An Architecture for a Power-Aware Distributed Microsensor Node · An Architecture for a Power-Aware...
Transcript of An Architecture for a Power-Aware Distributed Microsensor Node · An Architecture for a Power-Aware...
An Architecture for a Power-Aware Distributed Microsensor Node
Rex Min, Manish Bhardwaj, Seong-Hwan Cho, Amit Sinha, Eugene Shih, Alice Wang, and
Anantha Chandrakasan
Massachusetts Institute of Technology
October 12, 2000
Distributed Microsensor Networks
An alternative to macrosensorso Small, ubiquitous, easily deployed nodes
oCollaborative data gathering, ad hoc networking for fault-tolerance
o Battery replacement not an option:
Smart Dust PicoRadio WINS
n Some prototype nodes under active research:
How to achieve months/years of operation from a single battery?
Operational Characteristics
n Event driveno Low duty cycles
n Low bandwidtho bits/sec to kbits/sec
n High Spatial Densityo 0.1 nodes/m2 to 20 nodes/m2
n Short transmission distanceso 5-10m typical (< 100m)
n High operational diversity
...from the environmento Event arrival rate/type
o Ambient noise
o Signal statistics
...from network roleso Sensor
o Relay
o Data aggregator
:
...from user demandso Tolerable latency
o Result SNR
o Pr(Detection)
Characterizing Diversity
Scenario
Ene
rgy
Esystem2
di
Esystem1
n Scenario: the space of possible operating points
n Scenario distribution: quantifies diversity as a distribution di
n Energy curve: Esystem characterized for each operating point
Power-aware systems have a low Esystemdi product
A Power-Aware Sensor Node
MIT µAMPS: Adaptive Multi-Domain Power-Aware SensorsA sensor node that demonstrates power-aware methodologies
Scenario
Eperfect
Ene
rgy
Esystemdi
ACTIVE IDLE ACTIVE
n Component-by-component optimization
n Reduction of worst-case power dissipation
n Graceful energy scalability across a diversity of operating conditions
n Energy-quality trade-offs
n Collaboration across levels of the system hierarchy
Low-Power Design Power-Aware Methodologies
Power-Aware Node Architecture
Leakage current Workload variation
Bias currentStart-up time
Standby currentLow duty cycle
Capacity variations
Efficiency variations
Desired result quality variations
Available energyVoltage scheduling
RadioSA-1100A/DSeismic Sensor
Acoustic Sensor
ROMRAM
DC-DC Conversion
Battery
Power
Protocols
Algorithms
µOS
Node Prototype
sensor/processor boardradio baseband
miniaturized DVS control(Nathan Ickes)
n Version 1 prototype with COTS components
n Advanced iterations will feature custom chipsets
Power-Aware Methodologies
n Idle mode: deepest possible shutdown with minimum overheado Leakage current control
oRadio duty cycles
o Sleep state assignment
n Active mode: scalability in energy consumption exploits node’s operational diversityoDynamic voltage scaling
o Energy-quality scalable algorithms
oVariable-strength error correction
oCurrent profiles for maximum battery capacity
Idle Mode: Leakage Current Control
n Leakage dominates switching energy for low duty cycles
n A major concern for event-driven sensor operation
Measurements from SA-1100
Eswitch = CtotVDD2
Eleak = VDD (I0 e ) tVDDn’VT
Radio Considerations
n Startup energy can dominate transmission energy for short distances and packetsn Demands power-aware network protocolsn Favors buffering if latency is tolerable
101
102
103
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10510
-8
10-7
10-6
10-5
Packet Size
Ene
rgy/
bit (
J)
1 Mbps data rate0 dBm Tx power20 mW electronics
EFIXED = CVDD2
Active Mode: Dynamic Voltage Scaling
0.2 0.4 0.8 1.0
0.2
0.4
0.6
0.8
1.0
Normalized Workload
Nor
mal
ized
Ene
rgy
Fixed Supply
VariableSupply
0
0 0.6
ACTIVE IDLE
Fixed Power Supply
ACTIVE
EVAR = C (VDD/2)2 = EFIXED / 4
Variable Power Supply
12
12
DVS Implementation
SA-1100
Control
µOS
VoutController
Power
3.6V
Voltage request, 0.9 - 1.5 V
5
1.6V limiter
5
digitally adjustable DC-DC converter powers SA-1100 core
µOS selects appropriate clock frequency based on workload and latency constraints
SA-1100 requests a voltage appropriate for its clock frequency
Energy Characterization of SA-1100
Eswitch = CtotVDD2 Eleak = VDD (I0 e ) t
VDDn’VT
n Energy per operation at full processor utilization
fixed voltage operation
variablevoltageoperation
0 50 100 150 2000
0.2
0.4
0.6
0.8
1
Impulse Response Length
Nor
mal
ized
Ene
rgy
VariableVoltage
FixedVoltage
59MHz
73MHz
89MHz
206MHz
Application: Energy-Scalable Digital Filter
n Processor workload proportional to filter length
n µOS adjusts clock frequency (and voltage) with workload
w1[n]
wM[n]
w2[n]
s1[n]
y[n]s2[n]
sM[n]
Σ
Sensor Network
Beamforming Node
Algorithmic Considerations: Beamforming
n Data aggregation from multiple sensors into a single, high-SNR resultoData redundancy removed => less network transmission
o Energy-scalable: vary the number of input signals to beamformer
oHow is quality affected?
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43
21
50m
10m
target
B
A
Number of sensors
Qua
lity
(mat
ched
filte
r res
pons
e)
A
B
Beamforming Example
more energy consumed
LMS Beamforming in order (choosing sensors 1...N) leads to energy-quality variations with source location
Example scenario with sensor cluster, target, and interference source
interference
Number of sensors
Qua
lity
(mat
ched
filte
r res
pons
e)
A
B
Number of sensors
Qua
lity
(mat
ched
filte
r res
pons
e)
A
B
AfterBefore
n Most significant first transformation improves energy-quality characteristicsoQuicksort signals by their SNR; beamform with strongest signals
o Low overhead transformation: 0.44% overhead for 2-sensor (worst) case
Power-Aware Transformation
Conclusions
n Power-awareness means...oGraceful energy-quality scalability at all levels
oHardware-software collaboration to save energy
oAccounting for the unique power dissipation characteristics of the target application
n For a long lifetime, microsensor nodes must be power-aware