Application of Machine Learning in Wireless Sensor Network

Vijayakumar, V.

doi:10.1007/978-3-319-32903-1_282-1

Cited by 6 publications

(5 citation statements)

References 22 publications

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“…The specification of the dataset can be found in section 4.1. Diverse applications of machine learning in the field of networking like traffic prediction and classification [28] , optimisation of Software Defined Networks (SDN) [29] , intrusion detection in computer networks [30] , efficiency in routing protocol [31] light the way for more efficient machine learning solutions in WSN. Flooding attack intrusion detection algorithm using k-NN [32] outperformed AODV routing protocol in terms of Packet Delivery Ratio (PDR), delay and routing load.…”

Section: Machine Learning For Wsn Routing Protocolmentioning

confidence: 99%

Id3 Based Periodic Sampled Classifier Algorithm for The Enhancement of Routing Protocol in Wireless Sensor Networks

2022

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Routing protocols for a Wireless Sensor Network (WSN) accomplishes the data dissemination between sensor nodes by preferring the next best node in the routing path. Supervised machine learning comes up with many strategies which can upgrade this next node selection for the range of applications. In this paper, ID3 based next node selection algorithm which uses entropy values for path resumption has been proposed. The simulation study of the algorithm showed that it performs well when compared to k-Nearest Neighbour and Naive Bayes algorithms in terms of evaluation metrics obtained from the confusion matrix and F1-score.

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Section: Machine Learning For Wsn Routing Protocolmentioning

confidence: 99%

Id3 Based Periodic Sampled Classifier Algorithm for The Enhancement of Routing Protocol in Wireless Sensor Networks

2022

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“…Moreover, when dealing with vital signs, the percentage of "dead nodes", which is traditionally used as a reliability metric, should be analyzed separately for each type of sensor nodes. Several authors [4,5] suggest ML algorithms to be applied to nodes clustering. When used in BWSN, ML tools may help:…”

Section: Haiterov Danylo Student Of the Faculty Of Physics Electronics And Computer Systemsmentioning

confidence: 99%

Ai-Powered Topology for Biomedical Sensor Networks

Botsva¹,

Berns²,

Kurnosov³

et al. 2021

Theoretical and Empirical Scientific Research: Concept and Trends Volume1

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“…WSNs are an essential component of the future networked world, also commonly known as the Internet of Things (IoT), which is going to be extensively utilized for control, connectivity, security, and general awareness of things large or small, close or far, wired or wireless [1,2,3]. WSNs are a type of network composed of small, low-power devices called sensors that are used to collect data from the environment.…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, security is a critical concern in the design and implementation of WSNs. A comprehensive security framework should be established to ensure the protection of the network against attacks [1][2][3][4][5][6][7][8][9].…”

Section: Introductionmentioning

confidence: 99%

“…Unauthorized access, tampering, and interception of data can lead to serious consequences, such as compromising privacy, disrupting critical operations, and causing physical damage. Therefore, the main objective of security in WSNs is to provide confidentiality, integrity, and availability of data and services [3,4,6,7]. Many attacks that threaten a network's operation could be launched against WSNs, causing harm to either communication stability or sensitive data [9,11].…”

Section: Introductionmentioning

confidence: 99%

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A Review of Security Challenges and Solutions in Wireless Sensor Networks

Hassan,

Madkour,

Nouh

2023

Journal of Al-Azhar University Engineering Sector

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Wireless Sensor Networks (WSNs) are a vital component of the Internet of Things (IoT) since it is a viable technology for a variety of real-time applications. This is mainly because WSNs are naturally distributed, low-cost, fault-tolerant, self-organizing, easily deployable, scalable, and function without a predefined infrastructure. Their applications include surveillance, smart power grids, traffic networks, telecommunications systems, smart cities, intelligent transportation, industrial quality control, and the military, to mention a few. However, the security of WSNs is a major concern due to their vulnerability to various types of attacks, including node compromise, eavesdropping, and denial-of-service. This paper covers the security issues in WSNs and discusses the security challenges faced by these networks and the existing solutions. It illustrates the possible attacks and explores the different security mechanisms that can be used to protect WSNs, including encryption, authentication, and key management. Trust and reputation management are presented being effective techniques for mitigating routing attacks, and the role of machine learning techniques in enhancing WSN security is outlined. The paper concludes by discussing future research directions in WSN security. The findings of this paper are valuable for researchers, network administrators, and policymakers who are involved in the design and implementation of secure WSNs.

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Application of Machine Learning in Wireless Sensor Network

Cited by 6 publications

References 22 publications

Id3 Based Periodic Sampled Classifier Algorithm for The Enhancement of Routing Protocol in Wireless Sensor Networks

Id3 Based Periodic Sampled Classifier Algorithm for The Enhancement of Routing Protocol in Wireless Sensor Networks

Ai-Powered Topology for Biomedical Sensor Networks

A Review of Security Challenges and Solutions in Wireless Sensor Networks

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