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Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
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AI-Driven Energy-Efficient Routing in IoT-Based Wireless Sensor Networks: A Comprehensive Review.
Sumendra Thakur1, Nurul I Sarkar1, Sira Yongchareon1
1Computer and Information Sciences, Auckland University of Technology, Auckland 1010, New Zealand.
Sensors (Basel, Switzerland)
|December 31, 2025
Summary
This review explores AI-enhanced routing for Wireless Sensor Networks (WSNs) in the Internet of Things (IoT). It highlights AI
Area of Science:
- Computer Science
- Networking
- Artificial Intelligence
Background:
- Wireless Sensor Networks (WSNs) face challenges in the Internet of Things (IoT) due to network complexity and mobile nodes.
- Traditional routing struggles with data congestion and uneven energy consumption in WSN-IoT systems.
- Optimized routing strategies are crucial for sustainable WSN-IoT performance.
Purpose of the Study:
- To comprehensively review conventional and AI-enhanced energy-efficient routing techniques for WSN-IoT.
- To analyze the limitations and performance metrics of existing routing solutions.
- To identify research challenges and future directions in intelligent WSN-IoT routing.
Main Methods:
- Systematic literature review of over 100 studies on WSN-IoT routing.
- Categorization of techniques including metaheuristics, machine learning, reinforcement learning, and AI-based cross-layer methods.
- Analysis of performance metrics like scalability, energy efficiency, throughput, and packet delivery.
Main Results:
- AI-enhanced methods show promise in overcoming limitations of traditional routing in WSN-IoT.
- Diverse AI approaches offer potential for improved network performance and energy efficiency.
- Key limitations identified include scalability issues and dynamic topology adaptation.
Conclusions:
- A structured foundation is provided for advancing intelligent, energy-conscious routing in WSN-IoT.
- Future research should focus on addressing identified challenges for next-generation IoT networks.
- AI integration is pivotal for enhancing the efficiency and sustainability of WSN-IoT systems.
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