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

• Published in: Energies (Basel) Vol. 16; no. 7; p. 2943
• Main Authors: Estrada-López, Johan J., Vázquez-Castillo, Javier, Castillo-Atoche, Andrea, Osorio-de-la-Rosa, Edith, Heredia-Lozano, Julio, Castillo-Atoche, Alejandro
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01-04-2023
• Subjects: Agriculture; Agriculture 4.0; Algorithms; Alternative energy sources; Biochemical fuel cells; Data centers; Data interpretation; Data processing; edge computing; Electric power production; Electrodes; Embedded systems; Energy; Energy consumption; Energy harvesting; Environmental monitoring; Fuel cell industry; Fuel cells; Fuel technology; Grasses; Integrated circuits; Internet of Things; Inverse problems; IoT; Linear programming; Microorganisms; plant microbial fuel cells; Power consumption; Power management; Quantum dots; Renewable energy sources; Semiconductor chips; Sensors; Soil analysis; Soil temperature; Soil testing; Strategic planning (Business); Sustainability; System identification; Wireless sensor networks; Mexico
• ISSN: 1996-1073; 1996-1073
• DOI: 10.3390/en16072943

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 m3-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.