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A Framework for Enhancing Network Lifetime in Internet of Things Environment Using Clustering Formation

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International Conference on Artificial Intelligence for Smart Community

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 758))

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Abstract

Internet of Things (IoT) is one of the current technology that gains the highest acceptance from various industries and academia nowadays. The growth of this technology has streamlined the day-to-day activities of individuals. It can be stated that day-to-day activities in the industries without the aid of the IoT is becoming harder and harder due to the manual process. This study investigates the issues of energy consumption in the IoT environment due to denial of service (DoS) attack in the IoT environment. Besides, a solution that remedies the issues is proposed in this study. The contributions of this study are categorized into two aspects. Firstly, the study described the components of the IoT, which includes the sensor nodes. Secondly, the study emphasizes in improving the energy of the nodes to enhance the network lifetime in the IoT environment. To achieve the milestone the study was implemented using MATLAB, and thereby considering the enabling technology such as wireless sensor network (WSN) and the RFID in the perception layer of IoT. The results reveal that the network lifetime has improved significantly with about 6% improvements as compared with security-based modified LEACH (MS-LEACH).

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References

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Acknowledgements

The research was fully funded by the Center of Graduate Studies (CGS). Author completely acknowledges the financial support given by Universiti Teknologi PETRONAS (UTP) for this study.

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Ghali, A.A., Ahmad, R., Alhussian, H. (2022). A Framework for Enhancing Network Lifetime in Internet of Things Environment Using Clustering Formation. In: Ibrahim, R., K. Porkumaran, Kannan, R., Mohd Nor, N., S. Prabakar (eds) International Conference on Artificial Intelligence for Smart Community. Lecture Notes in Electrical Engineering, vol 758. Springer, Singapore. https://doi.org/10.1007/978-981-16-2183-3_39

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  • DOI: https://doi.org/10.1007/978-981-16-2183-3_39

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-16-2182-6

  • Online ISBN: 978-981-16-2183-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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