AI-Driven Energy Efficient Routing Protocols for Wireless Sensor Networks.

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Balakumar Muniandi
Ketan J. Raut
Pravin G. Gawande
Purushottam Kumar Maurya
Eric Howard


Wireless Sensor Networks (WSNs) are pivotal in enabling diverse applications such as environmental monitoring, industrial automation, and healthcare management. However, the constrained energy resources of sensor nodes pose formidable challenges to the sustainability and efficiency of WSNs. Routing protocols play a pivotal role in optimizing energy consumption by facilitating the efficient transmission of data within the network. Traditional routing protocols, while effective to a certain extent, often lack adaptability to dynamic network conditions and fail to fully exploit the potential of emerging technologies. [1] In response to these challenges, the integration of Artificial Intelligence (AI) techniques into routing protocols has emerged as a promising approach to enhance energy efficiency and prolong network longevity. This paper presents a comprehensive review of AI-driven energy-efficient routing protocols tailored specifically for WSNs. It delves into the various methodologies of AI, including machine learning, evolutionary algorithms, deep learning, and reinforcement learning, and their integration into routing protocols to achieve optimal energy utilization. Machine learning-based approaches leverage historical data to predict traffic patterns and dynamically adjust routing decisions, thereby optimizing energy consumption. Evolutionary algorithms offer a nature-inspired optimization paradigm, evolving routing strategies over time to adapt to changing network conditions. Deep learning techniques enable the extraction of intricate features from sensor data, facilitating more informed routing decisions. Reinforcement learning empowers sensor nodes to autonomously learn and adapt their routing strategies based on feedback from the environment.

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