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DAMFO-Based Optimal Path Selection and Data Aggregation in WSN

S. Sudha Mercy1,*, J. M. Mathana2, J. S. Leena Jasmine3

1 Jeppiaar Institute of Technology, Kanchipuram, 631604, India
2 St Peter’s Institute of Higher Education and Research, Chennai, 600054, India
3 Velammal Engineering College, Chennai, 600066, India

* Corresponding Author: S. Sudha Mercy. Email:

Intelligent Automation & Soft Computing 2022, 32(1), 589-604. https://doi.org/10.32604/iasc.2022.021068

Abstract

Wireless Sensor Network (WSN) encompasses several tiny devices termed as Sensor Nodes (SN) that have restriction in resources with lower energy, memory, together with computation. Data Aggregation (DA) is required to optimize WSN for secured data transmission at Cluster Head (CH) together with Base Station (BS). With regard to the Energy Efficiency (EE) along with the privacy conservation requirements of WSN in big-data processing and aggregation, this paper proposed Diversity centered Adaptive Moth-Flame Optimization (DAMFO) for Optimal Path Selection (OPS) and DA in WSN. In the proposed work, initially, the Trust Evaluation (TE) process is performed. The Pompeiu Distance-centered Fuzzy C-Means (PDFCM) is employed for Cluster Formation (CF) in addition to Cluster Head Selection (CHS) and then DAMFO algorithm chooses the optimal path to gather the data together with cluster centroids. The DHECC algorithm then generates keys and encrypts the aggregated data. The encrypted data is finally passed on to the BS. The experimentation outcomes exhibited that the proposed algorithm outweighs the traditional methods with respect to Energy Consumption (EC) 6.35 J, Packet Delivery Ratio (PDR) of 93%, Throughput of 0.956 bps, end-to-end delay 6.547 s, together with a lifetime of networks. Additionally, the proposed system exhibits the best Security Level (SL) of 94.2% amid the transmission.

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Cite This Article

S. Sudha Mercy, J. M. Mathana and J. S. Leena Jasmine, "Damfo-based optimal path selection and data aggregation in wsn," Intelligent Automation & Soft Computing, vol. 32, no.1, pp. 589–604, 2022.



This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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