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RSS FeedsSensors, Vol. 19, Pages 1378: Plant Microbial Fuel Cells-Based Energy Harvester System for Self-powered IoT Applications (Sensors)

 
 

20 march 2019 10:02:37

 
Sensors, Vol. 19, Pages 1378: Plant Microbial Fuel Cells-Based Energy Harvester System for Self-powered IoT Applications (Sensors)
 




The emergence of modern technologies, such as Wireless Sensor Networks (WSNs), the Internet-of-Things (IoT), and Machine-to-Machine (M2M) communications, involves the use of batteries, which pose a serious environmental risk, with billions of batteries disposed of every year. However, the combination of sensors and wireless communication devices is extremely power-hungry. Energy Harvesting (EH) is fundamental in enabling the use of low-power electronic devices that derive their energy from external sources, such as Microbial Fuel Cells (MFC), solar power, thermal and kinetic energy, among others. Plant Microbial Fuel Cell (PMFC) is a prominent clean energy source and a step towards the development of self-powered systems in indoor and outdoor environments. One of the main challenges with PMFCs is the dynamic power supply, dynamic charging rates and low-energy supply. In this paper, a PMFC-based energy harvester system is proposed for the implementation of autonomous self-powered sensor nodes with IoT and cloud-based service communication protocols. The PMFC design is specifically adapted with the proposed EH circuit for the implementation of IoT-WSN based applications. The PMFC-EH system has a maximum power point at 0.71 V, a current density of 5 mA cm − 2 , and a power density of 3.5 mW cm − 2 with a single plant. Considering a sensor node with a current consumption of 0.35 mA, the PMFC-EH green energy system allows a power autonomy for real-time data processing of IoT-based low-power WSN systems.


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34 viewsCategory: Chemistry, Physics
 
Sensors, Vol. 19, Pages 1379: A Distributed Architecture for Human-Drone Teaming: Timing Challenges and Interaction Opportunities (Sensors)
Sensors, Vol. 19, Pages 1381: Consensus-Based Track Association with Multistatic Sensors under a Nested Probabilistic-Numerical Linguistic Environment (Sensors)
 
 
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