Four main ambient energy sources are present in our environment: mechanical energy (vibrations, deformations), thermal energy (temperature gradients or variations), radiant energy (sun, infrared, RF) and chemical energy (chemistry, biochemistry).
These sources are characterized by different power densities (figure 2). Energy Harvesting (EH) from outside sun appears to be the most powerful (even if values given in figure 2 have to be weighted by conversion efficiencies that rarely exceed 20 percent in photovoltaic cells). Unfortunately, solar energy harvesting is not possible in dark areas (near machines, in warehouses). Similarly, it is not possible to harvest energy from thermal gradients when there is no thermal gradient or to harvest vibrations when there is no vibration. As a consequence, the source of ambient energy must be chosen according to the local environment of the WSN node: no universal ambient energy source exists.
Figure 2: Ambient sources power densities before conversion
Figure 2 also shows that 10-100µW is a good order of magnitude for 1cm² or 1cm³-EH output power. Obviously, 10-100µW is not a great amount of power; yet it can be enough for many applications and especially Wireless Sensor Networks.
Autonomous wireless sensor networks (aWSN) & needs
A simple vision of aWSN nodes is presented in figure 3a. Actually, aWSN nodes can be represented as 4 boxes devices: (i) “sensors” box, (ii) “microcontroller (µC)” box, (iii) “radio” box and (iv) “power” box. To power this device by EH, it is necessary to adopt a “global system vision” aimed at reducing power consumption of sensors, µC and radio.
Actually, significant progress has already been accomplished by microcontrollers & RF chips manufacturers (Atmel, Microchip, Texas Instruments…) both for working and standby modes. An example of a typical sensor node’s power consumption is given in figure 3b. 3 typical values can be highlighted:
- 1-5µW: standby mode’s power consumption
- 500µW-1mW: active mode’s power consumption
- 50mW: transmission power peak
Figure 3: (a) aWSN node and (b) sensor node’s power consumption
First of all, this diagram gives a “minimum” EH output power more or less necessary to build viable EH-powered sensor nodes. This limit can be fixed to 1-5µW that corresponds to a good order of magnitude for microprocessor and RF chips standby modes.
Secondly, this diagram highlights the fact that today’s EH devices cannot supply aWSN in a continuous active mode (500µW-1mW power consumption vs 10-100µW for EH output power). Fortunately, thanks to an ultra-low power consumption standby mode, EH-powered aWSN can be developed by adopting an intermittent operation mode as presented in figure 4. Energy is stored in a buffer (a) (capacitor, battery) and used to perform a measurement cycle as soon as enough energy is stored in the buffer (b & c). System then goes back to standby mode (d) waiting for a new measurement cycle.
Figure 4: WSN measurement cycle
Therefore, it is possible to power any application thanks to EH, even the most consumptive one. The main problem is to adapt the measurement cycle frequency to the continuously harvested power. To illustrate possibilities given by EH for aWSN, one needs only to look at the link between power, energy and measurement cycle frequency.
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FYI, Stephane Boisseau and Ghislain Despesse at the CEA-Leti (France) also contributed the article, entitled: "Vibration energy harvesting for wireless sensor networks: Assessments and perspectives".
The link to the article is: http://www.eetimes.com/design/smart-energy-design/4370888/Vibration-energy-harvesting-for-wireless-sensor-networks--Assessments-and-perspectives?pageNumber=2&Ecosystem=smart-energy-design#
Great article indeed; of course Micropelt offers thermoelectric chip-generators (Fig. 6). 100 uW can already be achieved with a temperature difference of just a couple of degrees. Industrial environments offer larger delta-T's, which can create "milliWatts". Thereby also industrial sensors using radio protocols like WHART or ISA100 can be supported by thermal energy harvesting. - Micropelt Germany -
David Patterson, known for his pioneering research that led to RAID, clusters and more, is part of a team at UC Berkeley that recently made its RISC-V processor architecture an open source hardware offering. We talk with Patterson and one of his colleagues behind the effort about the opportunities they see, what new kinds of designs they hope to enable and what it means for today’s commercial processor giants such as Intel, ARM and Imagination Technologies.