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OCSO-CA: opposition based competitive swarm optimizer in energy efficient IoT clustering

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Abstract

With the advent of modern technologies, IoT has become an alluring field of research. Since IoT connects everything to the network and transmits big data frequently, it can face issues regarding a large amount of energy loss. In this respect, this paper mainly focuses on reducing the energy loss problem and designing an energy-efficient data transfer scenario between IoT devices and clouds. Consequently, a layered architectural framework for IoT-cloud transmission has been proposed that endorses the improvement in energy efficiency, network lifetime and latency. Furthermore, an Opposition based Competitive Swarm Optimizer oriented clustering approach named OCSO-CA has been proposed to get the optimal set of clusters in the IoT device network. The proposed strategy will help in managing intra-cluster and inter-cluster data communications in an energy-efficient way. Also, a comparative analysis of the proposed approach with the state-of-the-art optimization algorithms for clustering has been performed.

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Acknowledgements

This publication was an outcome of the R&D work undertaken project under the Visvesvaraya PhD Scheme of Ministry of Electronics & Information Technology, Government of India, being implemented by Digital India Corporation.

Author information

Authors and Affiliations

  1. Department of Electronics and Communication Engineering, National Institute of Technology Silchar, Silchar, 788010, India

    Arpita Biswas & Krishna Lal Baishnab

  2. Department of Computer Science & Engineering, Karunya Institute of Technology & Sciences, Coimbatore, 641114, India

    Abhishek Majumdar

  3. Department of Electrical Engineering, National Institute of Technology Silchar, Silchar, 788010, India

    Soumyabrata Das

Authors
  1. Arpita Biswas
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  2. Abhishek Majumdar
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  3. Soumyabrata Das
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  4. Krishna Lal Baishnab
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Corresponding author

Correspondence to Abhishek Majumdar.

Additional information

Arpita Biswas received her B. Tech. degree in Computer Science and Engineering from Tripura Institute of Technology, India in 2012. Punjab Technical University, India in 2014. Currently, she is pursuing his PhD from National Institute of Technology Silchar, India under the Visvesvaraya PhD Scheme of Ministry of Electronics & Information Technology, Government of India, being implemented by Digital India Corporation. Her research areas are information security, Integration of IoT with cloud computing and optimization.

Abhishek Majumdar is working as Assistant Professor in Department of Computer Science and Engineering in Karunya Institute of Technology and Sciences, India. He did his PhD from National Institute of Technology Silchar, India under the Visvesvaraya PhD Scheme of Ministry of Electronics & Information Technology, Government of India, being implemented by Digital India Corporation. His research areas are machine learning, optimization techniques and information security.

Soumyabrata Das is currently associated with the National Institute of Technology (NIT) Silchar, India as a research scholar in the Department of Electrical Engineering. He completed his M. Tech degree from NIT Silchar in 2012 and BE degree in 2010 from NIT Agartala, India. His area of research are soft computing application to power system optimization, probabilistic load-flow analysis, and optimal DG placement.

Krishna Lal Baishnab did his MTech from Indian Institute of Technology kharagpur, WB and PhD from National Institute of Technology Silchar, India. He is currently working as Head and assistant professor, Electronics and Communication Engineering, NIT Silchar. He has more than 20 years of teaching experience and published more than 60 articles in various reputed journals and international conferences. His research areas are Analog VLSI and RF Design: Design of continuous/ discrete time Analog circuit for Biomedical applications, RF CMOS circuit design and wireless systems.

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Biswas, A., Majumdar, A., Das, S. et al. OCSO-CA: opposition based competitive swarm optimizer in energy efficient IoT clustering. Front. Comput. Sci. 16, 161501 (2022). https://doi.org/10.1007/s11704-021-0163-9

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  • DOI: https://doi.org/10.1007/s11704-021-0163-9

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