Contemporary Affirmation of Machine Learning Models for Sensor Validation and Recommendations for Future research Directions

Contemporary Affirmation of Machine Learning Models for Sensor Validation and Recommendations for Future research Directions

Abdo Mahyoub Taher Nasser

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Dr. V. P. Pawar

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α Swami Ramanand Teerth Marathwada University

Contemporary Affirmation of Machine Learning Models for Sensor Validation and Recommendations for Future research Directions

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Abstract

Wireless Sensor Networks (WSNs) are important and needed systems for the future as the notion “Internet of Things” has emerged lately. They’re used for observation, tracking, or controlling of several uses in sector, health care, home, and military. Yet, the quality of info collected by sensor nodes is changed by anomalies that happen because of various grounds, including node failures, reading errors, unusual events, and malicious assaults. Thus, fault detection is a necessary procedure before it’s used in making selections to make sure the quality of sensor information. A multitude of methods can be called multiple-changeable systems/agents. For example methods such as for example creating heating system, ventilation and air conditioner(HVAC) methods are changeable methods / agents . Multiple-changeable methods /agents such as for instance these commonly don’t meet performance expectations imagined at design time. Such failings can be a result of a number of factors, for example difficulties due to improper installment, substandard maintenance, or products failure. These issues, or “faults,” can comprise mechanical disappointments, management difficulties, design mistakes, and improper operator treatment.

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Funding

No external funding was declared for this work.

Conflict of Interest

The authors declare no conflict of interest.

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No ethics committee approval was required for this article type.

Data Availability

Not applicable for this article.

How to Cite This Article

Abdo Mahyoub Taher Nasser. 2014. \u201cContemporary Affirmation of Machine Learning Models for Sensor Validation and Recommendations for Future research Directions\u201d. Global Journal of Computer Science and Technology - E: Network, Web & Security GJCST-E Volume 14 (GJCST Volume 14 Issue E3): .

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GJCST Volume 14 Issue E3
Pg. 7- 13

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Journal Specifications

Crossref Journal DOI 10.17406/gjcst

Print ISSN 0975-4350

e-ISSN 0975-4172

Keywords

detection effectiveness detection efficiency energy cons sensor data fault proneness detection sensor validation wiireless sensor networks (WSNs)

Submission ReceivedDecember 6, 2013
Peer Review Double Blind
Handling Editor
Accepted January 4, 2014
Published January 15, 2014

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June 21, 2014

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Total Score: 107

Country: India

Subject: Global Journal of Computer Science and Technology - E: Network, Web & Security

Authors: Abdo Mahyoub Taher Nasser, Dr. V. P. Pawar (PhD/Dr. count: 1)

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