A Distributed, Hybrid Energy-Efficient Clustering Protocol for Heterogeneous Wireless Sensor Network

Wang, Jun; Zhu, Xuegang; Cheng, Yong; Zhu, Yongsheng

doi:10.14257/ijgdc.2013.6.4.04

Cited by 6 publications

(6 citation statements)

References 16 publications

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“…Three parameters are taken into consideration for CH election, namely, residual energy, node degree, and the type of node. It has better performance in case of the heterogeneous environment …”

Section: Related Workmentioning

confidence: 99%

“…It has better performance in case of the heterogeneous environment. 19 Balancing the energy consumption in the network is a crucial issue. So to resolve this issue, several protocols are developed.…”

Section: Related Workmentioning

confidence: 99%

“…Instead of separately minimizing each fitness function, it is better to minimize the combination of the above fitness function as shown in Equation (19). The above fitness functions are strongly in harmony of each other.…”

Section: Ch Balancing Factor (F 4 )mentioning

confidence: 99%

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GWO‐C: Grey wolf optimizer‐based clustering scheme for WSNs

Agrawal

Qureshi

Pincha

et al. 2020

Int J Communication

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Summary A wireless sensor network (WSN) is a prominent technology that could assist in the fourth industrial revolution. Sensor nodes present in the WSNs are functioned by a battery. It is impossible to recharge or replace the battery, hence energy is the most important resource of WSNs. Many techniques have been devised and used over the years to conserve this scarce resource of WSNs. Clustering has turned out to be one of the most efficient methods for this purpose. This paper intends to propose an efficient technique for election of cluster heads in WSNs to increase the network lifespan. For the achievement of this task, grey wolf optimizer (GWO) has been employed. In this paper, the general GWO has been modified to cater to the specific purpose of cluster head selection in WSNs. The objective function for the proposed formulation considers average intra‐cluster distance, sink distance, residual energy, and CH balancing factor. The simulations are carried out in diverse conditions. On comparison of the proposed protocol, ie, GWO‐C protocol with some well‐known clustering protocols, the obtained results prove that the proposed protocol outperforms with respect to the consumption of the energy, throughput, and the lifespan of the network. The proposed protocol forms energy‐efficient and scalable clusters.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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GWO‐C: Grey wolf optimizer‐based clustering scheme for WSNs

Agrawal

Qureshi

Pincha

et al. 2020

Int J Communication

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“…Elbhiri et al [14] proposed a developed distributed energy efficient clustering scheme (DDEEC) to avoid this unbalanced case of cluster head selection that makes some changes in DEEC to save advanced nodes from being punished over and again. DDEEC introduces threshold residual energy to save advanced nodes from being punished over and again.…”

Section: Related Workmentioning

confidence: 99%

Developed Distributed Energy-Efficient Clustering (DDEEC) Algorithm based on Fuzzy Logic Approach for Optimizing Energy Management in Heterogeneous WSNs

Rathee¹,

Kashyap²,

Choudhary³

2015

IJCA

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Energy Management can be improved by proficient clustering algorithms in heterogeneous wireless sensor networks. Coordination through cluster head selection provides efficient data aggregation that reduces communication overhead in the network. In this paper, we propose a fuzzy logic approach based DDEEC clustering algorithm which aims to prolong the lifetime of nodes in heterogeneous WSNs. We compare this algorithm with the PSO based DDEEC algorithm and original DDEEC algorithm according to the parameters of first node dies at different rounds and energy-efficiency metrics. The efficiency of proposed optimized fuzzy algorithm is proved by the Matlab experimental results. Simulation results exhibits that the proposed algorithm has higher energy efficiency and can improve life span of a node and data delivery at the base station over its comparatives.

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“…HEED considers a hybrid of energy and communication cost when selecting CHs [26]. Unlike LEACH, it does not select cell-head nodes randomly.…”

Section: Hybrid Energy-efficient Distributed Clustering (Heed)mentioning

confidence: 99%

WSN Performance Issues and Various Clustering Methods

Modi¹,

Prajapati²

2014

IJCA

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There are several application have been developed with need of self organization for network. To fulfill this requirement need of wireless sensor network in such applications. To manage network efficiently clustering is used. Lots of works have been done in field of wireless sensor networks (WSNs) in last few years. These researches have boost potential of WSNs in applications such as security monitoring, disaster management, military area, border protection and health monitoring systems. Such applications are required to be remotely deployed sensor nodes in huge numbers and to operate autonomously. So there need to scalability, nodes are often collected into disjoint clusters. This paper, presents a categorization and common organization of available clustering proposal. This work analysis various clustering algorithms used for WSNs and give a review with focusing on their objectives features, etc. and proposed efficient clustering method for stable cluster formation and maintenance.

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A Distributed, Hybrid Energy-Efficient Clustering Protocol for Heterogeneous Wireless Sensor Network

Abstract: Abstract

Cited by 6 publications

References 16 publications

GWO‐C: Grey wolf optimizer‐based clustering scheme for WSNs

GWO‐C: Grey wolf optimizer‐based clustering scheme for WSNs

Developed Distributed Energy-Efficient Clustering (DDEEC) Algorithm based on Fuzzy Logic Approach for Optimizing Energy Management in Heterogeneous WSNs

WSN Performance Issues and Various Clustering Methods

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