Flamingo Jelly Fish search optimization-based routing with deep-learning enabled energy prediction in WSN data communication

ABSTRACT

Nowadays, wireless sensor networks (WSN) have gained huge attention worldwide due to their wide applications in different domains. The limited amount of energy resources is considered as the main limitations of WSN, which generally affect the network life time. Hence, a dynamic clustering and routing model is designed to resolve this issue. In this research work, a deep-learning model is employed for the prediction of energy and an optimization algorithmic technique is designed for the determination of optimal routes. Initially, the dynamic cluster WSN is simulated using energy, mobility, trust, and Link Life Time (LLT) models. The deep neuro-fuzzy network (DNFN) is utilized for the prediction of residual energy of nodes and the cluster workloads are dynamically balanced by the dynamic clustering of data using a fuzzy system. The designed Flamingo Jellyfish Search Optimization (FJSO) model is used for tuning the weights of the fuzzy system by considering different fitness parameters. Moreover, routing is performed using FJSO model which is used for the identification of optimal path to transmit data. In addition, the experimentation is done using MATLAB tool and the results proved that the designed FJSO model attained maximum of 0.657J energy, a minimum of 0.739 m distance, 0.649 s delay, 0.849 trust, and 0.885 Mbps throughput.

KEYWORDS:

• Deep Neuro-Fuzzy Network
• jellyfish search optimization
• Flamingo search algorithm
• fuzzy system

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No potential conflict of interest was reported by the author(s).

Additional information

Funding

The author(s) reported there is no funding associated with the work featured in this article.

Notes on contributors

Dhanabal Subramanian

Dr. S. Dhanabal, Associate professor in the Department of Computer Science and Engineering, Kongunadu College of Engineering and Technology, Trichy, Tamilnadu, India. He has more than 17 years of teaching experience and 5 years of research experience. His area of interest includes Wireless Networking, Data structures and Mobile Computing. He has published several papers in International Journals and presented papers in National and International Conferences and published 2 books.

Sangeetha Subramaniam

Mrs. S. Sangeetha, working as an Assistant Professor in the Department of Information Technology, Kongunadu College of Engineering and Technology, Trichy, Tami Nadu, India. She received B.E. Computer Science and Engineering from PGP College of Engineering and Technology, Namakkal under Anna University-Chennai in 2006. She was awarded with M.E. in Computer Science and Engineering from M. Kumarasamy College of Engineering, Karur under Anna University-Coimbatore in 2009. She has 12 years of teaching experience and pursuing Ph.D., as a part-time research scholar in Anna University, Chennai. Her area of interest lies in Image Processing, Machine Learning and Deep Learning. She has published 10 papers in International journals and presented 15 papers in national and international conferences.

Krishnamoorthy Natarajan

Dr. N. Krishnamoorthy obtained his Ph. D. degree in 2015 from Anna University, Chennai. Currently, he is positioned as Associate Professor in the Department of Software Systems and Engineering-Vellore Institute of Technology-Vellore-Tamilnadu- India. He also got various funds from AICTE, DRDO, MoES, DeitY, DBT, NBHM and ICSSR for conducting various Research Projects, Skill Development Programs and also Seminar and Workshops. He is a Life member of Computer Society of India. Dr. N. Krishnamoorthy is working on various aspects of life-style related mental disorders and engaged in finding preventive as well as therapeutic solutions from indigenous resources.

Kumaravel Thangavel

Kumaravel Thangavel received a bachelor’s degree in Computer Science and Engineering and a master’s degree in Computer and Communication Engineering from Anna University, Chennai in 2006 and 2009 respectively. He is currently pursuing a Ph.D. degree in Computer Science at Anna University, Chennai, focusing on the area of Traffic sign detection using deep learning. From June 2010 to January 2020, he worked as an Assistant Professor in the Department of Computer Science and Engineering at Kongu Engineering College (Autonomous), Perundurai, Erode. Since January 2020, he has been working as an Assistant Professor Senior Grade in the same department at the same college. He has published a number of research articles in well-reputed journals indexed by Scopus. His research interests include Machine Learning, Deep Learning, Data Mining, Nature-Inspired Computing, and Big Data Analytics.