GIC-Net: AI-Driven Green IoT-CAN Bus Networks for Sustainable Energy Management with Optimization and Data Compression

P. Thirumavalavasethurayar; T. Ravi

doi:10.5750/ijme.v168iA6(S).2063

Published: Nov 13, 2026

DOI: https://doi.org/10.5750/ijme.v168iA6(S).2063

Keywords:

Artificial Intelligence (AI) Green Internet of Things (Green IoT) Controller Area Network (CAN Bus) Ant Colony Optimization (ACO) Energy management Data compression Cluster-based routing Sleep scheduling Network lifetime Sustainable computing

Thirumavalavasethurayar P.

Research Scholar, Faculty of Electronics and Communication, Sathyabama Institute of Science and Technology, India

Dr. T. Ravi

Professor, Faculty of Electronics and Communication,Sathyabama Institute of Science and Technology, India

Abstract

The rapid expansion of Internet of Things (IoT) applications has significantly increased energy consumption and network complexity, so that it becomes very essential for the development of efficient and sustainable communication. This paper proposes GIC-Net, an Artificial Intelligence (AI)-driven Green IoT-CAN Bus network designed for sustainable energy management through intelligent optimization and data compression techniques. The proposed framework integrates a Green IoT network model with dynamic cluster-based architecture with cluster head selection, cluster member management, and sleep scheduling to enhance energy efficiency. Additionally, an IoT-CAN Bus communication layer is developed to enable reliable, low-latency data transmission, supported by optimized data collection and compression mechanisms to reduce bandwidth and storage overhead.To further improve system performance, AI-driven optimization algorithms are employed for adaptive decision-making, while a comprehensive energy model is formulated to evaluate energy consumption, residual energy, and system lifetime. The proposed approach is validated through extensive simulations, considering key performance metrics such as cluster head selection efficiency, number of alive nodes, energy consumption, delay, packet delivery ratio, and total system energy.

Issue

Vol. 168 No. A6(S) (2026): Special Issue - New Technologies and their Effects on Real-Time Social Developments

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