et al. 2021

Mathematics

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With the development of wireless rechargeable sensor networks (WRSNs), many scholars began to attach attention to network security under the spread of viruses. This paper mainly studies a novel low-energy-status-based model SISL (Susceptible, Infected, Susceptible, Low-Energy). The conversion process from low-energy nodes to susceptible nodes is called charging. It is noted that the time delay of the charging process in WRSNs should be considered. However, the charging process and its time delay have not been investigated in traditional epidemic models in WRSNs. Thus, the model SISL is proposed. The basic reproduction number, the disease-free equilibrium point, and the endemic equilibrium point are discussed here. Meanwhile, local stability and global stability of the disease-free equilibrium point and the endemic equilibrium point are analyzed. The addition of the time-delay term needs to be analyzed to determine whether it affects the stability. The intervention treatment strategy under the optimal control is obtained through the establishment of the Hamiltonian function and the application of the Pontryagin principle. Finally, the theoretical results are verified by simulations.

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“…e first method is to use energy harvesters to capture solar energy and convert light energy into electrical energy to 8 Complexity supplement the energy of sensor nodes. e second method is to deploy UAVs to charge sensor nodes [21].…”

Section: Comparison With General Epidemic Modelmentioning

confidence: 99%

“…In this part, variations in the quantity of five node states, control variables and the quantity of high-and low-energy nodes, and the overall costs will be applied as indicators to explain the impact of charging. ree scenarios will be discussed here, namely, SILRD model with solar energy harvesters, SILRD model with UAVs [21], and SILRD model without charging capability. In order to facilitate the analysis of the impact of charging, this part will ignore the impact of system input but will consider multiple types of malicious programs' attacks and nonlinear infection rates.…”

Section: Effect Of Charging On Silrd Modelmentioning

confidence: 99%

“…In this paper, sensor nodes have been divided into five states based on the remaining energy, and the energy-harvesting technology is introduced to supplement the energy of sensor nodes to extend the lifespan of the EHWSNs. On the basis of [21], this paper changes the charging method to solar charging and constructs a new model, namely, Susceptible-Infected-Low (energy)-Recovered-Dead (SILRD) with solar energy harvesters. At the same time, considering networks input and multitypes of malicious programs attacks with nonlinear infection rates, a novel model named Λ-Susceptible-Infected-Low (energy)-Recovered-Dead (ΛSILRD) is proposed.…”

Section: Introductionmentioning

confidence: 99%

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Attack-Defense Game between Malicious Programs and Energy-Harvesting Wireless Sensor Networks Based on Epidemic Modeling

et al. 2020

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As energy-harvesting wireless sensor networks (EHWSNs) are increasingly integrated with all walks of life, their security problems have gradually become hot issues. As an attack means, malicious programs often attack sensor nodes in critical locations in the networks to cause paralysis and information leakage of the networks, resulting in security risks. Based on the previous works and the introduction of solar charging, we proposed a novel model, namely, Susceptible-Infected-Low (energy)-Recovered-Dead (SILRD) with solar energy harvesters. Meanwhile, this paper takes Logistic Growth as the drop rate of sensor nodes and the infection rate of multitype malicious programs under nonlinear condition into consideration. Finally, an Λ-Susceptible-Infected-Low (energy)-Recovered-Dead (ΛSILRD) model is proposed. Based on the Pontryagin Maximum Principle, this paper proposes the optimal strategies based on the SILRD with solar energy harvesters and the ΛSILRD. The effectiveness of SILRD with solar energy harvesters was demonstrated by comparison with the general epidemic model. At the same time, by analyzing different charging strategies, we conclude that solar charging is highly efficient. Moreover, we further analyze the influence of controllable and uncontrollable input and various node degrees on ΛSILRD model.

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Differential Games of Rechargeable Wireless Sensor Networks against Malicious Programs Based on SILRD Propagation Model

Cited by 11 publications

References 35 publications

Dynamics Analysis of a Wireless Rechargeable Sensor Network for Virus Mutation Spreading

Dynamics Analysis of a Wireless Rechargeable Sensor Network for Virus Mutation Spreading

Analysis of Time-Delay Epidemic Model in Rechargeable Wireless Sensor Networks

Attack-Defense Game between Malicious Programs and Energy-Harvesting Wireless Sensor Networks Based on Epidemic Modeling

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