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Incorporation of Energy Efficient Computational Strategies for Clustering and Routing in Heterogeneous Networks of Smart City

Author

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  • Vinoth Kumar Venkatesan

    (Department of Computer Science and Engineering, Jain (Deemed to be University), Bangalore 562 112, Karnataka, India)

  • Ivan Izonin

    (Department of Artificial intelligence, Lviv Polytechnic National University, 79013 Lviv, Ukraine)

  • Jayalakshmi Periyasamy

    (School of Information Technology and Engineering, Vellore Institute of Technology, Vellore 632 014, Tamil Nadu, India)

  • Alagiri Indirajithu

    (School of Information Technology and Engineering, Vellore Institute of Technology, Vellore 632 014, Tamil Nadu, India)

  • Anatoliy Batyuk

    (Department of Automated Control Systems, Lviv Polytechnic National University, 79013 Lviv, Ukraine)

  • Mahesh Thyluru Ramakrishna

    (Department of Computer Science and Engineering, Jain (Deemed to be University), Bangalore 562 112, Karnataka, India)

Abstract

For decision-making and governance, smart cities depend on tracking data collected via a substantial percentage of wireless sensing nodes. However, several limitations affect Wireless Sensor Network (WSN)-based Internet of Things (IoT) services, such as low battery life, recurrent connectivity problems due to multi-hop connections, and a limited channel capacity. Furthermore, in many systems, clustering and routing are handled independently, which prevents the adaptation of effective strategies for optimal energy usage and prolonged network lifespan. This research gathers data from heterogeneous IoT nodes linked via WSN and distributed across a smart infrastructure. There are two interrelated problems to be addressed with respect to energy efficiency computations: clustering and routing. We provide a new clustering strategy through which efficient routing of critical and regular data is handled. As a result, both clustering and routing have been significantly strengthened, which balances the communication load across different sectors of the smart infrastructure network. Minkowski distance and ranking strategy are used for routing and selecting cluster heads, respectively. Deterministic distributed–time division multiple access (DD-TDMA) scheduling is employed to balance the communication load across the network. The experimental results show that the proposed work outperforms some of the popular cluster-based routing strategies.

Suggested Citation

  • Vinoth Kumar Venkatesan & Ivan Izonin & Jayalakshmi Periyasamy & Alagiri Indirajithu & Anatoliy Batyuk & Mahesh Thyluru Ramakrishna, 2022. "Incorporation of Energy Efficient Computational Strategies for Clustering and Routing in Heterogeneous Networks of Smart City," Energies, MDPI, vol. 15(20), pages 1-22, October.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:20:p:7524-:d:940383
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    References listed on IDEAS

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    1. V. Vinoth Kumar & K. M. Karthick Raghunath & V. Muthukumaran & Rose Bindu Joseph & I. S. Beschi & Akshay K. Uday, 2022. "Aspect based sentiment analysis and smart classification in uncertain feedback pool," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 13(1), pages 252-262, March.
    2. Liu Yang & Yin-Zhi Lu & Yuan-Chang Zhong & Simon X. Yang, 2018. "An unequal cluster-based routing scheme for multi-level heterogeneous wireless sensor networks," Telecommunication Systems: Modelling, Analysis, Design and Management, Springer, vol. 68(1), pages 11-26, May.
    3. Muthukumaran V. & V. Vinoth Kumar & Rose Bindu Joseph & Meram Munirathanam & Balajee Jeyakumar, 2021. "Improving Network Security Based on Trust-Aware Routing Protocols Using Long Short-Term Memory-Queuing Segment-Routing Algorithms," International Journal of Information Technology Project Management (IJITPM), IGI Global, vol. 12(4), pages 47-60, October.
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