Equal-Size Clustering for Irregularly Deployed Wireless Sensor Networks

Shin, Heewook; Moh, Sangman; Chung, Ilyong; Kang, Moonsoo

doi:10.1007/s11277-014-2262-5

Cited by 20 publications

(18 citation statements)

References 14 publications

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“…Every cluster is assumed to have equal number of ordinary nodes, as described in Shin et al The ordinary nodes either communicate (a) directly with the CHs or (b) through other nodes if the CHs do not reside in their transmission range. Additionally, the power consumed by a particular node to transmit a packet to its CH and to its neighboring nodes is assumed to be similar to a wasp‐mote agriculture pro‐board developed by Libelium, ie, 5026 mAh—the initial energy of an ordinary board.…”

Section: Performance Evaluationmentioning

confidence: 99%

“…In our simulation setup, we modeled a HWSN with n randomly distributed sensor nodes comprised of 95% ordinary nodes and 5% CH nodes in an area with dimension 500 m × 500 m. Various characteristics and parameters of the experiments are shown in Table 2. 63 Every cluster is assumed to have equal number of ordinary nodes, as described in Shin et al 64 The ordinary nodes either communicate (a) directly with the CHs or (b) through other nodes if the CHs do not reside in their transmission range. Additionally, the power consumed by a particular node to transmit a packet to its CH and to its neighboring nodes is assumed to be similar to a wasp-mote agriculture pro-board developed by Libelium,* ie, 5026 mAh-the initial energy of an ordinary board.…”

Section: Performance Evaluationmentioning

confidence: 99%

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PFARS: Enhancing throughput and lifetime of heterogeneous WSNs through power‐aware fusion, aggregation, and routing scheme

Khan

Zakarya

Tan

et al. 2019

Int J Communication

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Summary Heterogeneous wireless sensor networks (WSNs) consist of resource‐starving nodes that face a challenging task of handling various issues such as data redundancy, data fusion, congestion control, and energy efficiency. In these networks, data fusion algorithms process the raw data generated by a sensor node in an energy‐efficient manner to reduce redundancy, improve accuracy, and enhance the network lifetime. In literature, these issues are addressed individually, and most of the proposed solutions are either application‐specific or too complex that make their implementation unrealistic, specifically, in a resource‐constrained environment. In this paper, we propose a novel node‐level data fusion algorithm for heterogeneous WSNs to detect noisy data and replace them with highly refined data. To minimize the amount of transmitted data, a hybrid data aggregation algorithm is proposed that performs in‐network processing while preserving the reliability of gathered data. This combination of data fusion and data aggregation algorithms effectively handle the aforementioned issues by ensuring an efficient utilization of the available resources. Apart from fusion and aggregation, a biased traffic distribution algorithm is introduced that considerably increases the overall lifetime of heterogeneous WSNs. The proposed algorithm performs the tedious task of traffic distribution according to the network's statistics, ie, the residual energy of neighboring nodes and their importance from a network's connectivity perspective. All our proposed algorithms were tested on a real‐time dataset obtained through our deployed heterogeneous WSN in an orange orchard and also on publicly available benchmark datasets. Experimental results verify that our proposed algorithms outperform the existing approaches in terms of various performance metrics such as throughput, lifetime, data accuracy, computational time, and delay.

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Section: Performance Evaluationmentioning

confidence: 99%

Section: Performance Evaluationmentioning

confidence: 99%

PFARS: Enhancing throughput and lifetime of heterogeneous WSNs through power‐aware fusion, aggregation, and routing scheme

Khan

Zakarya

Tan

et al. 2019

Int J Communication

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“…Also there are many published works which utilize equal size clustering to balance the energy consumption in sensor networks. A balanced clustering algorithm (BCA) is promoted for irregularly deployed sensor networks in [35], which uses equal size clustering to improve energy efficiency and balance energy consumption in non-uniformly deployed sensor networks. The cluster head selection is based on the node density parameter.…”

Section: Related Workmentioning

confidence: 99%

An Improved Energy Aware Distributed Clustering Protocol for Wireless Sensor Networks

Gupta¹,

Pandey

2016

IJCNIS

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Abstract-The present work considers energy aware distributed clustering protocol (EADC) which is commonly employed for balancing the energy consumption in non-uniformly deployed sensor networks. In the EADC, residual energy is considered as the primary clustering parameter and the clusters are constructed using uniform competition radius. However in non-uniform node distribution scenarios, more energy imbalance occurs in the network due to variation in intracluster energy consumption. For counter balancing it, an inter-cluster energy-efficient multi-hop routing protocol is utilized. In the EADC protocol, cluster heads select the relay node based on residual energy and cluster member count information. But this approach is less efficient in energy balancing. In this paper, an improved energy aware distributed equal clustering protocol is projected. Our scheme considers the relay metric directly in terms of energy expense in relaying the sensed data to the base station taking into account not only the residual energy and member count information but also distance information. It provides better balancing of energy in comparison with the existing approach. Three different scenarios created by varying node distribution are used for evaluating the performance of the proposed protocol and comparison is made with the EADC protocol. The results show that the proposed scheme extends network lifetime in all the scenarios.

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“…As a result, the coverage area of each cluster is almost evenly distributed, reducing the unnecessary repetition of sensing and transmission. (11) Chang et al proposed a distributed joint optimization routing algorithm based on an analytic hierarchy process (AHP). The remaining energy of the node, the distance from the node to the base station, and the degree of the node were defined as three key decision criteria.…”

Section: Introductionmentioning

confidence: 99%

Improvement of Energy Efficiency in Wireless Sensor Networks Using Low-energy Adaptive Clustering Hierarchy (LEACH)-based Energy Betweenness Model

Feng¹,

Pan²,

Huang³

et al. 2019

Sensors and Materials

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Keywords: sensor network, energy betweenness, protocol of clustering, energy consumption, lifespan of network, balanceThe imbalance of energy consumption in wireless sensor networks (WSNs) may affect both network lifetime and reliability. Traditionally, the low-energy adaptive clustering hierarchy (LEACH) protocol has been applied to lower the energy consumption. Although the LEACH protocol can choose cluster heads (CHs) randomly to prevent a number of nodes from premature failure due to overutilization, the discrepancy in the energy distribution under different network topologies may result in a low network performance efficiency. In this paper, the LEACHenergy betweenness (LEACH-EB) model is proposed by taking energy consumption as a constraint condition. It can judge the equilibrium of clustering based on the energy betweenness of each node and realize the optimization of clustering in WSNs. The simulation results verify that the proposed LEACH-EB model can make the clustering more energy-efficient for better performance in terms of reliability and stability than the LEACH protocol. Additionally, the model can significantly reduce the extra energy loss caused by uneven clustering and thus prevent the degradation of network performance from the premature senescence of some nodes.

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Equal-Size Clustering for Irregularly Deployed Wireless Sensor Networks

Cited by 20 publications

References 14 publications

PFARS: Enhancing throughput and lifetime of heterogeneous WSNs through power‐aware fusion, aggregation, and routing scheme

PFARS: Enhancing throughput and lifetime of heterogeneous WSNs through power‐aware fusion, aggregation, and routing scheme

An Improved Energy Aware Distributed Clustering Protocol for Wireless Sensor Networks

Improvement of Energy Efficiency in Wireless Sensor Networks Using Low-energy Adaptive Clustering Hierarchy (LEACH)-based Energy Betweenness Model

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