Ultra Low Power Wireless Weather Station

Rafael Lajara, Jorge Alberola, José Pelegrí, Tomás Sogorb, J.Vicente Llario Sensors and Magnetism Group (GSYM) Department of Electronic Engineering

Universidad Politécnica of Valencia, SPAIN {jolaviz, joralllu}@doctor.upv.es, {jpelegri, tsogorb, vllario}@eln.upv.es

Abstract

Currently the appearance of really low power

wireless transceivers at very low prices is motivating the development of many wireless applications out of the industrial field, which up to now turn to be large in size and expensive. We present the design of a tiny and low cost Wireless Weather Station to measure accurate temperature (±0.1ºC), relative humidity (±3%), light intensity and atmospheric pressure (±0.8hPa). These direct climatic variables and others indirectly attainable, like the dew-point, wind chill, etc, are readable through a web page. The chosen sensors are factory calibrated and have a digital interface. The Weather Sensor Nodes are able to achieve ultra low power consumption (40µA average), allowing a single supercapacitor to power them for 52 days. 1. Introduction & related work

Nowadays a climate change is being discussed. Its

analysis requires the deep knowledge of the weather variations, by which is of vital importance to monitor certain parameters like temperature, relative humidity, and atmospheric pressure, among others like CO2.

By the other hand, some economic sectors like tourism and agriculture strongly depends on the weather forecast and the measurement of the previously mentioned parameters. Consequently, the knowledge of the weather is waking-up the interest of some local administrations like city halls and companies like beach hotels or controlled microclimates like several green houses.

There are many commercial scientific Weather Stations, like the ones from Oregon, Davis or Vaisala as example. Some of them are wireless, accurate and

Figure 1. Conceptual view of the system.

autonomous but usually turn to be very expensive, large in size, and have large power consumption.

Wireless Sensor Networks (WSNs) based on IEEE

802.15.4 [1] have been designed for very low power and low voltage operation with a low cost and low effective data rate. They are being evaluated on many application areas, ranging from the animal’s environment analysis [2], [3] and the Earth’s environment analysis (climate change, volcanoes and Earthquakes [4]) to medical and clinical research [5], among many others. Thus, an application like the Wireless Weather Station can take advantage of all the benefits that the WSNs provide.

2. System description A conceptual view of the system is shown in Figure

1. A really small and autonomous wireless node transmits accurate information about several parameters of weather. These are temperature, relative humidity, atmospheric pressure and light intensity. A constantly powered base station collects the data and retransmits them through cable to a host computer. The host stores the data and creates several files which can be acceded through a web server.

Next sections will discuss in detail the system architecture and the web interface.

2007 International Conference on Sensor Technologies and Applications

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2007 International Conference on Sensor Technologies and Applications

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Figure 2. Block diagram of the autonomous

Weather Sensor Node.

2.1 Hardware This part of the paper is focused on the Weather

Sensor Node side, since it is the important one in charge of getting and transmitting the weather variables, whereas the Base Station simply acts as a gateway. Figure 2 shows all the internal blocks of the Weather Sensor Node, which are described in the next paragraphs.

The autonomous Weather Sensor Node uses an environmental energy source designed for this application based on a multiple energy source architecture [6], which uses solar energy to replenish a non degradable and fast rechargeable supercapacitor instead of the conventional and less durable lithium battery.

As the power consumption is a design key on a wireless Weather Sensor Node, it has been chosen the CC2420 RF transceiver [7], which is IEEE 802.15.4 compliant. This standard defines the physical and the MAC layer of the OSI seven-layer model. By using spread spectrum (DSSS) with O-QPSK @ 2.4GHz modulation the IEEE 802.15.4 allows a 250kbps data rate, a star or peer-to-peer network topology and a range distance around 120m @ 0dBm in free space [8]. The transceiver can be managed through an SPI interface.

The processing unit of the Weather Sensor Node is a PIC18LF4620 [9] microcontroller based on the “nanoWatt” technology from Microchip Inc., with several low power modes and multiple peripheral units.

The sensors have been selected as accurate as the actual market allows for ultra low power and embedded applications. They all were chosen with a digital interface built-in to minimize the number of components and avoid calibration; thus shrinking the board, reducing the power consumption and achieving design simplicity. The Wireless Weather Station

measures temperature, relative humidity, light intensity and atmospheric pressure. Each sensor has a different interface as it is shown in Figure 2.

The temperature sensor TSIC506F [10] is ±0.1ºC accurate and uses an IST-owned 1 ZACWire protocol.

