Probabilistic query generation and fuzzy c -means clustering for energy-efficient operation in wireless sensor networks

Pramod Kumar, Ashvini Chaturvedi

Research output: Contribution to journalArticle

1 Citation (Scopus)

Abstract

Depending upon sensing attributes, wireless sensor networks (WSNs) are classified as event driven, time driven, and query driven. In a given surveillance area, approximation of query generation process using uniform probability mass function (PMF) model seems to be reasonable in aggregate terms based on observations extracted from lifetime span of WSNs. However, owing to random generation aspects of query and the associated temporal variations, the Poisson distribution-based model appears to be more appropriate to resemble the realistic query generation pattern. Invariably, in all the sensor network architectures, the energy management requires an important consideration owing to limited energy resources. For the optimal utilization of energy resources, we propose fuzzy c-means (FCM) algorithm to form clusters in a hierarchical network configuration. Network performance is measured in terms of key performance measures, namely, average residual energy status, critical residual energy status (CRES), and number of network nodes that attain the CRES mark. These performance measures are estimated and analyzed for three different PMF models of query generation namely Uniform, Gaussian and Poisson. The merit of deploying FCM algorithm in terms of maintaining much better energy profile of the entire network is discussed.

Original languageEnglish
Pages (from-to)1439-1450
Number of pages12
JournalInternational Journal of Communication Systems
Volume29
Issue number8
DOIs
Publication statusPublished - 25-05-2016

Fingerprint

Wireless sensor networks
Energy resources
Poisson distribution
Energy management
Network performance
Network architecture
Sensor networks

All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications
  • Electrical and Electronic Engineering

Cite this

@article{92443c65f7ca4b40ba62d9a977e40f13,
title = "Probabilistic query generation and fuzzy c -means clustering for energy-efficient operation in wireless sensor networks",
abstract = "Depending upon sensing attributes, wireless sensor networks (WSNs) are classified as event driven, time driven, and query driven. In a given surveillance area, approximation of query generation process using uniform probability mass function (PMF) model seems to be reasonable in aggregate terms based on observations extracted from lifetime span of WSNs. However, owing to random generation aspects of query and the associated temporal variations, the Poisson distribution-based model appears to be more appropriate to resemble the realistic query generation pattern. Invariably, in all the sensor network architectures, the energy management requires an important consideration owing to limited energy resources. For the optimal utilization of energy resources, we propose fuzzy c-means (FCM) algorithm to form clusters in a hierarchical network configuration. Network performance is measured in terms of key performance measures, namely, average residual energy status, critical residual energy status (CRES), and number of network nodes that attain the CRES mark. These performance measures are estimated and analyzed for three different PMF models of query generation namely Uniform, Gaussian and Poisson. The merit of deploying FCM algorithm in terms of maintaining much better energy profile of the entire network is discussed.",
author = "Pramod Kumar and Ashvini Chaturvedi",
year = "2016",
month = "5",
day = "25",
doi = "10.1002/dac.3112",
language = "English",
volume = "29",
pages = "1439--1450",
journal = "International Journal of Communication Systems",
issn = "1074-5351",
publisher = "John Wiley and Sons Ltd",
number = "8",

}

Probabilistic query generation and fuzzy c -means clustering for energy-efficient operation in wireless sensor networks. / Kumar, Pramod; Chaturvedi, Ashvini.

In: International Journal of Communication Systems, Vol. 29, No. 8, 25.05.2016, p. 1439-1450.

Research output: Contribution to journalArticle

TY - JOUR

T1 - Probabilistic query generation and fuzzy c -means clustering for energy-efficient operation in wireless sensor networks

AU - Kumar, Pramod

AU - Chaturvedi, Ashvini

PY - 2016/5/25

Y1 - 2016/5/25

N2 - Depending upon sensing attributes, wireless sensor networks (WSNs) are classified as event driven, time driven, and query driven. In a given surveillance area, approximation of query generation process using uniform probability mass function (PMF) model seems to be reasonable in aggregate terms based on observations extracted from lifetime span of WSNs. However, owing to random generation aspects of query and the associated temporal variations, the Poisson distribution-based model appears to be more appropriate to resemble the realistic query generation pattern. Invariably, in all the sensor network architectures, the energy management requires an important consideration owing to limited energy resources. For the optimal utilization of energy resources, we propose fuzzy c-means (FCM) algorithm to form clusters in a hierarchical network configuration. Network performance is measured in terms of key performance measures, namely, average residual energy status, critical residual energy status (CRES), and number of network nodes that attain the CRES mark. These performance measures are estimated and analyzed for three different PMF models of query generation namely Uniform, Gaussian and Poisson. The merit of deploying FCM algorithm in terms of maintaining much better energy profile of the entire network is discussed.

AB - Depending upon sensing attributes, wireless sensor networks (WSNs) are classified as event driven, time driven, and query driven. In a given surveillance area, approximation of query generation process using uniform probability mass function (PMF) model seems to be reasonable in aggregate terms based on observations extracted from lifetime span of WSNs. However, owing to random generation aspects of query and the associated temporal variations, the Poisson distribution-based model appears to be more appropriate to resemble the realistic query generation pattern. Invariably, in all the sensor network architectures, the energy management requires an important consideration owing to limited energy resources. For the optimal utilization of energy resources, we propose fuzzy c-means (FCM) algorithm to form clusters in a hierarchical network configuration. Network performance is measured in terms of key performance measures, namely, average residual energy status, critical residual energy status (CRES), and number of network nodes that attain the CRES mark. These performance measures are estimated and analyzed for three different PMF models of query generation namely Uniform, Gaussian and Poisson. The merit of deploying FCM algorithm in terms of maintaining much better energy profile of the entire network is discussed.

UR - http://www.scopus.com/inward/record.url?scp=84955497220&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84955497220&partnerID=8YFLogxK

U2 - 10.1002/dac.3112

DO - 10.1002/dac.3112

M3 - Article

AN - SCOPUS:84955497220

VL - 29

SP - 1439

EP - 1450

JO - International Journal of Communication Systems

JF - International Journal of Communication Systems

SN - 1074-5351

IS - 8

ER -