AI-Optimized Routing Protocols for Energy-Efficient IoT Networks

Authors

  • Arafa Abdelzaher Nasef * Department of Physics and Engineering Mathematics, Faculty of Engineering, Kafrelsheikh University, Kafrelsheikh (33516), Egypt.
  • Arya Ashutosh Das School of Computer Science Engineering, KIIT University, Bhubaneswar, India.

https://doi.org/10.48313/siot.v2i1.268

Abstract

The advent of the Internet of Things (IoT) has led to a significant increase in the number of connected devices across various sectors, such as smart cities, healthcare, and industrial automation. These devices often operate in environments with limited power resources, making energy efficiency a critical consideration in IoT network design and implementation. Traditional routing protocols, while effective in conventional networks, frequently fall short in addressing the unique challenges posed by IoT, where devices are characterized by low power, mobility, and a dynamic operational context. The primary goal of this research is to explore AI-optimized routing protocols and their role in enhancing energy efficiency within IoT networks. This paper outlines how integrating Artificial Intelligence (AI) into routing protocols can address these challenges. AI techniques, including Machine Learning (ML) and Reinforcement Learning (RL), enable real-time optimization of routing paths based on current network conditions, device energy levels, and traffic patterns. By leveraging predictive algorithms, AI can significantly reduce energy consumption while maintaining reliable communication between devices. For instance, RL approaches allow devices to learn optimal routing strategies through experience, adapting to changes in the network environment and improving overall efficiency. The research presents a comprehensive review of existing literature on AI-based routing protocols, highlighting key methodologies and their effectiveness in real-world applications. Case studies demonstrate the successful deployment of AI-optimized routing in various IoT scenarios, showcasing improvements in energy management and operational lifespan of devices. These examples underline the potential for AI to transform energy management strategies within IoT networks, allowing for a more sustainable and scalable deployment of connected devices. Furthermore, the paper discusses the challenges and limitations of implementing AI-driven routing protocols in IoT environments. The paper critically analyzes issues such as the computational constraints of low-power devices, scalability in large networks, and privacy concerns associated with data collection and analysis. Addressing these challenges is essential for the broader adoption of AI-optimized solutions in energy-efficient IoT networks. In conclusion, this research emphasizes the importance of AI in optimizing routing protocols for energy efficiency in IoT systems. As the number of connected devices continues to rise, the need for intelligent, adaptive, and energy-efficient routing solutions becomes increasingly critical. The findings of this paper contribute to the ongoing discourse on AI applications in IoT and suggest avenues for future research and development that can further enhance the sustainability and functionality of IoT networks. By integrating AI into routing protocols, the potential for improved energy efficiency and extended operational lifespans can be realized, ultimately paving the way for more resilient and sustainable IoT infrastructures.

Keywords:

Internet of things, Energy efficiency, Routing protocols, Artificial intelligence, Reinforcement learning, Machine learning, Scalable and resilient networks

References

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Published

2026-03-22

Issue

Vol. 3 No. 1 (2026): Smart Internet of Things

Section

Articles

How to Cite

Nasef, A. A. ., & Das, A. A. (2026). AI-Optimized Routing Protocols for Energy-Efficient IoT Networks. Smart Internet of Things, 3(1), 32-38. https://doi.org/10.48313/siot.v2i1.268

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