Sensors form the first contact to the physical environment. MEMS technology has made advances in both reduction of size as well as increasing accuracy of the sensed quantity. MEMS-based sensors that monitor temperature, voltage, humidity, photo, vibration, and gas have been part of the onboard sensor for wireless sensor nodes. These sensors result in an analog signal which further has to be amplified in order to be able to process the signal and differentiate it from noise. The external sensors can be configured or controlled by serial interfaces like SPI,I2C.
Energy Requirements and Sources
Usually the major part of energy consumption goes into the analog sensors. For instance, active sensors like metal oxide-based gas sensors consume a large amount power for heating. Next in line comes the communication link which is usually characterized by bursts of packets at regular intervals. Finally, there is the digital processor which does the sensing/conversion of the signal and low-level processing before ordering the data to be transmitted.
A typical node consists of a sensor to capture the external phenomenon, an ADC with signal processing circuitry to convert sensor information into a reliable digital form, and a radio for communication of this data. Typically, energy consumption will be dominated by the radio. Depending on the duty cycle of sensing activity, sometimes the sensor will dominate the energy consumption. Studies quote values between 1uW to 20uW for the amount of energy required for operation of a node. Some techniques like collaborative processing of data jointly by a set of nodes is done to complete computationally intense tasks.
Harvesting Ambient Energy Sources
There are numerous sources of energy in the environment which can serve as a continuous source of energy for wireless sensor nodes over their lifetime. We will concentrate on RF energy, mechanical energy and solar energy harvesting which have been shown in literature as the viable options for powering wireless nodes.
RF energy refers to the energy available through the public telecommunications system. The energy levels provided by GSM at a distance of 30-100m from the base station are between 0.1 to 1mW/m2. This is not sufficient for any viable harvesting mechanism. WLAN, on the other hand, is a couple of orders lower compared to GSM. One possibility to harness this energy from GSM is to use a large area antenna or to have a dedicated source of RF energy. It should be noted here that these levels transmitted should be within the guidelines for the maximum amount permissible.
For harvesting mechanical motion or vibration, electrostatic, piezoelectric, and electromagnetic transduction techniques can be used. In an electrostatic transducer, as the distance between two electrodes of a polarized capacitor changes, this results in a change in voltage. In a piezoelectric transducer, a change in shape of the material results in a voltage change. In case of electromagnetic transduction, movement of magnetic mass causes a change in the flux linkages across the coil.
Thermal generators are based on principle of the Seebeck Effect where two junctions made of dissimilar materials are maintained at two different temperatures. This results in a voltage generation across the open-ended terminals of the junction. Photovoltaic energy is another important source whose efficiencies range from 5% to 30%, depending on the material used.
From Table 1, we find that power per area is reported because the thickness of these devices is typically dominated by the other two dimensions. The power available from these sources is highly dependent on the nodes’ environment. We can on average, assume that 10’s of microwatts of harvested energy to be available . This would put a range for power consumption by the micro sensor nodes to 10-40uW to enable harvesting. Therefore, coupling energy harvesting with some form of energy storage can theoretically extend system operating lifetime indefinitely.
Table1. Characteristics of Energy from Ambient and Harvested power 
The need for a complete platform comprising of analog and digital modules can be solved by moving to a system-on-chip architecture that encompasses the analog and digital peripherals suitable for implementing an autonomous sensor node. Cypress Semiconductors’ Programmable System-on-Chip (PSoC 3/5) processors can be used for such system designs. PSoC Creator is the development environment for the system development. Their low power features, among various other available mixed signal programmable modules, make this architecture popular for the high demanding applications of wireless sensor networks.
Fig3. Schematic Capture of Design in PSoC Creator Environment
Figure 3 represents a simple autonomous system. Three different sensors are interfaced via the programmable gain amplifier. The gain is set according to the different signal level outputs from the sensors.
The amplified signal (So) is time-multiplexed and sampled at a pre-determined rate, then converted by a Delta-Sigma ADC. The data collected is stored on-chip in Flash and then periodically transmitted via wireless communication.
