Water cycle algorithm based optimized clustering protocol for wireless sensor network

Gambhir, Ankit; Payal, Ashish; Arya, Rajeev

doi:10.1080/09720502.2020.1731950

Cited by 8 publications

(2 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In comparison with the earlier techniques, the outcomes of the simulations demonstrated the superior performance of the proposed SCBRP. An application of Water Cycle Algorithm (WCA) on the LEACH protocol in WSNs (which is a clustering hierarchy protocol) was presented by Gambhir et al [13]. Observation of the flow of streams and rivers to the sea influenced the proposal of the WCA.…”

Section: Related Workmentioning

confidence: 99%

Cluster Head Selection and Multipath Routing Based Energy Efficient Wireless Sensor Network

Shanmugapriya¹,

Kousalya²

2023

Intelligent Automation &Amp; Soft Computing

View full text Add to dashboard Cite

The Wireless Sensor Network (WSN) is a network of Sensor Nodes (SN) which adopt radio signals for communication amongst themselves. There is an increase in the prominence of WSN adaptability to emerging applications like the Internet of Things (IoT) and Cyber-Physical Systems (CPS). Data security, detection of faults, management of energy, collection and distribution of data, network protocol, network coverage, mobility of nodes, and network heterogeneity are some of the issues confronted by WSNs. There is not much published information on issues related to node mobility and management of energy at the time of aggregation of data. Towards the goal of boosting the mobility-based WSNs' network performance and energy, data aggregation protocols such as the presently-used Mobility Low-Energy Adaptive Clustering Hierarchy (LEACH-M) and Energy Efficient Heterogeneous Clustered (EEHC) scheme have been examined in this work. A novel Artificial Bee Colony (ABC) algorithm is proposed in this work for effective election of CHs and multipath routing in WSNs so as to enable effective data transfer to the Base Station (BS) with least energy utilization. There is avoidance of the local optima problem at the time of solution space search in this proposed technique. Experimentations have been conducted on a large WSN network that has issues with mobility of nodes.

show abstract

Section: Related Workmentioning

confidence: 99%

Cluster Head Selection and Multipath Routing Based Energy Efficient Wireless Sensor Network

Shanmugapriya¹,

Kousalya²

2023

Intelligent Automation &Amp; Soft Computing

View full text Add to dashboard Cite

show abstract

“…However, the challenges faced by WSNs in these contexts are even greater, as the scale of the networks is much larger, and the data collected is more diverse and complex. Overall, WSNs have enormous potential in a range of applications, but their success depends on addressing the operational challenges faced by these networks such as limited energy, security especially malware attack, and the placement of nodes [6], [7] etc.…”

Section: Introductionmentioning

confidence: 99%

Generalized Defensive Modeling of Malware Propagation in WSNs Using Atangana–Baleanu– Caputo (ABC) Fractional Derivative

Srivastava¹,

Srivastava²,

Mishra³

et al. 2023

IEEE Access

View full text Add to dashboard Cite

The malware spreading in Wireless Sensor Network (WSN) has lately attracted the attention of many researchers as a hot problem in nonlinear systems. WSN is a collection of sensor nodes that communicate with each other wirelessly. These nodes are linked in a decentralised and distributed structure, allowing for efficient data collection and communication. Due to their decentralised architecture and limited resources, WSN is vulnerable to security risks, including malware attacks. Malware can attack sensor nodes, causing them to malfunction and consume more energy. These attacks can spread from one infected node to others in the network, making it essential to protect WSN against malware attacks. In this paper, we focus on the analysis of a novel fractional epidemiology model, specifically the fractional order SEIVR epidemic model in the sense of Caputo's fractional derivative of order 0< α≤1 with the goal of examining the efficacy of vaccination strategies and the heterogeneity of a scale-free network on epidemic spreading. First, using the next-generation technique and obtain the basic reproduction number of the proposed epidemic model, which is essential for determining both the locally asymptotically stable equilibrium point of the wormfree system and the unique existence of the endemic equilibrium point. To numerically solve the model, the Adam-Bashforth-Moulton predictor-corrector (ABM) method is applied. The fractional calculus enables us to deal directly with the ''memory effect'' of numerous phenomena, taking into account the system's dependence on previous stages. This method provides the results of a complex system. Additionally, research demonstrates that vaccine treatments are quite effective at preventing the spread of malware. The outcome of the study reveals that the applied ABM predictor-corrector method is computationally strong and effective to analyse fractional order dynamical systems in the SEIVR epidemic model for malware propagation in WSN. The results show that the order of the fractional derivative has a significant effect on the dynamic process. Also, from the result, it is obvious that the memory effect is zero for α = 1. When the fractional order α is decreased from 1, the memory effect appears, and its dynamics vary according to the time. This memory effect points out the difference between derivatives of fractional and integer orders. The theorems and their proofs are presented to validate the validity of the proposed model. To validate the proposed model, extensive theoretical study and computational analysis have also been applied.The associate editor coordinating the review of this manuscript and approving it for publication was Stefano Scanzio .

show abstract

Novel Hybridized Crow Optimization for Secure Data Transmission in Cyber Networks

Qureshi

Shandilya

2022

EAI/Springer Innovations in Communication and Computing

View full text Add to dashboard Cite

Water cycle algorithm based optimized clustering protocol for wireless sensor network

Cited by 8 publications

References 17 publications

Cluster Head Selection and Multipath Routing Based Energy Efficient Wireless Sensor Network

Cluster Head Selection and Multipath Routing Based Energy Efficient Wireless Sensor Network

Generalized Defensive Modeling of Malware Propagation in WSNs Using Atangana–Baleanu– Caputo (ABC) Fractional Derivative

Novel Hybridized Crow Optimization for Secure Data Transmission in Cyber Networks

Contact Info

Product

Resources

About