Cluster Based Routing Scheme for Heterogeneous Nodes in WSN–A Genetic Approach

Lakshmanan, L.; Jesudoss, A.; Ulagamuthalvi, V.

doi:10.1007/978-3-030-03146-6_117

Cited by 4 publications

(2 citation statements)

References 14 publications

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“…The suggested security solution is applied for an existing WSN system to evaluate its viability, and its functioning is nearly proven by successfully identifying any suspicious sensor nodes and unusual behavior in the base station with high accuracy and little overhead. In this work, the efficiency and overheads are assessed by integrating the suggested model into the base station application Through the use of the genetic K-means algorithm [29], the authors presented a conceptual framework for recognizing intrusions. The algorithm divides instances into a set quantity of clusters.…”

Section: Related Workmentioning

confidence: 99%

An Intelligent Prairie Dog Optimization (IPDO) and Deep Auto-Neural Network (DANN) based IDS for WSN Security

et al. 2023

ijcsm

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Wireless sensor networks (WSNs) are targets of intrusion, which seeks to make these networks less capable of performing their duties or even completely eradicate them. The Intrusion Detection System (IDS) is highly important for WSN, since it aids in the identification and detection of harmful attacks that impair the network's regular functionality. In order to strengthen the security of WSN, several machine learning and deep learning approaches are employed in the traditional works. However, its main drawbacks are computational burden, system complexity, poor network performance outcomes, and high false alarms. Therefore, the goal of this study is to develop an intelligent IDS framework for significantly enhancing WSN security through the use of deep learning model. Here, the min-max normalization and data discretization operations are carried out to produce the preprocessed dataset. Then, an Intelligent Prairie Dog Optimization (IPDO) algorithm is used to reduce the dimensionality of features by identifying the best optimal solution with a higher convergence rate. Moreover, a Deep Auto-Neural Network (DANN) based classification method is used to properly forecast the class of data with less false alarms and higher detection rate. For evaluation, a thorough analysis is conducted to evaluate the performance and detection results of the proposed IPDO-DANN model.

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Section: Related Workmentioning

confidence: 99%

An Intelligent Prairie Dog Optimization (IPDO) and Deep Auto-Neural Network (DANN) based IDS for WSN Security

et al. 2023

ijcsm

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“…The authors also investigated remote consultation in e-health [11]. Lakshmanan et al proposed a hybrid approach in combining PSO and ACO & BCO on routing protocol and applying the K-Means algorithm for clustering the nodes [12]. Furthermore, Manoj et al [13] discussed a congestion adaptive navigation for emergency situations.…”

Section: Sigwele Et Al Proposed An Information and Communication Tech...mentioning

confidence: 99%

A Novel Big Data Intelligence Analytics Framework for 5G-Enabled IoT Healthcare

Sabri¹,

Outzourhit²

2022

IJACSA

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Intelligent networking is a concept that enables 5G, the Internet of Things (IoT) and artificial intelligence (AI) to combine as a way to accelerate technological innovation and develop new revolutionary digital services. In the intelligent connectivity vision, the digital information gathered by the machines, devices and sensors which make up the IoT is analysed and contextualized. It is anticipated that the high availability of 5G and its inclusion of a large number of connections would help promote the production of wearable devices used to monitor the different biometric parameters of the wearer. Since these are AIbased health systems, the data obtained from these devices will be analysed in order to assess a patient's current health status. This paper presents a detailed design for the development of intelligent data analytics and mobile computer-assisted healthcare systems.The proposed advanced PoS consensus algorithm provides better performance than other existing algorithms.

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