Prof. Bhavini Parmar

https://doi.org/10.64823/ijter.2506005

Wireless Sensor Networks (WSNs) are vital for applications such as environmental monitoring and industrial automation, yet their limited energy resources and dynamic environments challenge network longevity and data reliability. Artificial Intelligence (AI) offers effective solutions through adaptive, energy-aware routing strategies. This paper investigates AI-based routing techniques—including deep reinforcement learning, fuzzy logic, swarm intelligence, and hybrid meta-heuristics—for dynamic path optimization in WSNs. These methods enable sensor nodes to make context-aware decisions based on factors like residual energy, link quality, node density, and traffic load. We review current state-of-the-art algorithms, conduct comparative performance analysis, and examine trade-offs in energy efficiency, latency, and computational cost. Simulation results demonstrate that AI-driven routing significantly enhances network lifetime and data throughput over traditional approaches. The findings highlight AI’s potential to drive intelligent, scalable, and energy-efficient routing for next-generation IoT-based WSNs.