hardware of the sensor network -- sensors and peripheral hardware -- lin gu sept 8, 2003

Hardware of the sensor network-- Sensors and peripheral hardware

-- Lin GuSept 8, 2003

Outline

• Sensors and Actuators• Definition and examples

• Supporting circuit• Hardware compatibility

• Drivers• Software interface

• Put them together – Sample design• How to integrate a sensor and write a driver for it

Sensors and Actuators

• Transducer: “A device which transforms energy from one domain (magnetic, thermal, mechanical, optical, chemical, electrical) into another”

• Sensors: “devices which monitor a parameter of a system, hopefully without disturbing that parameter.”

• Actuators: “devices which impose a state on a system, hopefully independent of the load applied to them”

Sensor?

Actuator?

Sensors and Actuators• Example of sensors

– Magnetic sensors• Honeywell’s HMC/HMR magnetometers

– Photo sensors• Clairex: CL9P4L

– Temperature sensors• Panasonic ERT-J1VR103J

– Accelerometers• Analog Devices: ADXL202JE

– Motion sensors• Advantaca’s MIR sensors

• "Without disturbing that parameter" implies that the sensors must be small and low-power devices in order to reduce energy exchange.

» Sensors: “devices which monitor a parameter of a system, hopefully without disturbing that parameter.”


• Examples of Actuators– Motor (impose a torque)– Pumps (impose pressure or fluid velocity)

• Actuators may be powerful, large, and complicated

» Actuators: “devices which impose a state on a system, hopefully independent of the load applied to them”


• Properties of sensors– Range

• Example – HMC1053: +/-6 Gauss

• What decides range?– Saturated point– Noise

– Accuracy• Measure of error and uncertainty• HMC1002: 0.05% (Hysteresis)

– Repeatability• HMC1002: 0.05%

– Linearity• HMC1002: 0.1% (Best fit straight line +/- 1 Gauss)


• Properties of sensors– Sensitivity

• How output reflects input?• HMC1053: 1mV/V/gauss

– Efficiency• Ratio of the output power to the input power• Important for actuators

– Resolution• Determined by sensitivity and noise level• Measuring noise level

– SNR– Noise floor (High noise floor does not mean “useless”)

• HMC1002: 27uGauss


– Response time• How fast the output reaches a fraction of the

expected signal level

– Overshoot• How much does the output signal go beyond the

expected signal level

– Drift and stability• How the output signal varies slowly compared to

time

– Offset• The output when there is no input


• Properties of sensors– Packaging

• Example – HMC1053: 16-PIN LCC packaging

– Property of the circuit• Load of the circuit• Power drain

Sensors and Actuators• What’s the implication to the

application/middleware?– Select the suitable sensors for the target

application– Imposing three general requirements to the

application/middleware


• Requirement 1: sensor part– Application designer must be aware of the

properties of sensors• How to handle imperfect sensor devices

– Error, offset, drift, …– Repeatability– Sensors vary

• Requirement2: sensor reading– Application designers must be aware of the errors

introduced by the mote hardware?• The effect of ADC converting• The effect of signal amplification/distortion


• Requirement3: interaction– The application designer must be aware of the

interaction of multiple sensors and the mote hardware

• How to avoid race conditions on hardware wires and software event handlers?

• How to control the mutual interaction of various hardware components?

– Example: radio component increases the noise floor of the motion sensor

• Can we make the sensors complement with each other to achieve better sensing?


• Example of sensors: motion sensor using MIR– TWR-ISM-002

• Output (Advantaca’s)– Analog– Digital

• Packaging– 51-pin connector

• Fine tuned receiving gate can potentially detect moving objects at a certain distance

• Is it a typical sensor?


• Post-processing– Process the sensor reading to make it useful to

the application– The complexity varies from simple threshold

algorithm to full-fledged signal processing and pattern recognition


• Raw reading of an MIR sensor in a quiet environment– The beginning period represents some

unknown noise, possibly due to the positioning of the sensor

I ndoor test , qui et envi ronment wi thout mot i on

0

100

200

300

400

500

1 80 159 238 317 396 475 554 633 712 791 870 949 1028

7. I ndoorQui et


• Raw reading of an MIR sensor as a person walked by– The all-zero period is due to unreliable UART interface

used to collect the reading and can be ignored.

39. 64Hz. Mi l ton. sb. MI R. DanWal k. 3

0

20

40

60

80

100

120

1 13 25 37 49 61 73 85 97 109 121 133 145 157 169 181 193 205 217 229 241 253 265 277

39. 64Hz. Mi l ton. sb. MI R. DanWal k. 3


• Use a post-processing algorithm to transform the raw reading to what the application needs– The application needs to know whether

the motion of interest is detected– The post processing needs to filter out

noise whenever possible

Sensors and Actuators• Post-processing algorithms

– “Moving variance” algorithm• Adapt to the environment dynamically but requires

more computation• Designed by OSU• The basic idea is to track the changes of a statistic

variable• To avoid the complexity of moving variance

computation, another statistics variable was used for mote-based moving object detection

• If “adapting” feature is not required, offline modeling and online detection can be combined


• More on “Moving variance” algorithm– Calculate the variance of the samples– Example: Suppose the sensor data in a “quiet” environment is as

follows

• Mean: 3• Variance: 2.18

» This is my interpretation of OSU’s algorithm. I have not seen their code or detailed description of it.

