A Critical And Analytical Review On Energy Optimization Of Industrial Internet Of Thing Nodes In Industry 4.0

Sunanda Balkrishana Mane; Dr. Pradip Chandrakant Bhaskar

doi:10.36948/ijfmr.2026.v08i03.76235

A Critical And Analytical Review On Energy Optimization Of Industrial Internet Of Thing Nodes In Industry 4.0

Author(s)	Ms. Sunanda Balkrishana Mane, Dr. Pradip Chandrakant Bhaskar
Country	India
Abstract	The Industrial Internet of Things (IIoT) plays a central role in Industry 4.0 by enabling intelligent automation, real-time monitoring, and data-driven industrial decision-making. However, large-scale deployment of battery-powered IIoT nodes is constrained by excessive energy consumption and limited battery lifetime, particularly in harsh and inaccessible industrial environments. Although numerous energy-efficient techniques have been reported, most existing review studies remain largely descriptive and provide limited analytical insight into practical deployment challenges. This paper presents a critical and analytical review of energy optimization strategies for IIoT nodes, with particular focus on transmission power control, duty cycle optimization, and intelligence-driven energy management techniques. An energy-centric taxonomy is developed to classify energy consumption across hardware, communication, network, and intelligence layers. Furthermore, existing solutions are comparatively analyzed with respect to energy efficiency, latency, computational overhead, scalability, and industrial feasibility. The analysis indicates that while deep learning and reinforcement learning–based approaches offer strong adaptability in dynamic environments, their high computational complexity and deployment constraints limit real-world applicability. Based on these findings, key research challenges are identified, and future directions emphasizing lightweight and adaptive reinforcement learning frameworks are outlined. The study aims to provide structured insights to support the design of sustainable and scalable IIoT systems in Industry 4.0.
Keywords	Industrial Internet of Things; Energy Optimization; Transmission Power Control; Duty Cycle Optimization; Edge Intelligence; Reinforcement Learning.
Field	Computer > Artificial Intelligence / Simulation / Virtual Reality
Published In	Volume 8, Issue 3, May-June 2026
Published On	2026-05-09
DOI	https://doi.org/10.36948/ijfmr.2026.v08i03.76235

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A Critical And Analytical Review On Energy Optimization Of Industrial Internet Of Thing Nodes In Industry 4.0

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