system architecture of wireless body sensor networks

System Architecture of Wireless Body Sensor Networks

Dr. Emil JovanovElectrical and Computer Engineering Dept.

University of Alabama in Huntsvillehttp://www.ece.uah.edu/~jovanov

emil.jovanov@uah.edu

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Wireless Body Area Networks (WBAN)

Natural choice for unobtrusive wearable monitoring –

Implantable sensors

Ubiquitous ambulatory monitoringVery specific design spaceReactive vs. Proactive systems–

Real-time processing

Issues & Applications

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WBAN –

design goals

minimization of weight and size of sensors,portability,unobtrusiveness,ubiquitous (but intermittent) connectivity,reliability, and seamless system integration.

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Outline

IntroductionSystem ArchitectureHierarchical ProcessingWireless TechnologiesConclusion

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WSN System Architecture

Hierarchy of networksHierarchy of processingData aggregation

InternetInternet

MS

HS

PS

SP1

NC BAN

LAN

S11 S12 S1m

SPn

Sn1 Sn2 Snm

SAN

WAN

S21 S22 S2m

SP2

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ActiS: Activity Sensor

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iSense Sensor Node

Inertial sensor–

5DOF

–

3D acc–

2 axis gyro

I2C busF2274 processor

1.2”

0.9

4”

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Sensor Platform Architecture

SPi

Si1

Si2

Inertial Sensor device iSenseIntelligent sensor platformInertial sensors (accelerometer and gyroscope)–

Each sensor generates data streams (Fs

, Ss

, etc.)

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InternetInternet

S S S S

MS

HS

PS

Sn

NCWBAN

WLAN

S S S SSn

WBAN

InternetInternet

MS

HSNC

Sn

InternetInternet

S S S S

MS

PS

WBAN

WAN

NC

WBAN Configurations

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Example #1: Ubiquitous Health Monitoring

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WBAN Ubiquitous Health Monitoring

E. Jovanov, A. Milenkovic, C. Otto, P. de Groen, “A wireless body area network of intelligent motion sensors for computer assisted physical rehabilitation,”Journal of NeuroEngineering and Rehabilitation, March 1, 2005, 2:6, 2005, http://www.jneuroengrehab.com/content/2/1/6

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IMEC

Custom ASIC bioamplifier - ExGEnergy scavenging

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Toumaz

Extremely power efficient ASIC–

Hardwired protocol engineECG Sensium–

Switched–Opamp, ΔΣ

- ADC–

14 μW @ 1V

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deFOG

–

unfreezing of gait of Parkinson’s patients

Inertial sensor

Wireless headset

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Example #2: Avatar: Real-time Motion Capture System

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ImArmD

(Inertial Mobile Arm Device)

Master Node

Inertial Sensors

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Hybrid BAN

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Outline

IntroductionSystem ArchitectureHierarchical Processing Wireless TechnologiesConclusion

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System design –

on sensor processing

On-sensor processing reduces required communication bandwidth - SBWExample: Heart beat events (Rpeak event)

Rpeak–

BWi

= [1]·[0.6..4]·[16] = [9.6..64] bps

Nchi Fsi SSi

Local Intelligence always pays off

Raw ECG–

BWi

= [1..3]·[250..500] ·[12..16] = [3,000..24,000] bps

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Power Efficient Operation

Battery Life–

Battery Capacity [mAh] –

BL = BC / Iave

–

For simple time keeping and minimal processing average power is ~2.1μA, standard 750 mAh

batteries will allow battery life:

–

BL = 750 mAh

/ 2.1 μA ≈

44 years !!!Energy scavengingApple Computers

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Power efficient communication

Wireless communication requires at least 10 times more power than processingActive power significantly higher than standby or idle –

Turn-off radio whenever you can

Design parameters–

Average current battery life

–

Maximum current battery size/weight–

Asymmetric power requirements

•

UWB Rx/Tx

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Relevant controller parametersWireless Controller CC2420 (ZigBee)–

