Low power wireless sensor nodes for fluctuation

enhanced sensing

Robert Mingesz, Gergely Vadai and

Zoltan Gingl

University of Szeged, Hungary

http://www.noise.inf.u-szeged.hu/Research/fes/

23rd of September, 2015

Introduction

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Why is gas sensing important?

• Monitoring the quality of the air• Workplace

• Home, intelligent house

• Harmful gases and odors• CO, etc.

• Bacteria, infections

• Air pollution• Industrial facilities

• City traffic

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Gas sensing using Taguchi sensors

• Sensor resistance depends on gas concentration

• Limited selectivityDetecting multiple gases: sensor array/matrix

• Can a single sensor distinguish between different gases?

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Gas sensing using Taguchi sensors

• Can a single sensor distinguish between different gases?

• Fluctuation of the resistance: information source

• Resistance fluctuation depends on:• Material of the sensor

• Temperature

• Gas type and concentration

• Usually analyzed: PSD of the fluctuation

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Fluctuation enhanced gas sensing

L.B. Kish, Y. Li, J.L. Solis, W.H. Marlow, R. Vajtai, C.G. Granqvist, V. Lantto, J.M. Smulko and G. Schmera: "Detecting Harmful Gases Using Fluctuation-Enhanced Sensing With Taguchi Sensors", IEEE SENSORS JOURNAL, vol. 5, no. 4, august 2005

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Carbon nanotube gas sensors

1 0 1 0 0 1 0 0 01 E -1 5

1 E -1 4

1 E -1 3

1 E -1 2

1 E -1 1 C O H

2O

H2S

N2O

S(f)

[V2 /H

z]

F re q u e n c y [H z ]

5 0 p p m

-1 .0 x 1 0 -1 1 0 .0 1 .0 x 1 0 -1 1 2 .0 x 1 0 -1 1-4 .0 x 1 0 -1 2-3 .0 x 1 0 -1 2-2 .0 x 1 0 -1 2-1 .0 x 1 0 -1 2

0 .01 .0 x 1 0 -1 22 .0 x 1 0 -1 23 .0 x 1 0 -1 24 .0 x 1 0 -1 2

H2OH

2SN

2O

PC2

P C 1

C O

5 0 p p m

Á. Kukovecz, D. Molnár, K. Kordás, Z. Gingl et al, “Carbon nanotube based sensors and fluctuation enhanced sensing,” Phys. Status Solidi C, vol. 7, no. 3-4, pp. 1217–1221, 2010

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Bacterial odor sensing

Chang H-C, Kish LB, King MD, Kwan C, Fluctuation-enhanced sensing of bacterium odors. SENSORS AND ACTUATORS, B: CHEMICAL 142:(2), pp. 429-434. (2009)

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Typical FES measurement setup

• Traditional setup: require multiple expensive and bulky instruments

• Compact DAQ systems were designed• Built-in signal conditioning and data acquisition

• Analysis performed on a computer

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Our goal

• Design of a standalone, low-power, intelligent sensor node(software and hardware)

• Optimized battery lifetime

• Wireless communication

• Suitable for • Wireless sensor networks (WSN)

• Internet of things (IoT)

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Design of a wireless sensor module

Block diagram

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Sensor excitation and preamplifier

• Single 3 V supply voltage

• 300 A – 600A supply current

𝑉 R=𝑉 𝑅𝐸𝐹(1+𝑅SENSOR

𝑅G)

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Spectral analysis

• Limited resources: not suitable for FFT

• Spectral analysis:divide the spectrum into distinct frequency regions

• Analogue first-order low pass filters

• Digital spectral reconstruction method

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Low-pass filter bank

Corner frequency

10 Hz 47 kΩ 33 nF

27 Hz 18 kΩ 330 nF

72 Hz 22 kΩ 100 nF

190 Hz 18 kΩ 47 nF

530 Hz 30 kΩ 10 nF

1450 Hz 11 kΩ 10 nF

3700 Hz 13 kΩ 3.3 nF

10270 Hz 4.7 kΩ 3.3 nF

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Selecting the microcontroller

• High precision analogue inputs• Single ADC

• Multiplexed input

• Low power• Low active power consumption

• Low energy modes when idle

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Comparing selected microcontrollers