The humidity sensor SHT11 [11] is ±3% accurate in a wide range. Its measures should be temperature compensated for temperatures significantly different from 25ºC by using the equation 1, where RHtrue is the compensated relative humidity, TºC is the temperature, t1 and t2 are constant coefficients, SORH is the digital value given by the sensor’s ADC, and RHlinear is the relative humidity without compensation. In addition, the dew-point [12] can be calculated by using the temperature and relative humidity values, as it can be seen in equations 2 and 3, where a, b and c are constants. This sensor uses a Sensirion-owned serial protocol similar to I2C.

( ) ( ) linearRHRHSO2t1t25CºTtrueRH +⋅+⋅−= (1)

( )

++

−=

cCºT

·bCºT

a

2trueRHlogH (2)

HbH·cDP−

= (3)

The light sensor ISL29001 [13] contains two

photodiodes. One of the photodiodes is sensitive to visible and infrared light while the other diode is used for temperature compensation (leakage current cancellation) and IR rejection. It is 15 bit effective resolution and directly measures lux in a 0.3 to 10,000 range. It uses an I2C interface.

The pressure sensor SMD500 [14] is ±0.8hPa accurate and temperature compensated. Altitude also can be calculated by using equation 4, where p is the true pressure and p0 is the pressure at sea level. It has been designed to be connected directly to a microcontroller via the I2C interface.

−⋅=

5.2551

opp144330Altitude (4)

Some of the most important characteristics of the

used sensors are summarized in the Table 1.

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Table 1. Main characteristics of the sensors used.

1 In a range of 40ºC and Vcc = [4.5V,5.5V]. 2 Minimum, typical, maximum @ 25ºC and Vcc = 5V. 3 Sleeping, average consumption, measuring.

2.2 Software

The next two points describe the most important

strategies followed in order to reduce the power consumption by software and display the weather parameters on a computer wherever it is located.

2.2.1. Weather Sensor Node. The tasks of the

Sensor Node can be summarized in waking-up, reading sensors, building the data frame, sending-out the data and sleeping most of the time. Wireless Sensor Networks use to work on active-sleep schedule [15]. Thus, the power consumption can be widely reduced by minimizing the active time. Very low duty cycles approach the average consumption to the sleep mode consumption. Therefore, this is the key design that has been taken into account on the Weather Sensor Node program.

Since each sensor has a different response time, an efficient ordering of the sensor’s activation reduces the active time and therefore the power consumption. Looking at Table 1, the optimal schedule turns to be the one in Figure 3.

Initially all the sensors are switched off and the ports of the microcontroller are configured as Hi-Z, which also reduce the consumption. After the microcontroller’s wake-up, the ports are conveniently configured for the communication and all the sensors go into its on state. When the microcontroller goes into

the active mode, the sensors go being read at the end of its response time and stored into the microcontroller’s RAM memory. Once the digital value has been got, the sensor is turned off and its port lines come back to Hi-Z.

The present Wireless Weather Station just requires a star network topology. Since the data flow is unidirectional (from Sensor Node to Base Station) and the Base Station is perpetually powered, it is not needed any synchronization method but only a medium access protocol like CSMA/CA, which is used under the same conditions and as a part of the WiseMAC [16]. Thus, when the last sensor delivers its readout, the microcontroller goes into the deep sleep mode. In this mode, it is used the low precise watch-dog clock but the microcontroller consumes less than 2 µA.

When ten samples of each sensor have been acquired, a frame is built with an identifying field, the battery voltage level and the weather parameters.

Then the transceiver is turned on, the whole frame is sent out to the Base Station and the transceiver is turned back off, thus completing the cycle. The internal weak pull-up resistors from the SPI lines are only active during the microcontroller-transceiver communication, which saves over 500µA DC current.

Loop{ Config.Ports ( in-out )

Sensor TSIC506F SHT11 ISL29001 SMD500 Parameter

Temp.

(ºC) Relative

Humidity (%)

Temp. (ºC)

Illuminance

(lux)

Atmos. Pressure

(hPa)

Temp. (ºC)

Alture (m)

Accuracy@25ºC ± 0.1 1 ± 3 ± 0.4 ? ± 0.8..2.5 ± 1.5 ? Range -10 ...+60 0..100% -40..123.8 0.3..10000 300..

1100 -40..+85 -20..+60

+9000.. -500

Resolution 0.034 0.03% 0.01 3-15 counts 0.06 0.03

0.5m, 0.25m

Effective Bits 11 bits 8,12 bits 12,14 bits 15,16 bits 16 bits Repeatability -- ± 0.1% ± 0.1ºC -- --

Power Supply DC 2.97V-5.5V 2.4V-5.5V 2.5V-3.3V 2.2V-3.6V Supply Current

@25ºC 30,45,80µA2 0.3,28,550µA3 1µA 5µA,10µA(1sample/s),0.1µA(sleep)

Measure time 0.1 s 55ms 200ms 34ms @ 32.768 kHz ± 3% Response time -- 4s 5s-30s 100ms --

Interface 1 ZACWire Similar to I2C I2C I2C

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Init.Sensors ( ) Delay 34ms Measure.Pressure ( ) Delay 21ms Measure.Humidity ( ) Delay 10ms Measure.Temperature ( ) Delay 55ms Measure.Light ( ) Config.Ports ( Hi-Z ) If measures = 10 then { BuildPacket ( ) Transceiver ( on ) SendPacket ( ) Transceiver ( off ) } Sleep ( 32,768s ) }

Figure 3. Schedule of the Sensor’s readout. The order optimizes the power consumption.

