Deep Q Network for Intelligent Energy Efficient Routing in Wireless Sensor Network

Deep Q Network (DQN) is widely used to enhance energy-efficient routing in Wireless Sensor Networks (WSNs), improving network lifespan and data transmission reliability. Traditional protocols like LEACH and AODV struggle with energy consumption and scalability, leading to the adoption of AI-driven techniques. As a Reinforcement Learning (RL) approach, DQN enables dynamic route selection based on real-time conditions, reducing energy usage while ensuring reliable connectivity. Unlike static methods, DQN continuously learns and adapts, optimizing throughput and network performance. Several optimization techniques have been integrated with DQN to improve efficiency. NSSROP employs a scoring mechanism based on node energy, density, and connectivity to enhance routing decisions, while Taylor C-SSA uses a two-stage process for Cluster Head (CH) selection and optimal hop selection, achieving low energy consumption and high throughput. Other AI-driven approaches include GA-AOMDV, which optimizes energy use through fitness functions, and DOS-RL, which adapts dynamically to network changes, improving latency and efficiency. Additionally, CNN-based deep learning models enhance IoT security by detecting and mitigating cyber threats. DQN-based routing outperforms traditional methods in packet delivery, energy utilization, and network longevity. However, challenges like data heterogeneity, adversarial attacks, and privacy concerns persist. Future research aims to enhance DQN’s scalability, robustness, and adaptability, further advancing energy-efficient WSN routing.