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This paper presents an innovative approach combining triboelectric nanogenerators (TENG) with artificial intelligence for sustainable environmental monitoring. The proposed Adaptive Neural-TENG Integration System (ANTIS) addresses the challenges of power stability and efficiency in environmental sensing applications. By incorporating stream processing analytics through the LARA framework, our system achieves real-time data processing while optimizing energy harvesting efficiency. The methodology integrates neuromorphic computing principles with TENG-based power generation, achieving a 47% improvement in energy harvesting efficiency compared to conventional methods. Experimental results across 14 countries demonstrate the system’s adaptability to various environmental conditions, with an average response time of 3.2ms and 92% accuracy in environmental parameter detection. This research contributes to the development of self-powered, intelligent environmental monitoring systems with potential applications in smart cities and precision agriculture.

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AI-Driven Sustainable Energy Harvesting System with Triboelectric Nanogenerators for Smart Environmental Monitoring

  • 会议论文

  • 发表日期：

    2026年

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  • Akey Sungheetha,
  • John Blake,
  • R. Rajesh Sharma,
  • Sheila Mahapatra,
  • Jeba Singh O

摘要

This paper presents an innovative approach combining triboelectric nanogenerators (TENG) with artificial intelligence for sustainable environmental monitoring. The proposed Adaptive Neural-TENG Integration System (ANTIS) addresses the challenges of power stability and efficiency in environmental sensing applications. By incorporating stream processing analytics through the LARA framework, our system achieves real-time data processing while optimizing energy harvesting efficiency. The methodology integrates neuromorphic computing principles with TENG-based power generation, achieving a 47% improvement in energy harvesting efficiency compared to conventional methods. Experimental results across 14 countries demonstrate the system’s adaptability to various environmental conditions, with an average response time of 3.2ms and 92% accuracy in environmental parameter detection. This research contributes to the development of self-powered, intelligent environmental monitoring systems with potential applications in smart cities and precision agriculture.