Programmable output power

–

Bit rate: 250 kbps–

Quiescent current Power Down Mode 13-29 μA

–

Idle mode 426 μA–

Startup time: Typical 0.3ms, Maximum 0.6ms

–

Power RX: 18.8mA, TX: 17.4mA–

Power 0 dBm

17 mA

−5 dBm

14 mA -10 dBm

11 mA

-15 dBm

9.9 mA -25 dBm

8.5 mA

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Power consumption (Receive mode)

Energy = V * ∫ I(t)dt

I_idle

I_rec

Power_up Wait ReceiveReceive Power_down

Power supply current [mA]

Time [s]

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Power consumption overhead

Lin Zhong, Mike Sinclair, and Ray Bittner, “A Phone-centered body sensor network platform: Cost, energy efficiency, and user interface,”Proc. IEEE Workshop on Body Sensor Networks, Apr. 2006.

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Battery life as a function of duty cycle

5 10 15 20 25 30 35 400

5

10

15

20

25

30

35

40

45

50Battery life [days]

Channel utilization [% ]

2xAA Alkaline batteries (50gr)

Li-Ion battery (22gr) Li-Ion iPod mini battery (6gr)

Actis Processed ACC Signals

Actis Raw ACC Signals

C. Otto, A. Milenkovic, C. Sanders, E. Jovanov, "System Architecture of a Wireless Body Area Sensor Network for Ubiquitous Health Monitoring," Journal of Mobile Multimedia, Vol. 1, No. 4, January 2006, pp. 307-326.

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Power consumption (Transmit mode)Synchronization overheadProtocol overheadAcknowledgments

I_idle

I_tx

Power_up Prot/ohTransmitTransmit

Power_down

Power supply current [mA]

Time [s]Acknowledgment

I_rec

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Power efficient wireless communication -

implementation

Super cycle time in this example is 1 sec. Sensors listens at the beginning of each cycle for 50ms, and transmits its own messages (2 in this example) in predefined time slot.

0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 50

5

10

15

20

25

Time [sec]

I [m

A]

0.65 0.7 0.75 0.8 0.85 0.90

5

10

15

20

Time [sec]

I [m

A]

Listen Transmit

Idle

Power supply current on sensor

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Outline

IntroductionSystem ArchitectureHierarchical Processing Wireless TechnologiesConclusion

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Wireless technologies

WANWLANBluetooth–

Widespread, cell phones/PDAs, 720 kbps–

High power consumption, protocol stack complexity ZigBee–

Emerging standard–

Low power, 250 kbpsCustom ISM band controllers –

Nordic controller & coprocessorUWB–

High bandwidth, location capabilitiesCustom and alternative solutions–

MEMS resonators (100 µW)–

Integrated protocol processors

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WBAN design spaceBattery life

Bandwidth/Latency

Toumaz

ZigBee

Bluetooth

UWBWLAN

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Power efficiency

Pact [mW] Power/bit

Toumaz 6 180

ZigBee 66 264

Bluetooth 240 333

WiFi 600 11

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WBAN ApplicationsEmerging integration technology–

Promising technology for unsupervised, continuous, ambulatory health monitoring

–

Challenge: design WBAN for extended RT monitoring of physiological data and events

Opportunities: –

Ambulatory health monitoring, longitudinal monitoring–

Computer-assisted rehabilitation–

Augmented reality systemsLong-term benefits:–

Promote healthy lifestyle–

Seamless integration of data into personal medical records and research databases

–

Knowledge discovery through data mining

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ConclusionsOptimal solutions are application specificLocal intelligence always pays offUnderstand your applicationUnderstand underlying technologiesSimulate application scenariosMeasure real-time power consumption*

Commercial vs. Medical grade devices

* Aleksandar Milenkovic, Milena Milenkovic, Emil Jovanov, Dennis Hite, Dejan Raskovic, “An Environment for Runtime Power Monitoring of Wireless Sensor Network Platforms,”Proc. 37th Southeastern Symposium on System Theory (SSST’05), Tuskegee, AL, March 2005, pp. 406–410.