AI-based routing algorithms improve energy efficiency, latency, and data reliability in wireless sensor networks
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View abstract on PubMed
Summary
This summary is machine-generated.This study introduces a hybrid Artificial Intelligence (AI) routing framework for Wireless Sensor Networks (WSNs). The AI approach enhances network performance by dynamically adapting to changing conditions, improving reliability and energy efficiency.
Area Of Science
- Computer Science
- Electrical Engineering
- Artificial Intelligence
Background
- Wireless Sensor Networks (WSNs) face challenges in dynamic environments with limited resources.
- Traditional routing protocols often lack adaptability to real-time network changes.
- Static or standalone algorithms struggle to optimize WSN performance effectively.
Purpose Of The Study
- To propose a novel modular AI-based routing framework for WSNs.
- To integrate multiple AI techniques for adaptive and optimized routing.
- To enhance WSN performance metrics like packet delivery, latency, and energy efficiency.
Main Methods
- Developed a modular framework integrating Reinforcement Learning (RL), Supervised Learning, Genetic Algorithms (GA), and Particle Swarm Optimization (PSO).
- Implemented a structured decision-making pipeline for dynamic adaptation to network topology, traffic, and energy.
- Utilized MATLAB R2021b for simulations to evaluate the framework's performance.
Main Results
- The proposed AI framework demonstrated significant improvements in packet delivery ratio and energy efficiency.
- End-to-end latency was reduced compared to traditional routing protocols.
- The hybrid AI approach showed enhanced adaptability in dynamic WSN environments.
Conclusions
- Hybrid AI strategies offer a promising approach for optimizing WSN routing.
- The modular framework provides a foundation for future real-world WSN implementations.
- Addressing challenges like scalability, resource usage, and security is crucial for deployment.
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