Optimized cluster head selection using krill herd algorithm for wireless sensor network

Karthick, P.; Palanisamy, C.

doi:10.1080/00051144.2019.1637174

Cited by 57 publications

(30 citation statements)

References 22 publications

(24 reference statements)

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“…Karthick and Palanisamy [44] proposed a CH selection using krill herd algorithm to prolong the WSN network lifetime. The major limitations are network lifetime, data redundancy in aggregation and network coverage issues.…”

Section: Related Workmentioning

confidence: 99%

T2FL-PSO: Type-2 Fuzzy Logic-Based Particle Swarm Optimization Algorithm Used to Maximize the Lifetime of Internet of Things

Sankar¹,

Ramasubbareddy

Balasubramaniyam³

et al. 2021

IEEE Access

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In recent years, the Internet of Things (IoT) has evolved as a research field that transforms human lifestyle from traditional to sophisticated. In IoT, the network plays a crucial role in collecting data from sensors and moving to the sink. Increasing the network lifetime is a challenging task in IoT, which is connected to devices that are limited by resource. Clustering is one of the effective methods of increasing the network lifetime. However, improper cluster head (CH) selection easily drains the energy early in network nodes. With the aim to overcome the issue, this paper proposes the Type-2 Fuzzy Logic-based Particle Swarm Optimization (T2FL-PSO) algorithm to select the optimal CH to extend the network lifetime. The T2FL is highly useful in providing the accurate solution in uncertain network environments. Hence, T2FL is applied on the network parameters, residual energy, and the distance between sensor node and base station to determine the fitness value. Later, virtual clusters are formed on the basis of distance between sensor node and CH and between node and base station. To validate the performance of the proposed T2FL-PSO algorithm, extensive simulations are carried out using MATLAB 2019a. Furthermore, the proposed T2FL-PSO algorithm is compared with Particle Swarm Optimization Clustering (PSO-C) and Particle Swarm Optimization Wang Zhang (PSO-WZ). The result confirms that the proposed T2FL-PSO increases the network lifetime by 10%-15% and the packet transmission ratio by 10%. Compared with similar algorithms, the proposed T2FL-PSO also causes a higher increase of network lifetime.

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Section: Related Workmentioning

confidence: 99%

T2FL-PSO: Type-2 Fuzzy Logic-Based Particle Swarm Optimization Algorithm Used to Maximize the Lifetime of Internet of Things

Sankar¹,

Ramasubbareddy

Balasubramaniyam³

et al. 2021

IEEE Access

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“…Reddy and Babu [29] implemented own performance concerning whale optimization instead of CH section accessible Internet of Things. Various streamlined methods have been proposed in an attempt to discover the most energy-efficient variation of CH [30,31]. As a result, there is a need for a metaheuristic approach that can satisfy critical parameters in the optimization phase [32,33].…”

Section: Related Workmentioning

confidence: 99%

An Improved Bat Algorithm for Unequal Clustering in Heterogeneous Wireless Sensor Networks

Sahoo

Amgoth

2021

SN COMPUT. SCI.

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Clustering is one of the imperative solutions to the problem of energy unbalancing prevailing in the network. The major issues of wireless sensor network with unequal clustering and multi-hop communication are the energy stability and network longevity. In this paper, we consider the improved Bat optimization algorithm to improve the unequal clustering techniques by forming groups before selecting Cluster Heads. In terms of saving energy, knowing the positions of sink nodes in WSNs plays a vital role. Bat algorithm (BA), optimization of particle swarm, differential evolution, and whale optimization algorithm is now becoming efficient clustering methods as per the metaheuristic approach. Evaluate the life span of the entire network; this paper proposes an improved BA. The BA's primary goal of unequal clustering is to reduce energy consumption and extend the lives of the WSNs. An objective function has been formulated to reduce energy consumption and increase the network's lifespan to achieve these goals. Compared with three existing optimization methods, the experimental results showed that the proposed improved BA achieved better efficiency in reducing total power consumption: whale optimization for topology control, and improved particle swarm optimization algorithm.

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“…In Edla et al [13], a clustering method was presented using shuffled complex evolution of PSO (SCE-PSO), which is an effectual FF using average cluster distance, CHs load, and number of loaded CHs in a system. The approach in [14] focused on CH selection using GA and KH methods for WSN. The key objective of this model was to enhance the lifespan of WSN routing with the BS node by designing an effectual routing approach on the basis of hybridized metaheuristic optimization models [15].…”

Section: Related Workmentioning

confidence: 99%

Energy Efficient Cluster based Routing Protocol with Secure IDS for IoT Assisted Heterogeneous WSN

Alkhliwi¹

2020

IJACSA

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Currently, wireless sensor networks (WSNs) and the Internet of Things (IoT) have become useful in a wide range of applications. The nodes in IoT assisted WSN commonly operate on restricted battery units, meaning energy efficiency is a major design issue. Clustering and route selection processes are commonly utilized energy-efficient techniques for WSN. Although several cluster-based routing approaches are available for homogeneous WSN, only a limited number of studies have focused on energy efficient heterogeneous WSN (HWSN). Moreover, security poses a major design issue in the HWSN. This paper introduces an energy efficient cluster-based routing protocol with a secure intrusion detection system in HWSN called EECRP-SID. The proposed EECRP-SID technique involves three main phases: cluster construction, optimal path selection, and intrusion detection. Initially, the type II fuzzy logic-based clustering (T2FC) technique with three input parameters are applied for cluster head (CH) selection. These parameters are residual energy level (REL), distance to the base station (DTBS), and node density (NDEN). In addition to CH selection, the salp swarm optimization (SSO) technique is utilized to select optimal paths for inter cluster data transmission, which results in energy efficient HWSN. Finally, to achieve security in cluster based WSN, an effective intrusion detection system (IDS) using long short-term memory (LSTM) is executed on the CHs to identify the presence of intruders in the network. The EECRP-SID method was implemented in MATLAB, and experimental outcomes indicate that it outperformed the compared methods in terms of distinct performance measures.

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Optimized cluster head selection using krill herd algorithm for wireless sensor network

Cited by 57 publications

References 22 publications

T2FL-PSO: Type-2 Fuzzy Logic-Based Particle Swarm Optimization Algorithm Used to Maximize the Lifetime of Internet of Things

T2FL-PSO: Type-2 Fuzzy Logic-Based Particle Swarm Optimization Algorithm Used to Maximize the Lifetime of Internet of Things

An Improved Bat Algorithm for Unequal Clustering in Heterogeneous Wireless Sensor Networks

Energy Efficient Cluster based Routing Protocol with Secure IDS for IoT Assisted Heterogeneous WSN

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