AI-Optimized Routing Protocols for Energy-Efficient IoT Networks
DOI:
https://doi.org/10.48313/siot.v2i1.268Keywords:
Internet of things, Energy efficiency, Routing protocols, Artificial intelligence, Reinforcement learning, Machine learning, Scalable and resilient networksAbstract
The rapid growth of connected devices in fields such as smart cities, healthcare, and industrial automation has made energy efficiency a critical concern in Internet of Things (IoT) networks. These devices often operate under power constraints, and traditional routing protocols struggle to meet the specific challenges of IoT environments, which include device mobility, limited processing power, and dynamic network conditions. This research investigates the role of Artificial Intelligence (AI) in optimizing routing protocols to enhance energy efficiency in IoT systems. AI techniques such as Machine Learning (ML) and Reinforcement Learning (RL) enable real-time adaptation of routing paths based on network conditions, energy availability, and data traffic. These methods reduce energy consumption while maintaining reliable communication between devices. The paper reviews existing AI-based routing approaches and presents case studies that demonstrate improved energy management and extended device lifespans. It also addresses challenges like the computational limitations of low-power devices, scalability in large networks, and privacy concerns related to data usage. The findings highlight the potential of AI to significantly improve the sustainability and performance of IoT networks. As IoT adoption continues to grow, intelligent and adaptive routing solutions will be essential for building energy-efficient, scalable, and resilient network infrastructures.
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