2.2.2. Web interface. When the Base Station receives a frame from a Weather Sensor Node, the data are retransmitted through the serial cable (RS-232) to a computer (host) as soon as they are collected. The computer acts as a web server to allow the remote visualization of the weather sensed variables through internet. The host allocates two software modules; one is a LabVIEW program to manage the data and the other one is an Apache web server to provide the data.

The first software module manages the serial port to collect the data frame. Then the program splits the frame into the temperature, humidity, light and pressure fields. These are the direct measured parameters which are processed and compensated. Nevertheless other indirect parameters can be obtained from the direct ones, like the dew-point (equation 2 and 3) and the heat index (equation 5).

++++++= 2H6c2T5cTH4cH3cT2c1cHI

2H2T9c2TH8cH2T7c +++ (5)

Where the HI is the Heat Index, c1 to c9 are constants, T is the temperature and H is the relative humidity.

The first software module generates several files that provide information about the weather parameters through internet. These files are issued by an Apache web server. The web server and the generated files are the second software module. There are different ways

Figure 4. User-friendly graphical interface showing variations of the climate parameters. It is part of the accessible web interface.

to access the information; the first one is a PNG (Portable Network Graphics) file which shows the recent weather evolution and the actual values. The second one and the third way are using a javascript script, which can be easily inserted in other web pages and an XML (eXtensible Markup Language) file in RSS (Really Simple Syndication) format; both shows the instantaneous values. Finally, the last method is by using a database; this database contains the historical values of the weather variables measured. The aim is not only to show the data but to be a service provider to other web pages.

3. Results The Wireless Weather Station has been

autonomously working for months at the university’s roof. It is still sending out data to our web server, which hosts an accessible web page at http://www.gsym.upv.es/estacionClima.html, see Figure 4.

Since a Weather Station does not require high duty cycles it was chosen a 1% cycle with a sampling time of 30 seconds, which gives a screen refresh of 5 minutes. Thus, the current average consumption in the Weather Sensor Node is really ultra low (Iav = 40µA), closer to the one in the sleeping mode (Ioff = 15µA @ toff = 32.768s) than to the active mode (Ion = 7.4mA @ ton = 0.29s) or the transmitting mode (Itx = 25mA @ ttx = 0.012s). This can be calculated by using the equation 6.

( )

( ) txoffon

txtxoffoffononav ttt10

ItItIt10I

++⋅+⋅+⋅

= (6)

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Figure 5. Current consumption in the Weather Sensor Node at 3.3V supply voltage. The microcontroller wakes-up every second and takes a sample from each sensor. After getting 10 samples, the whole data frame is sent-out.

Figure 5 gives an idea of the current levels

consumed and how is a full cycle. Nevertheless, it corresponds to a 9% duty cycle with 1 second of sampling time, which was changed to be able to measure the short and high peak currents with enough temporal resolution. This is due to the limitations imposed by the acquisition equipment.

In normal conditions, the power consumption is such a low that with 1% duty cycle (40µA), a single supercapacitor is able to hold-up the system power supply during 52 days without any recharge from the sun’s energy, according to the equation 7 [17].

( )( ) VVI

VVCt

minmaxload

2min

2max

+⋅−⋅

= (7)

Where C is the nominal capacitance of the

supercapacitor in Farads, Iload is the average current delivered to the load, Vmax and Vmin are the maximum and minimum thresholds voltages for proper working, and t is the held-up time. The expected time has been calculated with 100F, 40µA, 2.7V and 0.9V.

In addition, the size of the Weather Sensor Node board has turned to be very small, only 4.4 x 6.6 x 1 cm as can be seen in Figure 6. The achievable or covering distance is around 120 meters per node in free space, although the obstacles diminish it quite a lot.

4. Conclusions & future work A prototype of autonomous Wireless Weather

Station has been designed, developed and tested. It is continuously delivering accurate temperature, relative humidity, atmospheric pressure and light intensity since months ago.

Figure 6. Picture of the Weather Sensor Node. It can be seen the location of the 4 sensors, the microcontroller and the transceiver. The programming connector is at the bottom side.

This application does not require a synchronization method. That is why all the components except the watch dog timer (to wake-up) can be taken into the deepest sleep mode, which saves a huge amount of energy. Usually, the watch dog (RC oscillator) is not suitable to measure precise time, but in this case it is not necessary to keep in mind the count during the sleep period. So it is the key to achieve an ultra low power mode.

