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Editor-in-chief
Dr. Lin Huang
Metropolitan State University of Denver, USA
It's my honor to take on the position of editor in chief of IJMLC. We encourage authors to submit papers concerning any branch of machine learning and computing.
IJMLC 2011 Vol.1(3): 224-230 ISSN: 2010-3700
DOI: 10.7763/IJMLC.2011.V1.33

Ensuring reliability and freshness for Data Aggregation in Wireless Sensor Networks

Shehzad Ashraf Ch., Mian Muhammad Omair, Iftikhar Ali Khan and Tahir Afzal Malik
Abstract—The emergence of senor node architecture with its advance capabilities to control the different hardware units and its enhancements in low power and affordable computational devices has made sensor networks from dream to reality. Sensor networks are deployed in widespread targeted areas where they can work for many years and can sense the environment behavior and its different variables without any need to externally charge their installed batteries, the security in sensor networks has become most important aspect along with low power, as the sensors are unattended so there is more possibility of attack in WSN than usual computer networks. Data aggregation security is an important task as if some false node injects a highly odd data values it will highly effect the whole aggregation process. The paper reviews the need of security for data aggregation and propose an architecture which eliminates the false values injection as well as it provides end to end reliability and data freshness, the architecture is also energy optimized.

Index Terms—data aggregation, sensor networks, energy efficiency, Hash.

Mr. Shehzad Ashraf, Mian Muhammad Omair and Iftikhar Ali Khan are with department of computer science International Islamic University Islamabad, Pakistan, Tahir Afzal Malik is With Al-Faisal University Abha Kingdom of Saudi Arabia. Email: t.a.malik@pscabha.edu.sa

[PDF]

Cite: Shehzad Ashraf Ch., Mian Muhammad Omair, Iftikhar Ali Khan and Tahir Afzal Malik, "Ensuring reliability and freshness for Data Aggregation in Wireless Sensor Networks," International Journal of Machine Learning and Computing vol. 1, no. 3, pp. 224-230, 2011.

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