Signals, Instruments, and Systems W6. Introduction to Embedded. Sensing, Communicating

Transcription

1 Signals, Instruments, and Systems W6 Introduction to Embedded Systems Computing, Sensing, Communicating

2 Outline Embedded system terminology and key concepts Examples of embedded systems The Mica-z as example of embedded system Perception Communication Wired Wireless

3 General Concepts for Embedded Systems

4 What is an Embedded System? From Wikipedia: An embedded system is a special-purpose p p computer system designed to perform one or a few dedicated functions often with real-time computing constraints. It is usually embedded as part of a complete device including hardware and mechanical parts. In contrast, a general-purpose computer, such as a personal computer, can do many different tasks depending on programming.

5 What is Challenging in Designing Embedded Systems? Computation is subject to physical and resource constraints such as timing, deadlines, memory restrictions, and power consumption requirements. The traditional abstraction of separating software from the hardware is more difficult. Hardware and software are integrally intertwined. But: hardware components are becoming more and more flexible, cheap, small, and standardized. The design complexity is shifting to software! Your role as Environmental/Civil Engineers: get enough background to contribute to the software side with your domain knowledge and collaborate with electrical/computer/mechanical/mechatronic engineers.

6 Perception - Sensors Proprioceptive ( body ) vs. exteroceptive ( environment ) Ex. proprioceptive: motor speed/robot arm joint angle, battery voltage, acceleration Ex. exteroceptive: distance measurement, light intensity, sound amplitude, temperature, wind speed Passive ( measure ambient energy ) vs. active ( emit energy in the environment and measure the environmental reaction ) Ex. passive: temperature probes, microphones, cameras Ex. active: laser rangfinder (LIDAR), IR proximity sensors, ultrasound sonars, ultrasound anemometers [Adapted from Introduction to Autonomous Mobile Robots, Siegwart R. and Nourbakhsh I. R.]

7 Computation Usually microcontroller-based Microcontrollers are all-in-one computer chips. They contain a processing core, memory, and integrated peripherals (e.g., ADC, motor control PWM generator, bus controller). Discretization (analog-to-digital for values, continuous-to-discrete to for time) and continuization (digital-to-analog for values, discrete-to-continuous to continuous for time)

8 Communication Different physical channels: wired (e.g., RS232, CAN, USB) and wireless (e.g., radio, infrared, ultrasound, sound) Internal or external to the device: buses connecting different components; external (e.g., node-to-node or node-to-basestation) Asymmetric (one way) or symmetric (bidirectional) link Direct (explicit) or indirect (implicit): direct implies dedicated hardware and software components for intentional, targeted information sharing; indirect, implies anonymous, broadcasting forms which are temporary (e.g., visual signs)

9 Examples of Embedded d Systems

10 Consumer Market Devices Digital Watch Weather station Digital camera Digital video camera

11 Niche Market Scientific Equipment Commercially Available Sensorscope station Mica-Z Handheld Airborne Mapping System

12 Example for Sensorscope Stations What is measured: temperature humidity precipitation wind speed/direction solar radiation soil moisture Pictures: courtesy of SwissExperiment

13 Why: Typical Applications for Sensorscope Stations Capture meteorological events with high spatial density. Pictures: courtesy of SwissExperiment

14 MicaZ An Example of Embedded System

15 MICA mote family designed in EECS at UCBerkeley manufactured/marketed by Crossbow several thousand produced used by several hundred research groups about CHF 250/piece variety of available sensors

16 MICAz Atmel ATmega128L 8 bit microprocessor, ~8MHz 128kB program memory, 4kB SRAM 512kB external flash (data logger) Chipcon CC (Zigbee) 2 AA batteries about 5 days active (15-20 ma) about 20 years sleeping g( (15-20 µa) TinyOS

17 Sensor board MTS 300 CA Light (Clairex CL94L) Temp (Panasonic ERT-J1VR103J) Acoustic (WM-62A Microphone) Sounder (4 khz Resonator)

18 / Zigbee Emerging standard for low-power wireless monitoring and control 2.4 GHz ISM band (84 channels), 250 kbps data rate Chipcon/Ember CC2420: Single-chip transceiver 1.8V supply 19.7 ma receiving 17.4 ma transmitting Easy to integrate: Open source drivers O-QPSK modulation o (Code Division Multiple Access, CDMA); plays nice with and Bluetooth

19 Comparison to other standards

20 Operating system An operating system (OS) is an interface between hardware and user applications. It is responsible for the management and coordination of tasks and the sharing of the limited resources of the computer system. At typical los can be decomposed dinto the following entities: Scheduler, which is responsible for the sharing of the processing unit (microprocessor or microcontroller) Device drivers, which are low-level programs that manage the various devices (sensors, actuators, secondary memory storage devices, etc.). Memory management unit, which is responsible for the sharing of the memory (virtual memory). Optional: Graphical User Interface, File System, Security, etc. Most OS for embedded systems include these two entities only!