The WSN’s philosophy perfectly adapts to the application of the Weather Station, where very low duty cycles are allowed.

The board can be shrunk even more by using both layers instead of only one, probably achieving more or less the size of the PICZEE module, which is only 2 x 5.4 x 0.5 cm.

The barometric pressure sensor also can be interesting to research about regional weather forecast models. In addition, it is being planned to include a rain sensor based on piezoelectric sensors as it is described in [18], which would increase not too much neither the power consumption nor the size of the Weather Station. Moreover it is also interesting to measure wind speed based on the cooling down of a SHT11 temperature sensor due to the breeze wind. Thus, by using the internal heating system built in this sensor, it is possible to heat up the sensor above the ambient temperature and measure the time spent to cool down the sensor 1ºC. Thus, one could find a relation between wind speed and temperature variation, as [19] shows. At the same time this would let us to measure the wind chill. Finally, taking into account that the location of our Weather Station is close to the

Humidit

Temp.

Light

Pressure

µController

Transceiver

Antenna

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beach, it would be useful to include an ultraviolet radiation sensor.

5. Acknowledgments This work was supported by the I+D+i program of

the Generalitat Valenciana, R&D Project GV05/043, and Vicerrectorado of investigation, development and innovation, UPV 20070323.

6. References

[1] IEEE 802.15.4, “Wireless Medium Access Control (MAC) and Physical Layer (PHY) Specification for Low-Rate Wireless Personal Area Networks”. October 2003. ISBN: 0-7381-3677-5. [2] C.Noda, J.Fernández, C. Pérez-Penichet, and E.Altshuler. “Measuring activity in Ant Colonies”. Review of Scientific Instrumentation, 77, December 2006. [3] Pei Zhang, Chistopher M. Sadler, Stephen A.Lyon, and Margaret Martonosi. “Hardware Design Experiencies in ZebraNet”. Conference on Embedded Network Systems, pp.227-238, Nov-2004. [4] Geoffrey Wemer-Allen, Jeff Johnson, Mario Ruid, Jonathan Lees, and Matt Welsh. “Monitoring Volcanic Eruptions with a Wireless Sensor Network”. Proceedings of the Second European Workshop on Wireless Sensor Networks, pp.108-120, Feb-2005. [5] Park, C.; Liu, J.; Chou, P.H. “Eco: an ultra-compact low-power wireless sensor node for real-time motion monitoring”. Information Proc. in Sensor Networks, 2005. Fourth International Symposium, 15 April 2005, pp. 398- 403. [6] Xiaofan Jiang, Joseph Polastre, and David Culler. “Perpetual Environmentally Powered Sensor Networks”, In Proceedings of IPSN/SPOTS, Los Angeles, CA, April 25-27, 2005. [7] Chipcon Inc. CC2420 datasheet. http://www.chipcon.com

[8] Flexipanel. Pixie DS481-11 Datasheet. http://www.FlexiPanel.com [9] Microchip Inc. PIC18LF4620 datasheet. http://www.microchip.com [10] Innovative Sensor Technology (ISL). ISL TSic506F datasheet. http://www.ist-ag.com [11] Sensirion. SHT1x datasheet. http://www.sensirion.com [12] Sensirion. Application Note Dew-point calculation. http://www.sensirion.com [13] Intersil. ISL 29001 datasheet. http://www.intersil.com [14] Bosh Sensortec GmbH. SMD500 datasheet. http://www.bosch-sensortec.com [15] Wei Ye; Heidemann, J.; Estrin, D. “An energy-efficient MAC protocol for wireless sensor networks”. INFOCOM 2002. Twenty-First Annual Joint Conference of the IEEE Computer and Communications Societies. Proceedings. IEEE, 2002 pp. 1567- 1576 vol.3. [16] El-Hoiydi, A.; Decotignie, J.-D. “WiseMAC: an ultra low power MAC protocol for the downlink of infrastructure wireless sensor networks”. Computers and Communications, 2004. Proceedings. ISCC 2004. Ninth International Symposium on, Vol.1, Iss., 28 June-1 July 2004, pp. 244- 251 Vol.1 [17] Power Stor, Cooper Bussmann. Application Note: “Design Considerations In Selecting Aerogel Supercapacitors” [18] http://www.cooperbussmann.com/3/PowerStor.html Atte Salmi and Jouni Ikonen. “Piezoelectric precipitation sensor from Vaisala”. WMO Technical Conference on Meteorological and Environmental Instruments and Methods of Observation, Mayo 2005. [19] Kofi A. A. Makinwa, Johan H. Huijsing. “A Smart CMOS Wind Sensor”. IEEE International Solid-State Circuits Conference 2002.

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