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Sensing and Hardware CS 4501. Professor Jack Stankovic Department of Computer Science Fall 2010. 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 Board.

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Sensing and Hardware CS 4501

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Sensing and hardware cs 4501

Sensing and HardwareCS 4501

Professor Jack Stankovic

Department 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 board

Sensor Board


Sensor board1

Sensor Board


Magnetometer compass

Magnetometer-Compass


Ultrasonic transceiver

Ultrasonic Transceiver


Mica weather board

Mica Weather Board


Micadot sensor boards

MicaDot 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 wins

Rockwell 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 2

UCLA 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 2

Medusa MK-2

  • Can attach to infrastructure via a high speed wire link

  • Daisy chain motes

Acoustic Sensor Magnetometer


Medusa mk 21

Medusa MK-2

  • Can power down various parts independently to save power

    • Subsystems

    • Each sensor

    • Radio

    • CPU (might have multiple power saving modes)


Specialized hardware

Specialized 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 hw

More Specialized HW

  • CCDs

  • Special logging mote (using camera memory card)

  • Stargates – heterogeneous WSNs

    • Powerful

    • Energy consumption is a problem

  • New devices appearing continuously


Robo mote

Robo Mote


Trio node

Trio Node


Solar cells detecting light

Solar Cells - Detecting Light


E tag mote

E-Tag Mote


Seemote

SeeMote


Sensors

Sensors

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

  • Packaging important

  • Robustness to weather needed


Sensors1

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


Actuators

Actuators

  • 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)


Sensors2

Sensors

  • Sensitivity

    • How output reflects input?

  • Efficiency

    • Ratio of the output power to the input power

  • Resolution

    • Temperature within ½ degree


Sensors3

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


Sensors4

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)


Sensors5

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


Sensors6

Sensors

  • 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


Sensors7

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.


Acoustic sensing

Acoustic Sensing

Three Cars

Initial

Calibration

No Detection

Detection when

Energy Crosses

Standard Deviation


Programming with sensors

Programming with Sensors

10

2

Voltage

Micro-

Proc

Sensor

ADC

Micro-

Proc

Sensor

AMP

ADC

Voltage

Micro-

Proc

Sensor

ADC

AMP


Sensing and hardware cs 4501

ADC

12

2

  • Resolution

  • Sample Rate

10

2

8

2

Resolution

V

Temp

0-100 C

Micro-

Proc

ADC

SPI

I2C

Sensor


Sensing and hardware cs 4501

ADC

  • 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


Sensing and hardware cs 4501

ADC

  • Sample rate

Too

slow

Nyquist

Sampling

Theorem


Temperature sensor

Temperature 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

5V

V(out)

A22100

GND


Other sensors

Other 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


Sensing and hardware cs 4501

RFID

  • RFID

    • Typical configuration

    • Application: ID based intelligent control

      • Such as access control, baggage ID, object tracking, inventory management, …

Plus

Microchip

With data


Sensing and hardware cs 4501

RFID

  • 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 tag

Intel 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*

WISP tags on kitchen artifacts

Antenna layout in home

* Ubicomp 2009


Wisp potential

WISP potential

  • Battery-free solution to sensor networks

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


Sensor and data fusion

Sensor and Data Fusion

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

  • Sensor Fusion – combine data from multiple sensors


Signatures

Signatures

  • Objects/phenomena generate signatures

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

  • Active or passive sensors

  • Affected by weather, clutter, countermeasures, etc.


Data fusion

Data Fusion

  • Ad hoc

  • Classical

  • Bayesian

  • Dempster-Shafer

  • Fuzzy Logic

  • Pattern Recognition

  • ANN

  • Etc.


Multi modal

Multi-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 architecture

Fusion Architecture

ZigBee Coordinator

ZigBee Router/FFD


Raw data to knowledge

Raw Data to Knowledge

  • Detection

  • Classification

  • Identification


Medical care

Medical Care


Sensing and hardware cs 4501

Diabetes

Depression

Eating

Level

Toileting

Level

Sleeping

Level

Movement

Level

Weight

Level

Light

Level

eating

toileting

showering

sleeping

Light

Weight

Kitchen

visits

bathroom

visits

bedroom

visits

Personal

location tracking


Reference

Reference

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


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