Ultra-Low Energy Wireless
Sensor and Monitor Networks
Jan M. Rabaey
http://www.eecs.berkeley.edu/~jan
In cooperation with Profs B. Brodersen, A. Sangiovanni-Vincentelli,
K. Ramchandran, P. Wright, and the PicoRadio group.
The New Internet
“The Berkeley Endeavour project”
The Vision
Localizers
“Tiny devices, chirping their impulse codes at one
another, using time of flight and distributed algorithms
to accurately locate each participating device. Several
thousands of them form the positioning grid … Together
they were a form of low-level network, providing
information on the orientation, positioning and the
relative positioning of the electronic jets…
It is quite self-sufficient. Just pulse them with
microwaves, maybe a dozen times a second …”
Pham Trinli, Thousands of years from now
Vernor Vinge,
“A Deepness in the Sky,” 1999
PicoRadio’s
Meso-scale low-cost (< 0.5 $) sensor-computationcommunication nodes for ubiquitous wireless
data acquisition that minimize power/energy dissipation
– Minimize energy (<5 nJ/(correct) bit) for energy-limited source
– Minimize power (< 100 mW) for power-limited source (enabling
energy scavenging)
By using the following strategies
– self-configuring networks
– fluid trade-off between communication and computation
– Integrated System-on-a-Chip, using aggressive low-energy
architectures and circuits
Target date: 2004
The Smart Home
Dense network of
sensor and monitor nodes
Security
Environment monitoring and control
Object tagging
Identification
Wireless in the Home
Source: IEEE Spectrum,
December 99
Industrial Building Environment
Management
•
•
•
Task/ambient conditioning systems allow thermal
condition in small, localized zones (e.g. work-stations)
to be individually controlled by building occupants
“micro-climates within a building”
Requires dense network of sensor/monitor nodes
Wireless infrastructure provides flexibility in
composition and topology
The Interactive Museum
Wireless
node
Offices
Entrance
Exhibits
Cafe
The Enhanced User Interface
The “virtual” keyboard
(Kris Pister, UCB)
Other Applications:
• Disaster mitigation, traffic management and control
• Integrated patient monitoring, diagnostics, and drug administration
• Automated manufacturing and intelligent assembly
• Toys, etc
System Requirements and Constraints
• Large numbers of nodes — between 0.05 and 1
nodes/m2
• Cheap (<0.5$) and small ( < 1 cm3)
• Limited operation range of network — maximum
50-100 m
• Low data rates per node — 1-10 bits/sec average
– up to 10 kbit/sec in rare local connections to potentially support
non-latency critical voice channel
• Low mobility (at least 90% of the nodes stationary)
• Crucial Design Parameter:
Spatial capacity (or density) — 100-200 bits/sec/m2
Fact or Fiction?
Today
Estimated: 2002-2003
Integrated radio
+ sensor on–a-chip
How to get there?
Network
level
Functional & Performance
Requirements
Network Architecture
Performance analysis
Constraints
Node
level
Functional & Performance
Requirements
Node Architecture
Performance analysis
Think Energy!
Opportunities
• Exploit the application properties
– Sensor data is correlated in time and space
– Sensor networks are “query-based”
– Sensing without precise localization seldom makes
sense
– Duty cycle of sensor nodes is very small
• And use node architectures that excel in the
“common case”
– Stream-based data flow processing for baseband
– Concurrent Finite State Machines for protocol stack
Power & energy dissipation
in ad-hoc wireless networks
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Plink
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bits
 a  b  dist 
 Pstandby
sec
actual _ bits
g
 e a  b  dist 
 Pstandby
sec
g
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Eactual_ bit  e  a  b  dist
g
  actual _ bits sec
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•
Pstandby
* These equations assume perfect power control
a: computation energy for
transceiving a single bit
b: transmission cost factor
for a single bit
g: path-loss exponent (2..4)
e: overhead (in extra bits
needed for transmission of
a single bit)
Pstandby: standby power (eg.,
due to need to keep
receiver on)
Saving Power at the Application Layer
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Plink
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
bits
 a  b  dist 
 Pstandby
sec
actual _ bits
g
 e a  b  dist 
 Pstandby
sec
g

