Hybrid Optimized Fuzzy Based Cluster Head Selection for WSN Data Communication in IoT Environment

Hybrid Optimized Fuzzy Based Cluster Head Selection for WSN Data Communication in IoT Environment

© 2022 by IJETT Journal
Volume-70 Issue-7
Year of Publication : 2022
Authors : Kanakaraju R, Arun Vikas Singh
DOI : 10.14445/22315381/IJETT-V70I7P244

How to Cite?

Kanakaraju R, Arun Vikas Singh, "Hybrid Optimized Fuzzy Based Cluster Head Selection for WSN Data Communication in IoT Environment" International Journal of Engineering Trends and Technology, vol. 70, no. 7, pp. 422-437, 2022. Crossref, https://doi.org/10.14445/22315381/IJETT-V70I7P244

Wireless Sensor Network (WSN) is resource-constrained and is applied in different applications, namely health care observation, home monitoring, military systems, etc. Moreover, these applications are interconnected with various devices, which are proficiently interrelating with each other with the Internet, and it is called the Internet of Things (IoT). Usually, WSN is a most significant role over IoT structure. The sensors are arbitrarily located in harsh environments where communication networks experience various privacy problems in WSN, which is critical for data transmission. This paper's Political Caviar Social Optimization Algorithm (PCSOA) is developed for Cluster Head Selection (CHS) for WSN data communication in IoT structure. The Deep Residual Network (DRN) is applied for predicting energy and is trained by the developed Political Caviar Social Optimization Algorithm (PCSOA). Moreover, an Adaptive Genetic Fuzzy System (AGFS) with various objectives, like residual energy, predicted energy, distance, trust factors, Link Life Time (LLT), and delay, is utilized for selecting CHs. In addition, PCSOA is employed for effective routing considering fitness parameters as different objectives. The proposed DRN+PCSOA outperformed other methods delay, distance, residual energy, and trust by 0.1941sec, 55.63m, 0.1991J, and 0.6109, respectively

Routing, Adaptive Genetic Fuzzy System, Political Optimizer, Link Life Time model, Wireless Sensor Network.

[1] Li, Lili, Shujuan Zhang, and Bin Wang, “Plant Disease Detection and Classification By Deep Learning—A Review,” IEEE Access, Vol.9, Pp.56683-56698, 2021.
[2] Sujatha, R., Jyotir Moy Chatterjee, N. Z. Jhanjhi, and Sarfraz Nawaz Brohi, “Performance of Deep Learning Vs. Machine Learning In PLDD, Microprocessors, and Microsystems,” Vol.80 , Pp.103615, 2021.
[3] Jogekar, Ravindranamdeorao, and Nandita Tiwari, “A Review of Deep Learning Techniques for Identification and Diagnosis of Plant Leaf Disease, Smart Trends In Computing and Communications,” Proceedings of Smartcom, Pp.435-441, 2020-2021.
[4] Vishnoi, Vibhor Kumar, Krishan Kumar, and Brajesh Kumar, “Plant Disease Detection Using Computational Intelligence and Image Processing,” Journal of Plant Diseases and Protection, Vol. 128, No. 1, Pp.19-53, 2021.
[5] Kaur, Navneet., “PLDD Using Ensemble Classification and Feature Extraction,” Turkish Journal of Computer and Mathematics Education (TURCOMAT), Vol.12, No. 11, Pp.2339-2352, 2021.
[6] Deepalakshmi, P., K. Lavanya, and Parvathaneni Naga Srinivasu, “PLDD Using CNN Algorithm,” International Journal of Information System Modeling and Design (IJISMD), Vol.12, No. 1 , Pp.1-21, 2021.
[7] Chowdhury, Muhammad EH, Tawsifur Rahman, Amithkhandakar, Mohamed Arseleneayari, Aftabullah Khan, Muhammad Salman Khan, Nasser Al-Emadi, Mamun Bin Ibnereaz, Mohammad Tariqul Islam, and Sawal Hamid Md Ali, “Automatic and Reliable Leaf Disease Detection Using Deep Learning Techniques,” Agriengineering, Vol. 3, No. 2 , Pp.294-312, 2021.
[8] Abbas, Amreen, Sweta Jain, Mahesh Gour, and Swethavankudothu, “Tomato Plant Disease Detection Using Transfer Learning With C-GAN Synthetic Images, Computers and Electronics In Agriculture,” Vol. 187, Pp.106279, 2021.
[9] Lu, Jinzhu, Lijuan Tan, and Huanyu Jiang, “Review on Convolutional Neural Network (CNN) Applied to Plant Leaf Disease Classification,” Agriculture, Vol. 11, No. 8 , Pp.707, 2021.
[10] Mohanty, Sharada P., David P. Hughes, and Marcel Salathé, “Using Deep Learning for Image-Based Plant Disease Detection,” Frontiers In Plant Science , Vol.7 , Pp.1419, 2016.
[11] Sladojevic, Srdjan, Marko Arsenovic, Andrasanderla, Dubravkoculibrk, and Darkostefanovic, “Deep Neural Networks Based Recognition of Plant Diseases By Leaf Image Classification,” Computational Intelligence and Neuroscience, Vol.2016, 2016.
[12] Ramcharan, Amanda, Kelseebaranowski, Peter Mccloskey, Babuali Ahmed, James Legg, and David P. Hughes, “Deep Learning for Image-Based Cassava Disease Detection,” Frontiers In Plant Science, Vol.8 , Pp.1852, 2017.
[13] Fuentes, Alvaro, Sook Yoon, Sang Cheol Kim, and Dong Sun Park, “A Robust Deep-Learning-Based Detector for Real-Time Tomato Plant Diseases and Pests Recognition,” Sensors, Vol. 17, No. 9, Pp.2022, 2017.
[14] Ferentinos, Konstantinos P, “Deep Learning Models for Plant Disease Detection and Diagnosis,” Computers and Electronics In Agriculture, Vol.145 , Pp.311-318, 2018.
[15] Agarwal, Mohit, Abhishek Singh, Siddhartha Arjaria, Amit Sinha, and Suneet Gupta, “Toled: Tomato Leaf Disease Detection Using Convolution Neural Network,” Procedia Computer Science, Vol.167, Pp.293-301, 2020.
[16] Karthik, R., M. Hariharan, Sundaranand, Priyanka Mathikshara, Annie Johnson, and R. Menaka, “Attention Embedded Residual CNN for Disease Detection In Tomato Leaves,” Applied Soft Computing, Vol.86 , Pp.105933, 2020.