Powering from Energy Harvesting Sources
An integrated boost regulator is ideal for applications where the energy supply is limited or is based on low harvesting sensors. On a PSoC device, for example, the supply can be as low as 0.5V and provide programmable levels between 1.8V to 5.25V at an average load current of 50mA. As discussed before, these boost regulators can be used to draw power from harvested sources, thus making a node self-powered. A rechargeable battery can be used too for supplying and storing harvested energy. The voltage level can be monitored and system operation can be based on the available power. For example, the entire system can go into sleep/hibernate when the battery signals that it is low. Before dropping into sleep while waiting for the battery to recharge, the system sends a signal to the nearby node/network gateway to update its status.
Fig4. Configuration window of Boost Converter for the application
The boost converter is configured by adjusting the values of the capacitors and inductors as shown in Figure 4. The input and output voltage can be specified depending on the requirements of the harvester and the end application. The switching frequency can be controlled based on the allowed size of inductor (inversely proportional to switching frequency) and switching losses (proportional to the frequency).
Features of Low Power Design targeted to vital battery operated systems
Severely energy constrained systems need to have a long battery lifetime due to the inherent difficulty in physically accessing them to replace batteries as well as the sheer scale of deployment. With shrinking sizes of the nodes, this becomes a challenge both in terms of storage and harvesting area available to the node. Low power design is very important in energy constrained mobile applications, and therefore SoCs must be designed to ensure the lowest quiescent energy is consumed during idling states where the node spends most of its time.
Many processors offer multiple operating and low power modes to improve power efficiency. For example, there are four modes available in PSoC3. Active mode is the primary mode where the CPU controls specific modules to be operational. Alternate active powers only specific sub systems while the CPU is halted. Sleep mode is where all modules and CPU are disabled except some supervisory systems like the Watch Dog Timer. Finally, the lowest energy consumption is hibernate mode where all modules including supervisory modules are turned off and consumption is less than 200nA. The device only wakes upon a hardware pin interrupt. These different operating modes are very useful, depending on the state in which the wireless sensor node is operating. This could be dependent on external environment, time of day, or the activity levels/frequency to be sensed. This kind of software-based state machine control is useful for the end user to choose the right mode and easily change power consumption based on the application’s current requirements.
Some other techniques can be used in order to avoid unnecessary power consumption like leaving I/O unused which could be driving a load. Instead, have a default high impedance on such I/O pins at the top level. Also, using a higher frequency than needed for a certain application is sub-optimal. Similarly, use of a PLL with source clock at a low frequency is more optimal than running the main clock frequency at higher cycles.
Wireless Sensor nodes continue to become more sophisticated as well as more computationally intensive. Features like low power as well as integration of modules in a single chip are vital for a rugged and efficient node development.
1. Vullers et al., “Micropower Energy Harvesting”, Solid State Electronics 2009.
2.Kantesh Kudapali, “Using sleep and hibernate modes in PSoC®3” AN66083 : http://www.cypress.com/?docID=26820
3.Course Report,U.R Kamath,“Energy Conversion Circuits for Wireless Autonomous Systems”, Delft University of Technology.
4. Arun Mahodaya et al., “Urban air quality monitoring through large scale wireless sensor network”, CISTUP 2010.
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About the author
Umanath Kamath is a Technical Consultant for Cypress Semiconductors. He works on designing and supporting systems around Cypress’ PSoC 1/3 and Power PSoC range of processors. He holds a Bachelors degree in Electronics and Communication from M.S.Ramaiah Institute of Technology, Bangalore. He enjoys designing analog circuits and systems and sensor interfaces. He can be reached at email@example.com
The radio can be interfaced to PSoC3 via SPI. We do have our transceivers (CYRF7936) which work on 2.4Ghz ISM band and could be useful for short range connectivity with sleep currents below 1uA.
Over the longer range, one can use GSM or wi-fi connectivity to link PSoC3 to the internet for data upload. There is one custom component in PSoC design software Creator for interfacing with WLAN board from Redpine Signals.Alternatively for GSM, one can interface over UART from PSOC3 with off the shelf chip such as SIM300.
Yes conservative data exchange and ad-hoc sharing of computational load while processing/transmitting data over large area could be very beneficial in power critical applications.
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