21 TinyOS: description Minimal OS designed for Sensor Networks Event driven execution Programming language: nesc (C-like syntax but supports TinyOS concurrency model) Widespread usage on motes MICA (ATmega128L) TELOS (TI MSP430) Provided simulator: TosSim

22 Perception

23 4a 4a - Perception - Sensors 23 Classification of Typical Sensors [From Introduction to Autonomous Mobile Robots, Siegwart R. and Nourbakhsh I. R.]

24 4a 4a - Perception - Sensors Classification of Typical Sensors 24 [From Introduction to Autonomous Mobile Robots, Siegwart R. and Nourbakhsh I. R.]

25 4a 4a - Perception - Sensors 25 General Sensor Performance Range Upper limit Dynamic range ratio between lower and upper limits, usually in decibels (db for power and amplitude) e.g. voltage measurement from 1 mv to 20 V Note: similar to the acoustic amplitude e.g. power measurement from 1 mw to 20 W P = U I = 1 U 2 R Note: see also the example of wireless transmission power in this lecture [Adapted from Introduction to Autonomous Mobile Robots, Siegwart R. and Nourbakhsh I. R.]

26 4a 4a - Perception - Sensors 26 General Sensor Performance Resolution minimum difference between two values usually: lower limit of dynamic range = resolution for digital sensors it is usually the A/D resolution. Linearity e.g. 5V / 255 (8 bit) variation of output signal as function of the input signal linearity is less important when signal is treated with a computer x f ( x) y f (y) α x + β y f ( α x + β y ) = α f ( x ) + β f ( y )? [From Introduction to Autonomous Mobile Robots, Siegwart R. and Nourbakhsh I. R.]

27 4a 4a - Perception - Sensors 27 General Sensor Performance Bandwidth or Frequency the speed with which a sensor can provide a stream of readings usually there is an upper limit it depending di on the sensor and the sampling rate lower limit is also possible, e.g. acceleration sensor frequency response (see signal processing lecture, filter part): phase (delay) of the signal and amplitude might be influenced [Adapted from Introduction to Autonomous Mobile Robots, Siegwart R. and Nourbakhsh I. R.]

28 4a 4a - Perception - Sensors 28 In Situ Sensor Performance Characteristics that are especially relevant for real world environments Sensitivity ratio of output change to input change however, in real world environment, the sensor has very often high sensitivity to other environmental changes, e.g. illumination Cross-sensitivity (and cross-talk) sensitivity to other environmental parameters influence of other active sensors Error / Accuracy difference between the sensor s output and the true value error m = measured value v = true value [Adapted from Introduction to Autonomous Mobile Robots, Siegwart R. and Nourbakhsh I. R.]

29 4a 4a - Perception - Sensors 29 In Situ Sensor Performance Characteristics that are especially relevant for real world environments Systematic ti error -> >deterministic i ti errors caused by factors that can (in theory) be modeled -> prediction e.g. calibration of a laser sensor or of the distortion cause by the optic of a camera Random error -> non-deterministic no deterministic prediction possible however, they can be described probabilistically e.g. gaussian noise on a distance sensor, black level noise of camera Precision (different from accuracy!) reproducibility of sensor results σ = standard dev of the sensor noise [From Introduction to Autonomous Mobile Robots, Siegwart R. and Nourbakhsh I. R.]

30 Wired Communication

31 Where? Within embedded systems (from sensor to microcontroller, from microcontroller to microcontroller, etc.) From an embedded system to another From an embedded system to a PC

32 Communication Model Transmitter channel Receiver

33 Communication Model Noise Transmitter channel Receiver Modulation Coding (Compression) Distortion Filtering Frequency shift... Demodulation Decoding (Decompression)

34 A Seminal Example: The RS-232 (serial port) Hardware: 3 wires: TxD, RxD, Ground RxD TxD Transceiver 1 TxD Ground RxD Ground Transceiver 2 Transceiver = Transmitter + Receiver

35 RS-232 (serial port) Signal: between RxD/TxD and Ground RxD TxD Transceiver 1 TxD Ground RxD Ground Transceiver 2

36 RS-232 Modulation

37 RS-232 Demodulation

38 RS-232 Delay Packet-based 1 byte (i.e. 8 bits)/ packet 8 data bits + 2 control bits = 10 bits Transmission speed max. 115'200 bits/s (bps) Propagation speed: approx. c (speed of light)

39 RS-232 Delay Transmission delay 10 bits / 115'200 bps = 86.8 us Signal propagation delay (2 m cable) 2 m / c = ns Processing delay: ~ 1 us (modulation, o demodulation, o processing) Total: ~ 90 us = 0.09 ms

40 Wireless Communication

41 Communication Model Noise Transmitter channel = ElectroMagnetic waves in air Reflections Fading Interference Other EM sources... Receiver

42 Communication Model Noise Transmitter channel = ElectroMagnetic waves in air Reflections Fading Interference Other EM sources... Receiver Channel estimation Advanced modulation types Coding and error correction