0

1

2

3

4

5

6

1 2 3 4 5 6 7 8 9 10 11 12

Ser i es1


• More on “Moving variance” algorithm– Continuously calculate the variance of the recent sampling period– When the variance changes, fire a “positive detection” event

• Mean: 3• Variance: 4.9

» This is my interpretation of OSU’s algorithm. I have not seen their code or detailed description of it.

0

1

2

3

4

5

6

7

1 2 3 4 5 6 7 8 9 10 11 12

Ser i es1


• More on “Moving variance” algorithm– Overall, the waveform looks like

– On the right half, a “positive” detection event is fired

0

1

2

3

4

5

6

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1 3 5 7 9 11 13 15 17 19 21 23

Seri es1

Motion!


• More on “Moving variance” algorithm– This technique can be applied to other statistical

variables• Mean• Standard deviation• MIN, MAX

– The main idea is to use the statistics in a recent sampling period to

• detect “phase change”• filter out burst noise reading

Sensors and Actuators• Another post-processing algorithm

– “Recently convincingly active” algorithm• Record a history of about half a second. A positive

(motion detected) event is fired when there have been more than a number of significant jumps in the waveform in the history period.

• Simple, fast, and quite reliable• Needs to be tuned in the environment. E.g., we need to

define the threshold for “significant jumps”.


• Other sophisticated sensors– GPS sensors– Ultrasonic transceiver

• Sensor network hardware that does not fall into the transducer category– RFID– Cots-Bots


• More on RFID– Typical configuration

– Application: ID based intelligent control• Such as access control, baggage ID, object tracking…


• More on RFID– What is RFID?

• Radio communication capability• Processing capability• Memory• Optionally, power supply

– What makes RFID unique and useful?• Flexible• Low-cost

– Compare RFID with motes• Difference? If yes, why?• Will they merge to be the same class of hardware?


• More on RFID– What is behind RFID, motes and many

other small intelligent devices?• We need to integrate the computation

capability into the physical world to improve the quality of life, and to facilitate applications that were not feasible before

• To make this possible, we need a large number of computing devices. This mandates that they have very low cost and consume little power

• For low-cost and low-power devices to do sophisticated work, they need communication capability

• For a large number of devices to communicate, radio communication is probably the only feasible option

• Finally, the current technology has enabled all these to happen

To incorporate intelligence in the physical world

Large number ofcomputing devices

Low-cost devices

Wireless communication


• More on RFID– The integration of low-cost computation and low-cost

radio communication enables not only RFID, but also other RF-* technologies. So RFID represents a broad spectrum of innovative applications. Sooner or later, people may create RF-CD (with super copyright protection), RF-liquor (which evaporates immediately when reaching an RFID indicating a non-adult), RF-book (which applies errata correction automatically), RF-map (which highlights the current location), RF-stick (which reminds a blind man about a bump ahead)…


• More on RFID– Challenges may include

• how to name?– For RFID, how to assign the ID?

– For RF-*, how to discover and address each other, including “master devices”?

• How to coordinate?• How to provide both security and privacy?

Supporting circuit• Sensors may need supporting circuit to

integrate with other sensors and the target application platform

• Similar to an “adaptor” on PCs– Makes the electrical features of the computer and

the I/O device compatible– Provides control and data transfer interface to the

I/O device

• This talk focus on the Berkeley mote platform

Supporting circuit

• Berkeley mote– Supports analog and digital

sensors– 10-bit ADC– Various sensor board available

• Special circuit is needed when incorporating special sensors– Sample: the power board for the

MIR sensor– Designed by OSU– Lift power voltage– Transfer the sensor output to the

mote

Drivers• Software that controls the operation of an I/O

device• In TinyOS, usually the software components

that correspond to the hardware components provide driver functions– HPLCC1000M: radio driver– ADCM: ADC driver– PhotoTempM: Photo sensor and temperature

sensor (sometimes hardware components share a driver)

Drivers• A driver usually provides some common

control interfaces and some hardware-specific interfaces

• A driver may use other components• Sample: Magnetometer – MagM

MagM

StdControl MagSetting

ADCControl PotControl (StdControl) I2CPot

Common control interface Mag-specific interface

Drivers• Sample interface definition of the magnetometer

– provides interface StdControl;• Standard control interface

– Init, start, stop

– provides interface MagSetting;– interface MagSetting {

command result_t gainAdjustX(uint8_t val);

command result_t gainAdjustY(uint8_t val);

event result_t gainAdjustXDone(bool result);

event result_t gainAdjustYDone(bool result);

}

• The operation of drivers is similar to those in traditional computers, but more resource-limited and usually simpler.

