Artificial intelligence enabled self-powered wireless sensing for smart industry

Abstract

Traditional batteries or external supply powered wireless sensing system are needed to be improved for realizing the development of the smart industry with low-carbon, green and sustainable. This paper proposes and develops a self-powered wireless sensing system for smart industry (SPOT), utilizing a triboelectric nanogenerator (TENG) coupled with an artificial intelligence (AI) transformer model. The SPOT system includes the TENG-based self-powered flexible sensor (SWNG), the wireless aggregate node (WAN), the electromagnetic and TENG hybrid generator (ETCG), and the monitoring and management center with an AI model (MACA). The ETCG serves as a power source for the WAN. The SWNG acquires voltage signals from products on the conveyor belt in the smart industry, powered by the TENG, and transmits the sensor data wirelessly to the MACA via the WAN for processing. The MACA processes the data using the transformer AI model, which not only ensures self-sustainability and long-term stability but also enables intelligent recognition and monitoring of industrial products by their packaging materials, thereby providing precise status information and decision support for the smart industry. The transformer model’s deployment in the MACA has demonstrated robustness and a high classification success rate of up to 97.8 %, efficiently categorizing multiple targets. Additionally, the SWNG and WAN exhibit low power consumption of approximately 80 mW, successfully contributing to the realization of green, low-carbon objectives. The SPOT system significantly enhances the efficiency of product transportation and management within the smart industry and contributes to the advancement of a sustainable, low-carbon, and green smart industry, offering novel technological insights and pathways for future development.