43 Sharing the Medium 1 2 3

44 Sharing the Medium TDMA Time-Division Multiple Access You shut up while I talk Time allocation Fixed, synchronized e.g. mobile phones (GSM) Dynamic (check kif channel lis free) e.g. Wireless LAN (802.11b/g/n) time

45 Sharing the Medium FDMA Frequency-Division MA e.g. FM radio channels Frequency regulation BAKOM (CH) bandwidth allocated by BAKOM frequency

46 Bandwidth Can be defined by the BAKOM for multiple channels for a given purpose (in the overall spectrum) Can be defined for a single channel similarly to a bandpass filter, as follow: B = bandwidth f 0 = central (filter) or carrier (channel) frequency f L = low cut-off frequency (typically defined at -3dB) f H = high cut-off frequency (typically defined at -3dB)

47 Bandwidth FM station broadcasting at 106,4 MHz actually occupies 106,3 MHz 106,5 MHz Bandwidth = 200 khz Mobile phone (GSM): 200 khz (around 900 MHz) WLAN/WiFi: 5 MHz (around 2,4 GHz) Analog TV station: 6 MHz (around 180 MHz) What does the bandwidth depend on? Bandwidth [Hz] Data rate (Throughput) [bits/s]

48 Bandwidth

49 Sharing the Medium CDMA (spread spectrum) Code-Division MA Using different transmission codes e.g. GPS, Wifi, 3G cell phones, g,, p, Zigbee Interesting properties Wide channels (fading) Concurrent communication More complex demodulation

50 Throughput (bits/s) TDMA, FDMA, CDMA can be combined Total throughput is shared TDMA CDMA FDMA

51 Shannon-Hartley Limit Hard theoretical limit on throughput More bandwidth = higher throughput More power (SNR) = higher throughput dilution Bandwidth C: capacity (throughput) B: bandwidth S: signal power (W) N: noise power (W) Bit energy to noise-power spectral density ~ S/N

52 Power Increased power higher throughput higher range mobile systems: shorter battery life increased health risk (?) Regulation CH: BAKOM e.g. WLAN: 100 mw Regulation

53 Power Unit: W (Watt) Often written in dbm (decibels to 1 mw) Gain / loss: factors Often written in db (decibels)

54 P dbm PW PdBm = 10log log( x * y ) = log( x ) + log( y ) 1mW 1mW 10 log(1mw/1mw) 10 log(1) = 10*0 = 0dBm 2mW 10 log(2mw/1mw) 10 log(2) 10*0.3=3dBm 10mW 10 log(10mw/1mw) 10 log(10) = 10*1=10dBm 100mW 10 log(100mw/1mw) W) 10 log(100) = 10*2=20dBm 20dB

55 Link Budget Typical lwlan link budget (100 m, dipole antennas): TX power TX losses TX antenna gain Free space path loss RX antenna gain 100 mw *0.5 *1.6 *1.0106*10-8 * dbm -3 db +2 db -80 db +2 db RX losses RX power * mw -3 db -62 dbm RX sensitivity mw -85 dbm Margin db

56 Free Space Path Loss (Friis Law) Signal power decay in air: Proportional to the square of the distance d Proportional to the square of the frequency f high frequency = high loss low frequency = low bandwidth

57 Ex.: Mica-Z vs.tinynode Sensorscope data logger Mica-Z (Crossbow) Microcontroller: ATmega128L TinyOs Transceiver: Chipcon CC GHz carrier Throughput: up to 250k bps Range: up to 75 m TinyNode (Shockfish) Microcontroller: TI MSP430 TinyOs Transceiver: Semtech XE and 915 MHz carriers Throughput: up to 153k bps Range: up to 2 km

58 Conclusion

59 Take Home Messages Embedded system: specific purpose, equipped for interfacing discrete/digital and continuous/analog world, microcontrollerbased design, often real-time constraints Main modules of an embedded system: perception, computation, communication Several examples of embedded systems in our daily life and for research/education purposes (e.g. Mica-z, Sensorscope stations) Perception and communication are two key features of embedded systems Some key concepts in sensing and communication systems Propioceptive/exteroceptive, active/passive, etc. Bandwidth, real throughput, h t TDMA, FDMA, CDMA Transmitted/received power and corresponding losses

60 Additional Literature Week 6 Pointers: Permasense GITWES the German Indonesian Tsunami Early Warning System ftp://ftp.cordis.europa.eu/pub/fp7/ict/docs/sustainablegrowth/workshops/workshop jwachter_en.pdf Sensorscope ch/ TinyOS: Com systems: Com systems: Books Siegwart R., Nourbakhsh I. R., and D. Scaramuzza, Introduction to A t M bil R b t S d diti MIT P 2011 Autonomous Mobile Robots, Second edition, MIT Press, Everett, H. R., Sensors for Mobile Robots, Theory and Application, A. K. Peters, Ltd., 1995.