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

Vinoth Kumar Venkatesan (), Ivan Izonin, Jayalakshmi Periyasamy, Alagiri Indirajithu, Anatoliy Batyuk and Mahesh Thyluru Ramakrishna
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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

Energies, 2022, vol. 15, issue 20, 1-22

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.

Keywords: smart city; WSN; energy efficiency; clustering; routing; DD-TDMA; data transmission; cluster head; IoT; base station (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
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