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Elangovan Muniyandy

Work place: Department of Biosciences, Saveetha School of Engineering. Saveetha Institute of Medical and Technical Sciences, Chennai - 602 105, India

E-mail: elangovanm2@outlook.com

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Biography

M. Elangovan has graduated with a Doctor of Engineering (Dr. Eng.) from Hiroshima University, Japan in the field of Naval Architecture and a master’s degree (M. Eng.) in Engineering Design from Government College Technology, Coimbatore, Tamilnadu, India. 
He is currently working as a Director (R&D and Consulting) at DTC Corp, Chennai. He provides high-level software development service on Python, SQL, AI and ML. He started his career as a Ship Designer at National Ship Design and Research Centre, Visakhapatnam which is a Central Government organization, and later, he worked for well-known companies like Jagson International Ltd, New Delhi, Allseas Engineering Dubai, IRS Mumbai, NAPA Finland, and GreenSHIP Bangalore. He has visited many countries like Japan, China, Korea, France, Brazil, Malaysia, Singapore for technical discussions and conferences. Altogether has got 24 years of industrial experience which includes five years of teaching at a university.  
He was awarded a funded project from Naval Science and Technological Laboratory (NSTL), Visakhapatnam, India, and completed it with successful output. Altogether, he has completed research and consultancy projects worth 40 crores and Published more than 150 papers in journals and conferences. He is an editor for two journals and a reviewer for four journals. He has remarkable contributions in marine hydrodynamics, design, CFD, underwater marine vehicles, robotics, energies, sensors and industrial robots.

Author Articles
Multi-objective Clustering Framework for Energy-efficient Precision Agriculture in WSNs using Optimized Convolutional Autoencoder with Dual-key Transformer Network

By M. Sudha Abha Kiran Rajpoot K. Narasimha Raju Elangovan Muniyandy

DOI: https://doi.org/10.5815/ijcnis.2026.01.06, Pub. Date: 8 Feb. 2026

Precision agriculture relies on wireless sensor networks (WSNs) to support informed decision-making, thereby enhancing crop yields and resource management. A critical challenge in such networks is minimizing the energy consumption of sensor nodes while ensuring reliable data transmission. Sensor nodes are grouped using an optimal multi-objective clustering approach, which also chooses appropriate cluster heads (CH) for effective communication. By combining the exploration power of the Osprey Optimization Algorithm with the exploitation power of the Parrot Optimizer, a hybrid optimization approach improves CH selection. A hybrid deep learning framework, combining a convolutional autoencoder with a dual-key transformer network, is designed to monitor energy utilization and detect constraints affecting consumption. Training and testing performance of this framework is further improved using a metaheuristic based on the cooperative feeding and locomotion behavior of gooseneck barnacles. Experimental evaluation demonstrates superior performance, achieving 99.2% accuracy, 68 kbps throughput, 98% packet delivery ratio, and a network lifetime of 85 ms. With an average delay of 0.23 seconds, energy consumption is decreased to 39 J, demonstrating the effectiveness of the suggested strategy for dependable and sustainable precision agriculture applications.

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