system architecture of wireless body sensor networks
TRANSCRIPT
System Architecture of Wireless Body Sensor Networks
Dr. Emil JovanovElectrical and Computer Engineering Dept.
University of Alabama in Huntsvillehttp://www.ece.uah.edu/~jovanov
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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.