A Sustainable Forage-Grass-Power Fuel Cell Solution for Edge-Computing Wireless Sensing Processing in Agriculture 4.0 Applications

Johan J. Estrada-López, Javier Vázquez-Castillo, Andrea Castillo-Atoche, Edith Osorio- de-la-Rosa, Julio Heredia-Lozano and Alejandro Castillo-Atoche ()
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Johan J. Estrada-López: Faculty of Mathematics, Autonomous University of Yucatan, Mérida 97000, Mexico
Javier Vázquez-Castillo: Informatics and Networking Department, Universidad Autónoma del Estado de Quintana Roo, Chetumal 77019, Mexico
Andrea Castillo-Atoche: Chemistry and Biochemistry Department, Tecnológico Nacional de México/Instituto Tecnológico de Mérida, Mérida 97118, Mexico
Edith Osorio- de-la-Rosa: Informatics and Networking Department, CONACYT-Universidad Autónoma del Estado de Quintana Roo, Chetumal 77019, Mexico
Julio Heredia-Lozano: Mechatronics Department, Autonomous University of Yucatan, Mérida 97000, Mexico
Alejandro Castillo-Atoche: Mechatronics Department, Autonomous University of Yucatan, Mérida 97000, Mexico

Energies, 2023, vol. 16, issue 7, 1-17

Abstract: Intelligent sensing systems based on the edge-computing paradigm are essential for the implementation of Internet of Things (IoT) and Agriculture 4.0 applications. The development of edge-computing wireless sensing systems is required to improve the sensor’s accuracy in soil and data interpretation. Therefore, measuring and processing data at the edge, rather than sending it back to a data center or the cloud, is still an important issue in wireless sensor networks (WSNs). The challenge under this paradigm is to achieve a sustainable operation of the wireless sensing system powered with alternative renewable energy sources, such as plant microbial fuel cells (PMFCs). Consequently, the motivation of this study is to develop a sustainable forage-grass-power fuel cell solution to power an IoT Long-Range (LoRa) network for soil monitoring. The stenotaphrum secundatum grass plant is used as a microbial fuel cell proof of concept, implemented in a 0.015 m 3 -chamber with carbon plates as electrodes. The BQ25570 integrated circuit is employed to harvest the energy in a 4 F supercapacitor, which achieves a maximum generation capacity of 1.8 mW. The low-cost pH SEN0169 and the SHT10 temperature and humidity sensors are deployed to analyze the soil parameters. Following the edge-computing paradigm, the inverse problem methodology fused with a system identification solution is conducted, correcting the sensor errors due to non-linear hysteresis responses. An energy power management strategy is also programmed in the MSP430FR5994 microcontroller unit, achieving average power consumption of 1.51 mW, ∼19% less than the energy generated by the forage-grass-power fuel cell. Experimental results also demonstrate the energy sustainability capacity achieving a total of 18 consecutive transmissions with the LoRa network without the system’s shutting down.

Keywords: Agriculture 4.0; edge computing; energy harvesting; IoT; plant microbial fuel cells; wireless sensor networks (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
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