Introduction to Wireless Sensor Networks and its H/W Design Experiences
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Transcript of Introduction to Wireless Sensor Networks and its H/W Design Experiences
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Introduction to Wireless Sensor Networks and its H/W Design Experiences
Paper from: P. Zhang, C. Sadler, S. Lyon, and M. Martonosi, “Hardware Design Experiences in ZebraNet,” Proceedings of SenSys 2004, November 2004
Yueh-yi Wang 2005.11.17
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Outline
Introduction to WSN Hardware and system architecture of WS
N Case study: ZebraNet Summary & conclusions
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Introduction to WSN – Why WSN?
Personal & institutional security National defense Radiology, medicine Chemical plants Toxic urban locations Agriculture Natural hazards Many others …
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Application of Sensors in - Environment Monitoring Measuring pollutant
concentration Pass on information
to monitoring station Predict current
location of pollutant contour based on various parameters
Take corrective action
Pollutants monitored by sensors in the river bed
Sensors report to the base monitoring station
BS
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Sensors in Unknown Terrain
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Composition of a sensor(-actuator) node
Portable and self-sustained (power, communication, intelligence) Capable of embedded complex data processing Note: Power consumed in transmitting 1Kb data over 100m is
equivalent to 30M Instructions on 10MIPS processor Technology trends predict small memory footprint may not be a
limitation in future sensor nodes Equipped with multiple sensing, programmable computing and
communication capability
Transceiver
Embedded Processor
Sensor
Battery
Memory
Transceiver
Embedded Processor
Sensor
Battery
Memory
1Kbps- 1Mbps3m-300m
Lossy Transmission
8 bit, 10 MHzSlow Computation
Limited Lifetime
Requires Supervision
Multiple sensors
128Kb-1MbLimited Storage
Sensing + CPU + Radio = Thousands of potential applications
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EmbedSense™ Wireless SensorA Wireless sensor and data acquisition system
Can be placed within implants on spining machinery and within composite materials
No batteries - big advantage Uses an inductive link to receive
power from external coil Can be used in monitoring
temperatures in Jet turbine engines www.microstrain.com
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Different from traditional networks
Sensor networks are “data-centric” networks
Unique ID not effective in sensor networks large number of nodes imply large id, thus, data
sent may be less than the address Adjacent nodes may have similar data
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Hardware architecture of WSN- Parameters
Cost Lifetime Performance
Speed (in ops/sec, in ops/joule) Comms range (in m, in joules/bit/m) Memory (size, latency)
Capable of concurrent operation Reliability, security, size, packaging
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Hardware issue on WSN - A Generic Sensor Network Architecture
PROCESSINGSUB-SYSTEM
COMMUNICATIONSUB-SYSTEM
SENSINGSUB-SYSTEM
POWER MGMT.SUB-SYSTEM
ACTUATIONSUB-SYSTEM
SECURITYSUB-SYSTEM
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Processing subsystem - Illustration
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Processing subsystem- Microcontroller von Neumann architecture (same address and data bus for I/D)
typical 4 bit, 8 bit, 16 bit or 32 bit architectures speed 4 MHz-400MHz with 10-300 or more MIPS
operate at various power levels: fully active: 1 to 50 mW sleep (memory standby, interrupts active, clocks active, cpu off) sleep (memory retained, interrupts active, clocks active, cpu off) sleep (memory retained, interrupts active, clocks off, cpu off) 5uW latency of wakeup is an issue
fixed point / floating point operations multiple processors may be used (potentially on same core)
could be DSP, FPGA
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Processing subsystem- Memory Considerations
Speed, capacity, price, power consumption, memory protection Types:
SRAM: typical 0.5KB-64MB Typical power consumption
retained: ~100ua; read/write: ~10ma if separate chip retained: 2ua-100ua, read/write:~5ma if in core
DRAM: high power consumption in retained mode
Flash: 256KB-1GB or beyond Typical power consumption
retained: negligible; read/write: ~7/20ma erase operation is expensive
Large flashes are outside of core EEPROM:4KB-512KB, often used as program store
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Processing subsystem- Peripherals
Clock generators / Dividers Hardware Timers Peripheral interfaces
(for sensors, actuators, I/O, power)(analog and digital)(multiple buses with bridges between them) SPI: Serial Peripheral Interface I2C UART: Serial communication General Purpose Input Output pins (GPIO)
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Processing subsystem- Peripherals (contd.) Interrupts:
Asynchronous breaks in program execution Press of a button; expiration of a timer; completion of sensing data c
