Sensing and HardwareCS 4501

Professor Jack StankovicDepartment of Computer

Science

Fall 2010

HW - Mica2 and Mica2Dot

HW - Mica2 and Mica2Dot

• ATMega 128L 8-bit, 8MHz, 4KB EEPROM, 4KB RAM, 128KB flash• Chipcon CC100 multi-channel radio (Manchester encoding, FSK).

From 10-20 ft. up to 500-1000ft.

Sensor BoardSensor Board

Sensor BoardSensor Board

Magnetometer-CompassMagnetometer-Compass

Ultrasonic TransceiverUltrasonic Transceiver

Mica Weather BoardMica Weather Board

MicaDot Sensor BoardsMicaDot Sensor Boards

Spec Mote (3/6/2003)Spec Mote (3/6/2003)

• Size: 2x2.5mm, AVR RISC core, 3KB memory, FSK radio (CC1000), encrypted communication hardware support

Mica2

Rockwell WINSRockwell WINS

• StrongARM SA 1100, 32-bit RISC processor, 1MB SRAM, 4MB flash

• 900MHz spread spectrum radio, with dedicated microcontroller: 32KB RAM, 1MB bootable flash

• 3.5”x3.5”x3” package size• acoustic sensor• magnetometer• accelerometer• seismic sensor

module

UCLA Medusa MK-2UCLA Medusa MK-2

• Radio-acoustic localization• ATMega 128L 8-bit, 8MHz, 4KB flash, 4KB SRAM

( interface w/ sensors & radio)

• ARM Thumb 32-bit, 40MHz, 1MB flash, 136KB RAM (more demanding processing)

• TR1000 radio Monolithics (OOK, ASK modulation)• Ultrasonic ranging system, light & temperature

Medusa MK-2Medusa MK-2

• Can attach to infrastructure via a high speed wire link

• Daisy chain motes

Acoustic Sensor Magnetometer

Medusa MK-2Medusa MK-2

• Can power down various parts independently to save power– Subsystems– Each sensor– Radio– CPU (might have multiple power saving

modes)

Specialized HardwareSpecialized Hardware

• Environmental Motes (Berkeley, UVA)

• Medical Motes (Harvard/UVA)– Wireless EKG– Pulse Oximeter

• Robotic nodes• New

microprocessors/microcontrollers– Use TI chips instead of Atmel

More Specialized HWMore Specialized HW

• CCDs• Special logging mote (using camera

memory card)• Stargates – heterogeneous WSNs

– Powerful– Energy consumption is a problem

• New devices appearing continuously

Robo MoteRobo Mote

Trio NodeTrio Node

Solar Cells - Detecting LightSolar Cells - Detecting Light

E-Tag MoteE-Tag Mote

SeeMoteSeeMote

SensorsSensors

• Sensors must be small and low-power in order to reduce energy and fit form factor

• Packaging important• Robustness to weather needed

Sensors Sensors

• 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

– GPS– Cameras

ActuatorsActuators

• Examples of Actuators– Motor (for mobile nodes)– LEDs– Buzzer– Emit chemical

• In general, actuators may be powerful, large, and complicated– Can be outside of motes (e.g., turn on

lights, send a vehicle into system, …)

• What actuators should go on motes?

Properties of Sensors (14)

Properties of Sensors (14)

– Range• Example

– HMC1053: +/-6 Gauss

– Accuracy• Measure of error and uncertainty

– Repeatability• HMC1002: 0.05%

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

Gauss)

Sensors Sensors

– Sensitivity• How output reflects input?

– Efficiency• Ratio of the output power to the input power

– Resolution• Temperature within ½ degree

Sensors Sensors

•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

Sensors Sensors

– Packaging• Example – HMC1053: 16-PIN LCC

packaging

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

– Initialization Time (important when nodes are asleep and awakened dynamically when an event occurs)

Sensors Sensors

• Signal Processing– Process the sensor reading to make it

useful to the application• Sensor fusion (heterogeneity possible)• False alarm processing (false positives and false

negatives)

– The complexity varies from a simple threshold algorithm to full-fledged signal processing and pattern recognition

