ItemArticle

AI-driven deep learning framework for energy-efficient optimization in IoT-enabled wireless networks

Title
AI-driven deep learning framework for energy-efficient optimization in IoT-enabled wireless networks
Creator
Venugopal, Anita; Purad, Hemavati C.; Shanbhog, Manjula; Malhotra, Richa; Kaur, Tejinder; Jha, Nivedita
Description
Artificial intelligence (AI) and Internet of Things (IoT)-enabled wireless sensor networks (WSNs) have revolutionized industries by providing automation, real-time monitoring, and analytics that are predictive. WSNs still face significant obstacles such data security, network flexibility, and energy limitations in spite of these developments. In order to optimize energy use in Internet of Things (IoT)-based WSNs, this study introduces a novel Reinforcement Learning-based Energy-Efficient Communication Protocol (RL-EECP) to optimize the lifetime of networks and guarantee effective data transmission. The suggested protocol integrates sleep scheduling, reinforcement learning, and data fusion techniques. Also, an adaptive prioritization approach is introduced that assesses nodes according to the surroundings, significance, and energy consumption. Experiments show that RL- EECP performs better than existing studies in extending node lifetime and preserving excellent network performance. Bharati Vidyapeeth's Institute of Computer Applications and Management 2025.
Source
International Journal of Information Technology (Singapore);
Date
01-01-2025
Publisher
Springer Science and Business Media B.V.
Subject
Data Fusion; Energy Efficiency; IoT; Network Optimization; Reinforcement Learning; Sleep Scheduling; Wireless Sensor Networks
Coverage
Venugopal A., IT Deparment, Dhofar University, Dhofar, Salalah, Oman; Purad H.C., Department of AIML, Ballari Institute of Technology & Management, Karnataka, Ballari, India; Shanbhog M., Department of Computer Science, Christ University, Bangalore, India; Malhotra R., Dr. Akhilesh Das Gupta Institute of Professional Studies (ADGIPS), Delhi, India; Kaur T., Department of MMICTBM, Maharishi Markandeshwar (Deemed To Be University), Mullana, Haryana, Ambala, India; Jha N., Symbiosis Institute of Business Management, Symbiosis International (Deemed) University, Nagpur, India
Rights
Restricted Access; Hardcopy may be available in the library
Relation
ISSN: 25112104;
Format
online
Language
English
Type
Article

Citation

Venugopal, Anita; Purad, Hemavati C.; Shanbhog, Manjula; Malhotra, Richa; Kaur, Tejinder; Jha, Nivedita, “AI-driven deep learning framework for energy-efficient optimization in IoT-enabled wireless networks,” CHRIST (Deemed To Be University) Institutional Repository, accessed June 19, 2026, https://archives.christuniversity.in/items/show/22101.

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