1. Sensing and Hardware CS 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

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

20. Solar Cells - Detecting Light

21. E-Tag Mote

22. SeeMote

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

24. 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

25. 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?

26. 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)

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

28. 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

29. 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)

30. 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

31. 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

32. 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.

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

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

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

36. 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

37. ADC Sample rate Nyquist Sampling Theorem Too slow

38. 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 A22100 V(out) GND 5V

39. 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

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

41. 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

42. 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

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

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

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

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

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

48. 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)

49. Fusion Architecture ZigBee Coordinator ZigBee Router/FFD

50. Raw Data to Knowledge Detection Classification Identification

51. Medical Care

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53. Reference Sensor and Data Fusion, L. Klein, SPIE Press, 2004.