Improving wireless sensor networks performance through epidemic model

Ojha, Rudra Pratap; Srivastava, Pramod K.; Sanyal, Goutam

doi:10.1080/00207217.2019.1570563

Cited by 26 publications

(21 citation statements)

References 28 publications

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“…It is an improvement of SIRS and SEIS models. In 2019, Ozha R.P, et al [ 18 ], proposed SEIRS model that beats the downsides of existing models as follows (Fig. 4 ).…”

Section: Epidemic Modelsmentioning

confidence: 99%

“…represents the probability to convert into dead node as Fig. 4 Represents schematic diagram of the system proposed by [18] the energy of nodes exhaust, = ( r 2 )∕L 2 represents the total number of neighbouring nodes that are lying in the sensing area of a sensor node and where denotes infectious contact rate, the transmission area of a sensor node is r 2 with sensing range r , L 2 represents the area in which the nodes are uniformly scattered, represents the probability that recovered node may become susceptible again, represents the rate at which recovery is provided to the susceptible node, represents rate at which exposed node falls in the infectious class, represents the rate of recovery is provided to the susceptible node. This proposed enhanced model incorporates a limited correspondence sweep and the related hub thickness.…”

Section: Epidemic Modelsmentioning

confidence: 99%

“…Without a doubt, controlling worm engendering is urgent to guarantee sustenance of the system. Along these lines, investigation of vindictive signs transmission and scientific demonstrating turns into a fundamental instrument [9][10][11][12][13][14][15][16][17][18][19][20]. The exhibition of remote sensor networks is dissected by fluctuating different parameters, for example, hub thickness, standard deviation, correspondence go, and so on.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A Review Article on Wireless Sensor Networks in View of E-epidemic Models

Srinivas

Madhusudanan²,

Murty

et al. 2021

Wireless Pers Commun

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Epidemic simulations have recently been used to model the dynamics of malicious codes in the network of wireless sensors. This is because of its open existence, which offers a simple target for malware attacks aimed at disrupting network operations or, worse, causing complete network failure. The Susceptible-Exposed-Infectious-Quarantined-Recovered–Susceptible with Vaccination compartments models like SIR-M, SEIRV, SEIQRV, SEIRS, SITR, SIR with delay are studied by various authors and some of such models that characterize worm dynamics in WSN. After a concise presentation of the wireless sensor network, some primary research consequences of e-pandemic models (of various researchers) are given and assessed. At that point the uses of wireless sensor network in the clinical wellbeing, agribusiness, and military, space and marine investigation are laid out. What's more, we break down the upside of wireless sensor network in these sectors. In this review article, we sum up the fundamental factors that influence the uses of wireless sensor networks in view of e-epidemic models and revived some epidemic models and also discussed some conceivable future works of different epidemic wireless sensor models.

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“…It is an improvement of SIRS and SEIS models. In 2019, Ozha R.P, et al [ 18 ], proposed SEIRS model that beats the downsides of existing models as follows (Fig. 4 ).…”

Section: Epidemic Modelsmentioning

confidence: 99%

Section: Epidemic Modelsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A Review Article on Wireless Sensor Networks in View of E-epidemic Models

Srinivas

Madhusudanan²,

Murty

et al. 2021

Wireless Pers Commun

View full text Add to dashboard Cite

show abstract

“…Ojha et at. [24] investigated a SEIRS model for the study of worm propagation in WSN. They investigated the conditions of system stability in worm-free and endemic equilibrium state.…”

Section: Related Workmentioning

confidence: 99%

Analyze The Effects of Quarantine And Vaccination on Malware Propagation in Wireless Sensor Network

Biswal¹,

Swain²

2019

IJITEE

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Malware (worm, virus, malicious signals, etc.) propagation in Wireless Sensor Network (WSN) is one of the important concern. The WSN becomes unstable due to presence of malicious signals. Vulnerability of WSN is very high because of the structural constraint of sensor nodes. The attackers target a sensor node of WSN for malware attack. A single infected node starts to spread the malware in the entire network through neighbouring nodes. Therefore, for controlling of malware propagation in WSN a mathematical model is developed. The developed model is based on epidemic theory. The developed model consist of five states such as Susceptible-Infectious-Quarantine-Vaccination-Dead (SIQVD). The quarantine is a method through which to cease the infection spread in WSN. And through vaccination eliminate the malware from the network. The combination of quarantine and vaccination technique improves the network stability. This technique prevents malware propagation in WSN. The basic reproduction number ( ) of the model is deduced. The stability of the network depends on the value of basic reproduction number. It is found that if the value of is less than one the network system exist in malware-fee state, otherwise in endemic state. The equilibrium points of the system is obtained. The effects of quarantine and vaccination has been analyzed on system performance. The theoretical findings are verified by simulation results. Attack Epidemic model Equilibrium point Malware propagation Security Wireless Sensor Network

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“…The authors also incorporated a vaccination strategy with temporary immunity to get threshold. Ojha et al (2019) include the effect of sensor node energy into classification criteria that has shown the improvement proposed by Singh et al (2018). Qu and Wang (2017)proposed a model considering node degree that can affect the infection rates.…”

Section: Introductionmentioning

confidence: 99%

SEIRS model with spatial correlation for analyzing dynamic of virus spreading in event-driven wireless sensor networks

Shakya

Ayane

Diba

et al. 2021

Int J Syst Assur Eng Manag

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In Event-driven wireless systems, mostly data transmission depends on events occurring in the sensor field. Most of the time, sensor nodes are silent or in sleep mode. When events occur in the sensor field, a single event can trigger many nodes for data transmission. In such a scenario, the nodes collect the correlated information due to the overlapped coverage area. Existing epidemiological designs do not consider the nodes' behavior to investigate infection dynamics for this scenario. In this paper, a susceptible-exposed-infectious-recovered-susceptible (SEIRS) is designed by considering the spatial correlation for analyzing the dynamics of the virus spreading in eventdriven wireless systems. Firstly, we show how strongly correlated nodes and less correlated nodes are formed in a WSN based on sensor coverage. The differential equations of SEIRS are then derived. An analysis on system stability is performed for finding the basic reproduction number R 0 . The value of R 0 gives important significance in terms of spatial correlation for analyzing virus spreading.Experiments are performed to validate the model using various parameters such as correlation, node density, the basic reproduction number. Comparisons with existing models show the effectiveness of the SEIRS model. Based on the analysis, it is observed that the virus spread control can be possible by reducing R 0 . It is also found that the threshold of virus propagation is strongly dependent on the spatial correlation between nodes in the network. The virus is the network persists at virus-free equilibrium when R 0 [ 1 with higher spatial correlation, whereas it becomes globally stable for R 0 \1.

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Improving wireless sensor networks performance through epidemic model

Cited by 26 publications

References 28 publications

A Review Article on Wireless Sensor Networks in View of E-epidemic Models

A Review Article on Wireless Sensor Networks in View of E-epidemic Models

Analyze The Effects of Quarantine And Vaccination on Malware Propagation in Wireless Sensor Network

SEIRS model with spatial correlation for analyzing dynamic of virus spreading in event-driven wireless sensor networks

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