• Polling and interrupt-based I/O are used• Sample: PhotoTempM - reading

1 TOSH_CLR_TEMP_CTL_PIN();

2 TOSH_MAKE_TEMP_CTL_INPUT();

3 TOSH_SET_PHOTO_CTL_PIN();

4 TOSH_MAKE_PHOTO_CTL_OUTPUT();

5 return call InternalPhotoADC.getData();

Drivers

Shut down temperature sensor

Start photo sensor

Drive ADC to get reading

• Illustration of one of the interfaces -- ADC– How a driver uses support components/drivers?– What does the InternalPhotoADC.getData() do?

Drivers

getData()

getContinuousData()

Interface ADC

dataReady(uint16_t data)

Commands

EventsADC

Middleware/app

getData() dataReady(data)

call callback

Put them together – Sample Design

• Step by step sample of how to integrate a new type of sensor into the system

• Sample sensor: MIR motion sensor– An MIR based motion sensor

• TWR-ISM-002 made by Advantaca

– Working with a different electrical setting than the mote’s

– 51-pin connector

Sample Design

• First, we need to make the electrical settings compatible

• OSU made a power board for this purpose– 51-pin connector– Provides 5.5V and 3.6V

power supply– Transfers the output from

the MIR sensor to the motes

Sample Design• Power supply and

sensor data pins• Power supply pins

are also control pins

• Connection:– Power supply pins

to the power supply input of the sensor

– The sensor output to the sensor data pins

Sample Design• Connects to the Mica

sensor board or Mica2 board

• Connection:– Mica2 (MicaSB) power

supply pins to the power supply pins on the power board

– Sensor data output to Mica2 (MicaSB) sensor data output/input

Sample Design• Now we’ve connected

the hardware. How can we drive them by software?

• Again, we need to look at the hardware– Question: How are the

I/O devices connected to the CPU?

– Look at the connector configuration

Sample Design• Then…

– Question: How the CPU communicates with the I/O devices?

– Look at the Mica2 schematic

– From the CPU’s point of view, the pins are addressed as bits in PortC, PortE and PortF

Sample Design• Then…

– Aliasing for easy use• PW0_PIN is aliased to the 0th bit of PortC, which connects to

– Pin 29 of the connector on the Mica2 board– (optionally) pin 29/49 of the connectors on the Mica sensor board– Pin 29 of the connector on the power board– Pin 29 of the connector on the MIR sensor board

• MIR_DIN_PIN is aliased to the 2nd bit of PortE, which connects to

– pin 25 of the connector on the Mica2 board– Optionally, pin 2/25 of the connectors on the Mica sensor board– pin 25 of the connector on the power board– and pin 25 of the connector on the MIR sensor board

– We can now read/write the pins to enable/disable/read the sensor

Sample Design• Enabling/Disabling/Reading

– EnablingTOSH_SET_PW0_PIN();

TOSH_SET_PW1_PIN();

TOSH_SET_PW4_PIN();

– DisablingTOSH_CLR_PW0_PIN();

TOSH_CLR_PW1_PIN();

TOSH_CLR_PW4_PIN();

– Reading• TOSH_READ_MIR_DIN_PIN();

Sample Design• Now we’ve acquired the digital reading.

Sometimes we also hope to read analog reading from the sensors

• The analog readings are read through the ADC component

• The general procedure is similar• The enabling/disabling procedure is usually the

same as that for the digital reading• Specifics about ADC reading

– Event driven– Multiplexing and demultiplexing

Sample Design• Post-processing of the information collected

from the sensor– The raw reading is not reliable

• High noise floor, due to environment noise, hardware interaction, and sensor interference

• SNR <= 1

– Apply the “Recently convincingly active” algorithm– The analog reading is analyzed and transformed to a

binary reading: “positive detection” and “negative detection”, which can be conveniently used by higher level applications

References and acknowledgements

•Thanks to Prof. Stankovic for reviewing and commenting on the slides

•Thanks to Yingmin Li for explaining some concepts in Physics

•Reference sites

•Honeywell: www.honeywell.com

•Alien Technology: http://www.alientechnology.com/

•Phillips Semiconductor: http://www.semiconductors.philips.com/markets/identification/products/icode/

•Texas Instruments: www.ti.com

•RFID Journal: www.rfidjournal.com

•www.rfid-handbook.com

•TinyOS: http://today.cs.berkeley.edu/tos

http://www.honeywell.com/

http://www.alientechnology.com/

http://www.semiconductors.philips.com/markets/identification/products/icode/

http://www.semiconductors.philips.com/markets/identification/products/icode/

http://www.ti.com/

http://www.rfidjournal.com/

http://www.rfid-handbook.com/

http://today.cs.berkeley.edu/tos

http://today.cs.berkeley.edu/tos

References and acknowledgements

Reference books and papers

• Ilene J. Busch-Vishniac, Electromechanical Sensors and Actuators• Vince Stanford, Pervasive Computing Goes the Last Hundred Feet with RFID Systems• Prabal K. Dutta. Integrating Micropower Impulse Radar and Motes• Michael J. Caruso, Lucky S. Withanawasam. Vehicle Detection and Compass Applications using AMR Magnetic Sensors• Advantaca: Hardwre User’s Manual for TWR-ISM-002-I Radar

hardware of the sensor network -- sensors and peripheral hardware -- lin gu sept 8, 2003

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