Fuzzy logic for cluster head selection in wireless sensor network

Din, Wan Isni Sofiah Wan; Yahya, Saadiah; Jailani, Rozita; Taib, Mohd Nasir; Yassin, Ahmad Ihsan Mohd; Razali, Razulaimi

doi:10.1063/1.4965093

Cited by 3 publications

(3 citation statements)

References 20 publications

(14 reference statements)

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“…The authors in [41] proposed a fuzzy logic-based cluster head selection method in two tiers called multitier algorithm (MAP). The CH is selected by using fuzzy logic based on three parameters, which are residual energy, centrality, and communication cost.…”

Section: Multihop Data Transmissionmentioning

confidence: 99%

A Survey on Cluster Head Selection and Cluster Formation Methods in Wireless Sensor Networks

Raj

Ahmedy

Idris

et al. 2022

Wireless Communications and Mobile Computing

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In recent years, wireless sensor networks (WSNs) have been growing rapidly because of their ability to sense data, communicate wirelessly, and compute data efficiently. These networks contain small and low-powered sensor nodes that organize and configure themselves to carry out their functions. Even though WSNs are cheap, easy to deploy, flexible, and efficient, there are some challenges in terms of energy efficiency and network lifetime. Clustering in WSNs is the most reliable solution for the challenges, in which nodes are grouped into few clusters, and a cluster head (CH) is selected for data aggregation and data transfer to the base station (BS). However, there are still many challenges such as energy hole and isolated node problems that exist because of inefficient CH selection and cluster formation methods. In this work, we comprehensively reviewed various nonmetaheuristic and metaheuristic methods for CH selection and cluster formation that are used in networks from various environmental settings, for a better understanding of how the aforementioned problems are tackled by some authors. Moreover, the methods’ parameter settings, advantages, limitations, and future directions are presented with a brief performance summary of the approaches.

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Section: Multihop Data Transmissionmentioning

confidence: 99%

A Survey on Cluster Head Selection and Cluster Formation Methods in Wireless Sensor Networks

Raj

Ahmedy

Idris

et al. 2022

Wireless Communications and Mobile Computing

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“…Malicious URLs contains vulnerabilities and poses a significant threat to the computer. This malicious website threat has become an important rising issue [17,18,19]. Many studies have been proposed for analyzing and detecting malicious URLs.…”

Section: Related Workmentioning

confidence: 99%

“…In [18] proposed risk assessment to monitor the risk on URL by using the destination information when generating a short URL. By monitoring URL, any risky URL or risk over the threshold will be blocked to prevent malicious attack [19], especially from drive-by download through the short URL. In [20,22] applied machine learning for detecting malicious websites.…”

Section: Related Workmentioning

confidence: 99%

A malicious URLs detection system using optimization and machine learning classifiers

Lee

Heryanto

Razak

et al. 2020

IJEECS

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<span>The openness of the World Wide Web (Web) has become more exposed to cyber-attacks. An attacker performs the cyber-attacks on Web using malware Uniform Resource Locators (URLs) since it widely used by internet users. Therefore, a significant approach is required to detect malicious URLs and identify their nature attack. This study aims to assess the efficiency of the machine learning approach to detect and identify malicious URLs. In this study, we applied features optimization approaches by using a bio-inspired algorithm for selecting significant URL features which able to detect malicious URLs applications. By using machine learning approach with static analysis technique is used for detecting malicious URLs applications. Based on this combination as well as significant features, this paper shows promising results with higher detection accuracy. The bio-inspired algorithm: particle swarm optimization (PSO) is used to optimized URLs features. In detecting malicious URLs, it shows that naïve Bayes and support vector machine (SVM) are able to achieve high detection accuracy with rate value of 99%, using URL as a feature.</span>

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Federated Learning-based Misbehaviour detection on an emergency message dissemination scenario for the 6G-enabled Internet of Vehicles

Vinita¹,

Vetriselvi²

2023

Ad Hoc Networks

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Fuzzy logic for cluster head selection in wireless sensor network

Cited by 3 publications

References 20 publications

A Survey on Cluster Head Selection and Cluster Formation Methods in Wireless Sensor Networks

A Survey on Cluster Head Selection and Cluster Formation Methods in Wireless Sensor Networks

A malicious URLs detection system using optimization and machine learning classifiers

Federated Learning-based Misbehaviour detection on an emergency message dissemination scenario for the 6G-enabled Internet of Vehicles

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