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Sensing and Hardware CS 4501

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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  1. Sensing and HardwareCS 4501 Professor Jack Stankovic Department of Computer Science Fall 2010

  2. 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.

  3. Sensor Board

  4. Sensor Board

  5. Magnetometer-Compass

  6. Ultrasonic Transceiver

  7. Mica Weather Board

  8. MicaDot Sensor Boards

  9. Spec Mote (3/6/2003) • Size: 2x2.5mm, AVR RISC core, 3KB memory, FSK radio (CC1000), encrypted communication hardware support Mica2

  10. 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

  11. 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

  12. Medusa MK-2 • Can attach to infrastructure via a high speed wire link • Daisy chain motes Acoustic Sensor Magnetometer

  13. Medusa MK-2 • Can power down various parts independently to save power • Subsystems • Each sensor • Radio • CPU (might have multiple power saving modes)

  14. 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

  15. More Specialized HW • CCDs • Special logging mote (using camera memory card) • Stargates – heterogeneous WSNs • Powerful • Energy consumption is a problem • New devices appearing continuously

  16. Robo Mote

  17. Trio Node

  18. Solar Cells - Detecting Light

  19. E-Tag Mote

  20. SeeMote

  21. Sensors • Sensors must be small and low-power in order to reduce energy and fit form factor • Packaging important • Robustness to weather needed

  22. 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

  23. 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?

  24. 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)

  25. Sensors • Sensitivity • How output reflects input? • Efficiency • Ratio of the output power to the input power • Resolution • Temperature within ½ degree

  26. 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

  27. 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)

  28. 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

  29. 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

  30. 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.

  31. Acoustic Sensing Three Cars Initial Calibration No Detection Detection when Energy Crosses Standard Deviation

  32. Programming with Sensors 10 2 Voltage Micro- Proc Sensor ADC Micro- Proc Sensor AMP ADC Voltage Micro- Proc Sensor ADC AMP

  33. ADC 12 2 • Resolution • Sample Rate 10 2 8 2 Resolution V Temp 0-100 C Micro- Proc ADC SPI I2C Sensor

  34. 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

  35. ADC • Sample rate Too slow Nyquist Sampling Theorem

  36. 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

  37. 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

  38. RFID • RFID • Typical configuration • Application: ID based intelligent control • Such as access control, baggage ID, object tracking, inventory management, … Plus Microchip With data

  39. 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

  40. 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

  41. Activity recognition using WISP* WISP tags on kitchen artifacts Antenna layout in home * Ubicomp 2009

  42. WISP potential • Battery-free solution to sensor networks • Great potential for elderly activity inference and other smart home applications

  43. Sensor and Data Fusion • Data Fusion – combine data from multiple sources (not only sensors) • Sensor Fusion – combine data from multiple sensors

  44. Signatures • Objects/phenomena generate signatures • Type of energy (electromagnetic, acoustic, ultrasonic, seismic, etc. • Active or passive sensors • Affected by weather, clutter, countermeasures, etc.

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

  46. 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)

  47. Fusion Architecture ZigBee Coordinator ZigBee Router/FFD

  48. Raw Data to Knowledge • Detection • Classification • Identification

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