Energy-Aware Routing Via Transformer-Based Spectrum Learning For Iot Mesh Networks

S. Indhumathi; B. Tamil Selvi; Sruthi S. Madhavan; Dr. S. M. Deepa; J. Dhanasekar; N. Revathi

doi:10.29284/w96j2v52

Authors

S. Indhumathi Assistant Professor Department of Mathematics Nehru Arts and Science College Thirumalayampalayam, Coimbatore, India
B. Tamil Selvi Assistant Professor Department of Electronics and Communication Engineering Karpagam Institute of Technology Coimbatore, India
Sruthi S. Madhavan Assistant Professor Department of Artificial Intelligence & Data Science Nehru Institute of Engineering and Technology Coimbatore, India
Dr. S. M. Deepa Assistant Professor Department of Electronics and Communication Engineering Nehru Institute of Engineering and Technology Coimbatore, India
J. Dhanasekar Assistant Professor (Senior Grade) Department of Electronics and Communication Engineering Sri Eshwar College of Engineering Kinathukadavu, Coimbatore, India
N. Revathi Assistant Professor (Senior Grade) Department of Electronics and Communication Engineering Nehru Institute of Engineering and Technology Coimbatore, India

DOI:

https://doi.org/10.29284/w96j2v52

Keywords:

Internet of Things (IoT); IoT Mesh Networks; Energy-Aware Routing; Transformer Networks; Spectrum Learning; Attention Mechanism; Network Lifetime Optimization; Intelligent Routing; Wireless Sensor Networks.

Abstract

Energy efficiency and spectrum utilization are critical challenges in large-scale Internet of Things (IoT) mesh networks due to dense node deployment, dynamic traffic patterns, and limited battery resources. Conventional routing protocols often fail to adapt to rapidly varying spectrum conditions and energy constraints, leading to increased packet loss and reduced network lifetime. This paper proposes an Energy-Aware Routing framework based on Transformer-Driven Spectrum Learning (EAR-TSL) for IoT mesh networks. The proposed approach leverages a transformer architecture to learn long-range temporal and spatial dependencies in spectrum availability, interference patterns, and node energy states. By integrating attention-based spectrum prediction with adaptive routing decisions, the framework dynamically selects energy-optimal paths while minimizing spectrum congestion and transmission overhead. Extensive simulations demonstrate that the proposed EAR-TSL scheme significantly improves network lifetime, packet delivery ratio, and throughput while reducing end-to-end delay and energy consumption when compared with traditional energy-aware and machine learning-based routing protocols. The results confirm that transformer-based spectrum intelligence provides a scalable and robust solution for next-generation IoT mesh networking environments.

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