Application of Microbial Fuel Cells to Power Sensor Networks for Ecological Monitoring

  • Chris Knight
  • Kate Cavanagh
  • Christopher Munnings
  • Tim Moore
  • Ka Yu Cheng
  • Anna H. Kaksonen
Part of the Smart Sensors, Measurement and Instrumentation book series (SSMI, volume 3)


The reliability of wireless computing and in particular nodal networks of wireless sensors has seen increased application of these systems for ecological monitoring. The increase in reliability and decrease of cost enable the establishment of large nodal networks. Each of these nodes require a power source and although batteries are an energy dense solution, if a nodal network is to run for a reasonable length of time, replacement of these batteries becomes time consuming. One solution is energy harvesting, where ambient local energy is used to run the network and charge a secondary cell for use when the ambient energy is unavailable. Microbial fuel cells (MFCs) have quite a long history but only recently have they been considered as a viable source of energy for a practical use. This chapter describes some simple MFC devices and determines their practicality for powering large scale sensor networks. The discussion includes basic design elements, power density, prototyping methods and power output determination. Overall, this chapter reveals that present MFC technologies suffer from very low specific power output, short life time and low efficiencies. These factors make current devices of little practical use, however, there has been a dramatic increase in funding and research into MFC’s leading to a greater understanding of the fundamental science behind their operation. This is driving significant improvements in both the reliability and efficiency of these systems. This, along with a rapid decrease in power requirements of wireless sensor nodes, marks MFC as a promising technology for the powering of wireless sensor networks.


Fuel Cell Sensor Network Sensor Node Wireless Sensor Network Microbial Fuel Cell 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Chris Knight
    • 1
  • Kate Cavanagh
    • 1
  • Christopher Munnings
    • 2
  • Tim Moore
    • 1
  • Ka Yu Cheng
    • 3
  • Anna H. Kaksonen
    • 3
  1. 1.CSIRO Energy TechnologyMayfield WestAustralia
  2. 2.CSIRO Energy TechnologyClaytonAustralia
  3. 3.CSIRO Land and WaterFloreatAustralia

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