Open Access

ARTICLE

Type II Fuzzy Logic Based Cluster Head Selection for Wireless Sensor Network

J. Jean Justus^1,*, M. Thirunavukkarasan², K. Dhayalini³, G. Visalaxi⁴, Adel Khelifi⁵, Mohamed Elhoseny^6,7

1 Department of Computer Science & Engineering, St. Joseph’s College of Engineering, Chennai, 600119, India
2 School of Computing Science and Engineering, Galgotias University, Uttar Pradesh, 203201, India
3 Department of Electrical and Electronics Engineering, K. Ramakrishnan College of Engineering, Tiruchirappalli, 621112, India
4 Department of Computer Science & Engineering, Bharath Institute of Higher Education and Research, Chennai, 600073, India
5 Department of Computer Science and Information Technology, Abu Dhabi University, Abu Dhabi, 59911, United Arab Emirates
6 College of Computer Information Technology, American University in the Emirates, Dubai, United Arab Emirates
7 Faculty of Computers and Information, Mansoura University, Egypt

* Corresponding Author: J. Jean Justus. Email:

Computers, Materials & Continua 2022, 70(1), 801-816. https://doi.org/10.32604/cmc.2022.019122

Received 03 April 2021; Accepted 23 May 2021; Issue published 07 September 2021

Abstract

Wireless Sensor Network (WSN) forms an essential part of IoT. It is embedded in the target environment to observe the physical parameters based on the type of application. Sensor nodes in WSN are constrained by different features such as memory, bandwidth, energy, and its processing capabilities. In WSN, data transmission process consumes the maximum amount of energy than sensing and processing of the sensors. So, diverse clustering and data aggregation techniques are designed to achieve excellent energy efficiency in WSN. In this view, the current research article presents a novel Type II Fuzzy Logic-based Cluster Head selection with Low Complexity Data Aggregation (T2FLCH-LCDA) technique for WSN. The presented model involves a two-stage process such as clustering and data aggregation. Initially, three input parameters such as residual energy, distance to Base Station (BS), and node centrality are used in T2FLCH technique for CH selection and cluster construction. Besides, the LCDA technique which follows Dictionary Based Encoding (DBE) process is used to perform the data aggregation at CHs. Finally, the aggregated data is transmitted to the BS where it achieves energy efficiency. The experimental validation of the T2FLCH-LCDA technique was executed under three different scenarios based on the position of BS. The experimental results revealed that the T2FLCH-LCDA technique achieved maximum energy efficiency, lifetime, Compression Ratio (CR), and power saving than the compared methods.

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Keywords

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