Next-Generation Wireless Sensor Networks: EnergyEfficient Architectures and AI-Powered Data Analytics
DOI:
https://doi.org/10.63282/3050-9416.IJAIBDCMS-V3I2P101Keywords:
Wireless Sensor Networks (WSNs), Energy-Efficient Architectures, AI-Powered Data Analytics, Machine Learning Algorithms, Deep Learning Techniques, Energy Harvesting, Low-Power Communication, Federated Learning, Scalability and Security, Edge ComputingAbstract
Next-Generation Wireless Sensor Networks (NG-WSNs) are poised to revolutionize various sectors, from environmental monitoring to industrial automation, by enabling real-time data collection and analysis. However, the success of NG-WSNs hinges on addressing critical challenges such as energy efficiency and data analytics. This paper explores the latest advancements in energy-efficient architectures and AI-powered data analytics for NG-WSNs. We delve into the design of energy-harvesting techniques, low-power communication protocols, and AI algorithms that optimize data processing and decision-making. Through a comprehensive review of existing literature and empirical studies, we highlight the potential and limitations of these technologies. We also propose a novel framework that integrates energy-efficient architectures with AIdriven analytics to enhance the performance and sustainability of NG-WSNs. The paper concludes with a discussion on future research directions and practical implications
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