• New solutions needed on minimal capacity devices

SensorsSensors

• 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

Sensors Sensors

• 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

Acoustic SensingAcoustic Sensing

Three Cars

Initial Calibration

No Detection

Detection whenEnergy Crosses

Standard Deviation

Programming with Sensors

Programming with Sensors

Sensor

Sensor

Sensor

ADC

ADC

ADC

Voltage Micro-Proc

Micro-Proc

Micro-Proc

AMP

AMP

Voltage

210

ADCADC

• Resolution• Sample Rate

ADCMicro-Proc

Temp0-100 C

V

SensorSPII2C

210

28

212

Resolution

ADCADC

• MAX1245– 8 channels of analog input– Can sample up to 100,000 samples per

sec– Resolution of 12 bits– Interfaces with SPI and I2C buses– Can enter low power mode– Interface to Processor: processor issues

commands to read channel– Interfaces to sensors

ADCADC

• Sample rate

NyquistSamplingTheorem

Tooslow

Temperature SensorTemperature Sensor

• A22100– Output voltage: 22.5mV/C over

temperature range of -50C to 150C– Derive conversion equation (see spec

sheet)– Example: for 5 V power supply

• T = (V(out) – 1.375)/0.0225• If V(out) = 1.94V then T = 25.1C

A22100V(out)

GND

5V

Other SensorsOther Sensors

• Light– Add power and ground– Analog output voltage is proportional to

incident light– May need an amp to detect full range

• Accelerometer– Output voltage is proportional to acceleration

and power V(s)– V(out) = V(s)/2 – (sensitivity * V(s)/5 *

acceleration)– Sensitivity depends on particular

accelerometer

RFIDRFID

• RFID– Typical configuration

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

tracking, inventory management, …

PlusMicrochipWith data

RFIDRFID

– What makes RFID useful?•Ubiquitous•Low-cost (pennies)

– Compare RFID with motes•Difference? Yes (today).•Will they merge to be the same

class of hardware as motes?– Active RFID tags exist (battery/sensors)

– Privacy and security issues

Intel WISP tagIntel WISP tag

• Essentially a battery-less sensor mote– Light, temperature,

3d- accelerometer– 10 feet range with

harvested RF power

• Requires RFID reader and (large) antennas

Activity recognition using WISP*

Activity recognition using WISP*

* Ubicomp 2009

Antenna layout in home

WISP tags on kitchen artifacts

WISP potentialWISP potential

• Battery-free solution to sensor networks

• Great potential for elderly activity inference and other smart home applications

Sensor and Data FusionSensor and Data Fusion

• Data Fusion – combine data from multiple sources (not only sensors)

• Sensor Fusion – combine data from multiple sensors

SignaturesSignatures

• Objects/phenomena generate signatures

• Type of energy (electromagnetic, acoustic, ultrasonic, seismic, etc.

• Active or passive sensors• Affected by weather, clutter,

countermeasures, etc.

Data FusionData Fusion

• Ad hoc• Classical• Bayesian• Dempster-Shafer• Fuzzy Logic• Pattern Recognition• ANN• Etc.

Multi-ModalMulti-Modal

• Robustness• Act synergistically in high clutter and

inclement weather

• Example: Weather satellites use microwave, millimeter wave, infrared and cameras

• Example: Fog at an airport• Example: Rain cools targets (PIR

sensors not as effective)

Fusion ArchitectureFusion Architecture

ZigBee Coordinator

ZigBee Router/FFD

Raw Data to KnowledgeRaw Data to Knowledge

• Detection• Classification• Identification

Medical Care Medical Care

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18

front

floor

fridge

microwave

pantry

cook

top

sink flush

entrance

sink shower

motion

motion

motion

weight

light

light

light

pressur

e

bed room

kitchen

kitchen

kitchen

kitchen

kitchen

bath

room

bath

room

bath

room

bath

room

bed room

kitchen

bath

room

bed room

bed room

kitchen

bath

room

bed room

Personal location tracking

Kitchenvisits

bedroomvisits

bathroomvisits

eating toileting showering sleeping

Eating Level

ToiletingLevel

Sleeping Level

MovementLevel

LightLevel

WeightLevel

Diabetes Depression

Light Weight

ReferenceReference

• Sensor and Data Fusion, L. Klein, SPIE Press, 2004.