ollection, of DMA transfer, of transmission event, … When interrupt occurs, processor transitions to t
he corresponding interrupt handler to service interrupt and then resumes execution
Can have multiple priority levels Interrupts are enabled and disabled through reg
isters for each peripheral
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Processing subsystem- Timers
Controls the mode (interval or one-shot)Starts and stops the timerEnables/disables the interrupts for this timer
Holds value to compare against
Holds the value that initializes the timer at startup
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Sensor Subsystem Multiple types of sensors may be used:
Environmental: pressure, gas composition, humidity, light… Motion or force: accelerometers, rotation, microphone,
piezoresistive strain, position… Electromagnetic: magnetometers, antenna, cameras… Chemical/biochemical
Digital or analog output
MEMS enabling
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Power Management Subsystem Voltage regulator
typical ranges: 1.8V, 3.3V, 5V multiple voltages for various subsystem/power levels
Gauges for voltage or current battery monitor (allows software to adapt
computation)
Control of subsystems wakeup/sleep
Control of platform clock rate, processor voltage
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Communication Subsystem
IEEE 802.11
Bluetooth
Mote
Energy per bit
Startup time
Idle current
TechnologyData Rate
Tx Current
Energy per bit
Idle Current
Startup time
Mote76.8 Kbps
10 mA 430 nJ/bit 7 mA Low
Bluetooth 1 Mbps 45 mA 149 nJ/bit 22 mA Medium
802.11 11 Mbps 300 mA 90 nJ/bit 160 mA High
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Design Principles Key to Low Duty Cycle Operation:
Sleep – majority of the time Wakeup – quickly start processing Active – minimize work & return to sleep
Wtotal=Rsleep*Wsleep + Rwake*Wwake + Ractive*Wactive
W: Power Dissipation R: Ratio of the time period
Dynamic Power Consumption Pdynamic = Cswitched * VDD
2 * fclk
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Case Study: Hardware Design Experiences in ZebraNet Biologists Wishlist
Lightweight
Detailed 24/7 archival position logs
Mobile
No fixed base station (no cellular service)
Restricted human access to systems
ZebraNet: Wireless ad hoc network on zebras Intelligent tracking collars placed on sampled set of zebras Sensor network: data collected includes
GPS position info, temperature, …
➨ Energy-efficient
➨ GPS-enabled
➨ Wireless
➨ Peer-to-peer routing and data storage
➨ Plan 1 year of autonomous operation
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ZebraNet vs. Many Other Sensor Networks… All nodes mobile: Even “base station” is
mobile; intermittent drive-bys upload data
Large spatial extent 100s-1000s of sq. kilometers
“Coarse-Grained” nodes: Storage and processing capability >> many other sensor systems
Long-running and autonomous Reliability and energy-efficiency are key
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Hardware Evolution
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Other Evolution Change of -controller
Main reason is the variable clock frequency. Lower power usage (switching clocks) TI MSP430F149 allows multiple clocks
32 KHz in sleep mode 8 MHz in normal mode 32 KHz clock consumes 0.05 mA more than sleep
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Important Features Nodes obtain GPS reading
every 8 minutes GPS can sync to global clock
Nodes attempt to send information over radio every 2 hours
All data logged to onboard flash (local as well as received)
~256 bytes per hour
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ZebraNet Protocols Two peer-to-peer protocols evaluated here
Flooding: Send to everyone found in peer discovery. History-Based: After peer discovery, choose at most o
ne peer to send to per discovery period: the one with best past history of delivering data to base.
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Zebra show time
Solar Power with loosely rotated
=> efficiency dropped
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GPS Data for 1 Zebra Over 24 Hours
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Power Consumption Radio Tx consumes th
e most critical power.
The 2nd one is GPS.
Radio Rx takes the longest time while working.
Not much difference on u-C under 8MHz and 32KHz (odd?)
Dynamic Power ConsumptionPdynamic = Cswitched * VDD
2 * fclk
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Summary and Conclusions New design approach derived from the experience
with resource constrained wireless sensor networks Active mode needs to run quickly to completion Wakeup time is crucial for low power operation
Wakeup time and sleep current set the minimal energy consumption for an application
Sleep most of the time
Tradeoffs between complexity/robustness and low power radios
Careful integration of hardware and peripherals
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Summary and Conclusions Hardware choice worked very well for sparse node-to-no
de communication Simplicity of software environment dictated -controller
choice Details matter in WSN power management Future work of ZebraNet
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Thank you