Taylor kernel fuzzy C-means clustering algorithm for trust and energy-aware cluster head selection in wireless sensor networks

Augustine, Susan; Ananth, John Patrick

doi:10.1007/s11276-020-02352-w

Cited by 31 publications

(22 citation statements)

References 38 publications

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“…In 2020, Augustine et al [23] offered an improved system for CHS using Taylor Kernel Fuzzy C-Means (KFCM) customized from the KFCM method. The modeled system designated the CH via the acceptability factor, which was reviewed by energy, distance, and trust.…”

Section: Related Workmentioning

confidence: 99%

“…The MOFPL used in [22] offered high network energy with lesser simulation time, but resource management is not analyzed here. In addition, the Taylor KFCM used in [23] provided high throughput with negligible delay; nevertheless, there was no deliberation on real-time experiments. Likewise, the FF-based model used in [24] improved EE efficiently with enhanced EE.…”

Section: Related Workmentioning

confidence: 99%

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Optimal Cluster Head Selection in WSN with Convolutional Neural Network-Based Energy Level Prediction

Gurumoorthy¹,

Subhash

Prado

et al. 2022

Sensors

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Currently, analysts in a variety of nations have developed various WSN clustering protocols. The major characteristic is the Low Energy Adaptive Clustering Hierarchy (LEACH), which attained the objective of energy balance by sporadically varying the Cluster Heads (CHs) in the region. Nevertheless, because it implements an arbitrary number system, the appropriateness of CH is complete with suspicions. In this paper, an optimal cluster head selection (CHS) model is developed regarding secure and energy-aware routing in the Wireless Sensor Network (WSN). Here, optimal CH is preferred based on distance, energy, security (risk probability), delay, trust evaluation (direct and indirect trust), and Received Signal Strength Indicator (RSSI). Here, the energy level is predicted using an improved Deep Convolutional Neural Network (DCNN). To choose the finest CH in WSN, Bald Eagle Assisted SSA (BEA-SSA) is employed in this work. Finally, the results authenticate the effectiveness of BEA-SSA linked to trust, RSSI, security, etc. The Packet Delivery Ratio (PDR) for 100 nodes is 0.98 at 500 rounds, which is high when compared to Grey Wolf Optimization (GWO), Multi-Objective Fractional Particle Lion Algorithm (MOFPL), Sparrow Search Algorithm (SSA), Bald Eagle Search optimization (BES), Rider Optimization (ROA), Hunger Games Search (HGS), Shark Smell Optimization (SSO), Rider-Cat Swarm Optimization (RCSO), and Firefly Cyclic Randomization (FCR) methods.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Optimal Cluster Head Selection in WSN with Convolutional Neural Network-Based Energy Level Prediction

Gurumoorthy¹,

Subhash

Prado

et al. 2022

Sensors

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“…For trust and energy aware cluster head selection in sensor networks, a taylor kernel fuzzy C-means clustering (TKFCC) algorithm is proposed in [22]. Where clusters are formed based on a taylor kernel fuzzy C-means algorithm, and cluster heads are selected according to the fitness constraints of minimal distance, maximal trust, and maximal energy.…”

Section: B Cluster Based Trust Evaluationmentioning

confidence: 99%

A Secure Clustering Protocol With Fuzzy Trust Evaluation and Outlier Detection for Industrial Wireless Sensor Networks

Yang

et al. 2021

IEEE Trans. Ind. Inf.

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Security is one of the major concerns in Industrial Wireless Sensor Networks (IWSNs). To assure the security in clustered IWSNs, this paper presents a secure clustering protocol with fuzzy trust evaluation and outlier detection (SCFTO). Firstly, to deal with the transmission uncertainty in an open wireless medium, an interval type-2 fuzzy logic controller is adopted to estimate the trusts. And then a density based outlier detection mechanism is introduced to acquire an adaptive trust threshold used to isolate the malicious nodes from being cluster heads. Finally, a fuzzy based cluster heads election method is proposed to achieve a balance between energy saving and security assurance, so that a normal sensor node with more residual energy or less confidence on other nodes has higher probability to be the cluster head. Extensive experiments verify that our secure clustering protocol can effectively defend the network against attacks from internal malicious or compromised nodes.

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“…In 2020, Augustine and Ananth [27] have presented an enhanced framework for CHS based on Taylor KFCM that was modified from the KFCM approach in the Taylor series.…”

Section: A Related Workmentioning

confidence: 99%

“…Numerous methods have been focused on energy-aware CHS models in WSN. But still, the existing models like FF-PUD [24], BOA + ACO [25], MOFPL [26], Taylor KFCM model [27], FF [28] have some common problems like high convergence, local search issues in FF, high-cost efficiency, there is a need of standard optimizations and need consideration on constraints like security and trust.…”

Section: B Problem Formulationmentioning

confidence: 99%

Hybrid Metaheuristic Aided Energy Efficient Cluster Head Selection in Wireless Sensor Network

Alghamdi¹

2021

IJACSA

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Clustering is one of the significant techniques for expanding the lifetime of networks in wireless sensor networks (WSNs). It entails combining of sensor nodes (SNs) into clusters and electing cluster heads (CHs) for each and every cluster. CH collects the information from particular cluster nodes and passes the cumulative data to the base station (BS). However, the most important requirement in WSN is to choose a suitable CH with an increased network life span. This work introduces a new CHS model in WSN. The optimal CH is elected by a new hybridized model termed as "Lion Updated Dragonfly Algorithm (LU-DA) that hybrid the concepts of Dragonfly Algorithm (DA) and Lion Algorithm (LA)". Moreover, the optimal selection of CH is done depending upon constraints like "energy, delay, distance, security (risk) and trust (direct and indirect trust)". This optimal CH ensures the network lifetime enhancement. At last, the superiority of the developed approach is proved on varied measures like energy and alive node analysis. Accordingly, the proposed model has accomplished higher energy of 0.55 at 1 st round, whereas at the 2000 th round, the normalized energy value has been dropped to 0.1.

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Taylor kernel fuzzy C-means clustering algorithm for trust and energy-aware cluster head selection in wireless sensor networks

Cited by 31 publications

References 38 publications

Optimal Cluster Head Selection in WSN with Convolutional Neural Network-Based Energy Level Prediction

Optimal Cluster Head Selection in WSN with Convolutional Neural Network-Based Energy Level Prediction

A Secure Clustering Protocol With Fuzzy Trust Evaluation and Outlier Detection for Industrial Wireless Sensor Networks

Hybrid Metaheuristic Aided Energy Efficient Cluster Head Selection in Wireless Sensor Network

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