A New Energy-Aware Cluster Head Selection Algorithm for Wireless Sensor Networks

Tay, Muhammed; Şentürk, Arafat

doi:10.1007/s11277-021-08990-3

Cited by 36 publications

(17 citation statements)

References 22 publications

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“…To augment the performance of the traditionalist SSA model [36], BES [37] is combined with it to form BEA-SSA. Usually, self-improvement in typical optimization schemes [38][39][40][41][42][43][44] outcome in superior accuracy. The following rules are included in BEA-SSA.…”

Section: Proposed Bea-ssa Modelmentioning

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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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: Proposed Bea-ssa Modelmentioning

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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“…A cluster-based sensor network is a widespread method for optimizing sensor nodes' energy consumption to cope with the total performance of the network. 7 Clustering issues are handled through the inclusion of optimization methods that incorporate biologically inspired computing. In this context, the selection of CH s through swarm intelligence algorithms is determined to be highly suitable and predominant.…”

Section: Introductionmentioning

confidence: 99%

“…A cluster‐based sensor network is a widespread method for optimizing sensor nodes' energy consumption to cope with the total performance of the network 7 . Clustering issues are handled through the inclusion of optimization methods that incorporate biologically inspired computing.…”

Section: Introductionmentioning

confidence: 99%

Improved bat optimization algorithm and enhanced artificial bee colony‐based cluster routing scheme for extending network lifetime in wireless sensor networks

Janakiraman¹

2023

Int J Communication

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Summary In this paper, improved bat and enhanced artificial bee colony optimization algorithm‐based cluster routing (IBEABCCR) scheme is proposed for optimal cluster head (CH) selection with the merits of global diversity and improved convergence rate. It is proposed for achieving optimal CH selection by balancing the tradeoff between the phases of exploration and exploitation. It specifically targeted on the formulation of an ideal CH selection scheme using improved bat optimization algorithm (IBOA) for minimizing the energy depletion rate. It also focuses on the design of an enhanced artificial bee colony (EABC)‐based sink node mobility scheme for determining the optimal points of deployment over which sink nodes can be moved to achieve better delivery of packets from CH to sink node. This CH selection and sink node mobility schemes are contributed for extending the network lifespan using the fitness function, which adopted the factors of node centrality, node degree, distance amid CH and base station (BS), distance among sensor nodes, and residual energy during CH selection process. The simulation experiments were performed using MATLAB version 2018, which confirmed that the number of alive nodes realized in the network is enhanced by 39.21% with the location of BS positioned at (100, 100). The number of rounds (network lifetime) is enhanced by 23.84% with different BS locations in the network. Furthermore, the packets received at the BS are also realized to be enhanced by 26.32% on an average in contrast to the baseline CH schemes used for investigation.

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“…As a result, new studies are constantly being published in efforts to improve this technology. It is clear that WSNs face several issues and challenges that must be addressed [ 6 ]. Therefore, achieving solutions to these problems is an essential part of the research.…”

Section: Introductionmentioning

confidence: 99%

A Lightweight Authentication MAC Protocol for CR-WSNs

Aloufi

Alhakami

2023

Sensors

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Cognitive radio (CR) has emerged as one of the most investigated techniques in wireless networks. Research is ongoing in terms of this technology and its potential use. This technology relies on making full use of the unused spectrum to solve the problem of the spectrum shortage in wireless networks based on the excessive demand for spectrum use. While the wireless network technology node’s range of applications in various sectors may have security drawbacks and issues leading to deteriorating the network, combining it with CR technology might enhance the network performance and improve its security. In order to enhance the performance of the wireless sensor networks (WSNs), a lightweight authentication medium access control (MAC) protocol for CR-WSNs that is highly compatible with current WSNs is proposed. Burrows–Abadi–Needham (BAN) logic is used to prove that the proposed protocol achieves secure and mutual authentication. The automated verification of internet security protocols and applications (AVISPA) simulation is used to simulate the system security of the proposed protocol and to provide formal verification. The result clearly shows that the proposed protocol is SAFE under the on-the-fly model-checker (OFMC) backend, which means the proposed protocol is immune to passive and active attacks such as man-in-the-middle (MITM) attacks and replay attacks. The performance of the proposed protocol is evaluated and compared with related protocols in terms of the computational cost, which is 0.01184 s. The proposed protocol provides higher security, which makes it more suitable for the CR-WSN environment and ensures its resistance against different types of attacks.

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A New Energy-Aware Cluster Head Selection Algorithm for Wireless Sensor Networks

Cited by 36 publications

References 22 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

Improved bat optimization algorithm and enhanced artificial bee colony‐based cluster routing scheme for extending network lifetime in wireless sensor networks

A Lightweight Authentication MAC Protocol for CR-WSNs

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