Predilection Perspective of Peremptory Evaluation of Wireless Sensor Networks with Machine Learning Approach

Jagadeeswara rao.E; Nimmakayala.S.V.Srinivas; Dr.K.V.Ramana,Ph.d

Predilection Perspective of Peremptory Evaluation of Wireless Sensor Networks with Machine Learning Approach

Jagadeeswara rao.E * §

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Nimmakayala.S.V.Srinivas

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Dr.K.V.Ramana,Ph.d

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JNTUK,Kakinada, A.P, India.

Predilection Perspective of Peremptory Evaluation of Wireless Sensor Networks with Machine Learning Approach

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Abstract

Data mining based information processing in Wireless Sensor Network (WSN) is at its preliminary stage, as compared to traditional machine learning and WSN. Currently researches mainly focus on applying machine learning techniques to solve a particular problem in WSN. Different researchers will have different assumptions, application scenarios and preferences in applying machine learning algorithms. These differences represent a major challenge in allowing researchers to build upon each other’s work so that research results will accumulate in the community. Thus, a common architecture across the WSN machine learning community would be necessary. One of the major objectives of many WSN research works is to improve or optimize the performance of the entire network in terms of energy conservation and network lifetime. This paper will survey Data Mining in WSN application from two perspectives, namely the Network associated issue and Application associated issue. In the Network associated issue, different machine learning algorithms applied in WSNs to enhance network performance will be discussed. In Application associated issue, machine learning methods that have been used for information processing in WSNs will be summarized.

References

6 Cites in Article

Reference Format

D Culler,D Estrin,M Srivastava (2004). Guest Editors' Introduction: Overview of Sensor Networks.
W Heinzelman,A Chandrakasan,H Balakrishnan (2000). Energy-efficient communication protocol for wireless microsensor networks.
M Yong Wang,Li-Shiuan Peh (2006). A Supervised Learning Approach for Routing Optimizations in Wireless Sensor Networks.
Charles Chien,Igor Elgorriaga,Charles Mcconaghy (2001). Low-power direct-sequence spread-spectrum modem architecture for distributed wireless sensor networks.
R Intanagonwiwat,D Estrin (2000). Directed Diffusion: A Scalable and Robust Communication Paradigm for Sensor Networks.
S Joel,B Predd,H Vincent Poor (2006). Distributed Learning in Wireless Sensor Networksapplication issues and the problem of distributed inference.

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Funding

No external funding was declared for this work.

Conflict of Interest

The authors declare no conflict of interest.

Ethical Approval

No ethics committee approval was required for this article type.

Data Availability

Not applicable for this article.

How to Cite This Article

Jagadeeswara rao.E, Nimmakayala.S.V.Srinivas, Dr.K.V.Ramana,Ph.d. 2012. "Predilection Perspective of Peremptory Evaluation of Wireless Sensor Networks with Machine Learning Approach". Global Journal of Computer Science and Technology - E: Network, Web & Security GJCST-E Volume 12 (GJCST Volume 12 Issue E10).

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GJCST Volume 12 Issue E10

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Journal Specifications

Crossref Journal DOI 10.17406/gjcst

Print ISSN 0975-4350

e-ISSN 0975-4172

Keywords

Data mining Fusion Center Machine Learning Wireless Sensor Network

Classification

GJCST-E Classification I.2.6

Submission ReceivedDecember 15, 2011
Peer Review Double Blind
Handling Editor
Accepted January 4, 2012
Published January 15, 2012

Version of record

v1.2

Issue date

June 2, 2012

Language

English

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Article Matrices

Total Score: 149

Country: India

Subject: Global Journal of Computer Science and Technology

Authors: Jagadeeswara rao.E, Nimmakayala.S.V.Srinivas,Dr.K.V.Ramana,Ph.d (PhD/Dr. count: 1)

View Count (all-time): 392

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Publish Date: 2012 01, Sun

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Total Views: 2.8K

Total Downloads: 225

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