C8051F410 EFM32 Wonder Gecko

Architecture 8 bit, 8051 32 bit, ARM Cortex M4

ADC 12 bit 12 bit

Real-time clock yes yes

RAM 2368 byte 32 KiB

Flash 32 KiB 256 KiB

DSP instructions no yes

Current consumption

160 µA / MHz 225 µA / MHz

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Wireless modules

Module type Line of sight distance

Current consumption

Data rate

Bluetooth 2.0(BTM-112) 8 m 80 mA 460.8 kbit/s

Bluetooth 4.0 Low Energy(BR-LE4.0-S3A) 100 m 20 mA 460.8 kbit/sZigbee (2.4 GHz)(XBee 802.15.4) 100 m 50 mA 250 kbit/sZigbee (868 MHz)(XBee-PRO XSC) 9.6 km 265 mA 57.6 kbit/sWIFI(ESP8266 module) 100 m 220 mA 1 Mbit/s

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Digital signal processing and pattern recognition

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Spectral reconstruction

• Measured quantities: variance of the filter outputs ()

• First order low pass filters -> there is an overlap between them

• Spectral reconstruction method:approximate the original PSD

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Spectral reconstruction

• σNi2 can be stored at look up table

max*

2

2

1

2

21

2 HzV1

ffy

iiNiN

iipi

max*

2

2

2 HzV1

ffy

iiN

ipi

if i>1, otherwise

fff

f

f

f

f

f

f

i

f

h

hiN d

HzV1

1

1

1max

2

20 2

2

2 max

where

More details: Mingesz R, Vadai G, Gingl Z, Power spectral density estimation for wireless fluctuation enhanced gas sensor nodes FLUCTUATION AND NOISE LETTERS 13:(2) Paper N°1450011. 14 p. (2014)

• Designed for 1/f-like noises

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Example for spectral reconstruction

1/f noise 1/f0.8 noise

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Effect of filter number

• Resolution limited by the order of the filter

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Pattern recognition methods

• PCA: maximum variance at principal components

• LDA: maximum distance between classesReconstructed PSD (y) – 8 points Whole spectrum – 2000 points

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Is spectral reconstruction required?

Variance of filters Reconstructed PSD (y)

• Variances of filter outputs: contain all information

• Spectral reconstruction: prescaling of the data

• Comparison of raw data and reconstructed data:

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Implementation of gas identification

• Teaching phase and calibration before installation

• Pattern recognition:only final phase is performed by the node

• Decision by the sensor node

Alternative:

• Sensor sends the RMS values to the cloud server

• Server performs the pattern recognition and decision

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Optimizing power consumption

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Analogue frontend energy consumption

• Low noise and low power amplifier

• e.g. LTC6078 (16nV/√Hz noise, 54 μA supply curr.)

Circuit current [A]

power [mW]

Sensor excitation 220 0,66Preamplifier 370 1,11Low pass filters 30 0,09Total 620 1,86

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Optimizing microcontroller energy consumption

• Clock frequency• Too fast: extra wait states may be introduced

• Too slow: static consumption will be dominant

• Used peripherals• Switch off non-used peripherals

• Energy mode• Using the lowest possible while maintaining

functionality(performing ADC, real-time clock)

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Microcontroller energy consumption• Recording time: 10 s

• Repetition rate 10 minute

• 10k samples per filter for RMS calculation (1kS/s)

• Calculating: mean and variance C8051F410 ARM W. GeckoActive power 3.6 mW 1.9 mW

Idle power 9 µW 3.9 µW

Average power 68 µW 35.4 µW

With analogue front. 100 µW 69 µW

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Energy harvesting

• Suitable for low power devices

• Harvested power: not constant

• Possibilities:• Light (solar cells)

• Vibration/motor

• Thermal

• RF

• Harvested power: 0.1 µW/cm – 100 µW/cm

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Conclusion

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Conclusions

• Complete solution for FES gas sensing

• High accuracy and sensitivity

• Sensor node:• Low power (average consumption 100 µW)

• Low cost ($30 component cost)

• Compact (75mm x 35mm)

• General purpose applications

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Applications

• FES gas sensing• gas detecting

• bacterial odor sensing

• replacement of bulky and expensive setups

• Supports using noise as a diagnostic tool• electronic device degradation

• General purpose power spectrum estimation

• Suitable for WSN and IoT

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Acknowledgments

• The presentation is supported by the European Union and co-funded by the European Social Fund. Project title: “Telemedicine-focused research activities on the field of Mathematics, Informatics and Medical sciences” Project number: TÁMOP-4.2.2.A-11/1/KONV-2012-0073.

• This research was supported by the European Union and the State of Hungary, co-financed by the European Social Fund in the framework of TÁMOP 4.2.4. A/2-11-1-2012-0001 ‘National Excellence Program’.

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Thank you for your attention!More information:http://www.noise.inf.u-szeged.hu/Research/fes/