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•
a: computation energy for
transceiving a single bit
b: transmission cost factor
for a single bit
g: path-loss exponent (2..4)
e: overhead (in extra bits
needed for transmission of
a single bit)
Pstandby: standby power (eg.,
due to need to keep
receiver on)
 actual_bits/sec: source coding
Opportunity: sensor data is correlated in time and space
Trading off communication for computation
Distributed Source Coding (Ramchandran)
• Sensor networks present major spatial data
correlation, but exploitation requires major intranode communication
Good theoretical question:
How much performance loss if such communication unavailable?
• Answer: no performance loss! (under certain
conditions) based on non-constructive
information-theoretic argument (Slepian-Wolf).
Engineering question:
How do we develop a practical and constructive framework
to optimize global network usage?
Saving Power at the Network Layer
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Plink
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bits
 a  b  dist 
 Pstandby
sec
actual _ bits
g
 e a  b  dist 
 Pstandby
sec
g





•
a: computation energy for
transmission of a single bit
b: transmission cost factor
for a single bit
g: path-loss exponent (2..4)
e: overhead (in extra bits
needed for transmission of
a single bit)
Pstandby: standby power (eg.,
due to need to keep
receiver on)
 dist: network partitioning using multi-hop
 e: cost of network discovery and maintenance
Opportunities:
 exploit application (i.e. sensoring) properties
 merge with localization
Communicating over Long Distances
Multi-hop Networks
Source
Dest
Example:
•
1 hop over 50 m
1.25 nJ/bit
•
5 hops of 10 m each
log(b/a)
5  2 pJ/bit = 10 pJ/bit
•
Multi-hop reduces transmission energy by 125!
(assuming path loss exponent of 4)
Optimal number of hops needed for
free space path loss.
But … network discovery
and maintenance overhead
Low-Energy Network Strategies
•
•
Reactive Routing good for rarely used routes
Proactive Routing good for frequently used routes
• Need solution that is more adequate for problem at hand:
class (contents)-based and location (geographic)-based
addressing.
Routing Overhead (bytes)
4000
3500
3000
2500
2000
1500
1000
500
0
Routing Overhead (bytes) Normalized
DSDV
AODV
20
33
56
Number of Nodes
(discovering one route)
16000
14000
12000
10000
8000
6000
4000
2000
0
20
33
56
Number of Nodes
(discovering n routes)
Example: Directed Diffusion (Estrin)
• Application-aware communication primitives
– expressed in terms of named data (not in terms of the nodes
generating or requesting data)
– Example: “Give me temperature information”
• Nodes diffuse the interest towards producers via a
sequence of local interactions
• This process sets up gradients in the network which
channel the delivery of data
• Reinforcement and negative reinforcement used to
converge to efficient distribution
• Intermediate nodes opportunistically fuse interests,
aggregate, correlate or cache data
• Other options: swarm intelligence
Distributed Positioning
Average Position Error (% of grid dimensions)
10 nodes, 25 waves, anchor weight=5, 30 iterations, 30% anchors, NO gradients
10
8
6
4
2
0
60
50
40
40
30
20
Range Error (%)
20
0
10
0
Initial Position Error (%)
Merging locationing with networking leads to pruning
of overhead messaging
Saving Power at the Media Access Layer (!)
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Plink


bits
 a  b  dist 
 Pstandby
sec
actual _ bits
g
 e a  b  dist 
 Pstandby
sec
g





•
a: computation energy for
transceiving a single bit
b: transmission cost factor
for a single bit
g: path-loss exponent (2..4)
e: overhead (in extra bits
needed for transmission of
a single bit)
Pstandby: standby power (eg.,
due to need to keep
receiver on)
 Pstandby: Power-management of inactive nodes
 e: Cost of collisions and retransmissions (interference)
Opportunities: distribution of communication in time and frequency
 rendez-vous scheduling in local neighborhood
 usage of multiple virtual channels reduces interference
Where Does Energy Go?
When idle: Channel Monitoring
Collision and Retransmission
Signaling overhead (header, control pkts)
Mostly-Sleepy MAC Layer Protocols
• Computational energy for receiving a bit is larger
than the computational energy to transmit a bit
(receiver has to discriminate and synchronize)
• Most MAC protocols assume that the receiver is
always on and listening!
• Activity in sensor networks is low and random
• Careful scheduling of activity pays off big time, but
… has to be performed in distributed fashion
PicoMAC
Spread Spectrum Multi-Channel Scheme
To Reduce Collision Rate
To Reduce Signaling Overhead (Shrink Address Space)
Truly Reactive Messaging
Power Down the Whole Data Radio
Reduce Monitoring Energy Consumption by 103 Times
Wakeup Radio will Power Up Data Radio for Data Reception
Saving Power at the Physical Layer



bits
 Pstandby
sec
actual _ bits
g
 e a  b  dist 
 Pstandby
sec
Plink  a  b  dist g 





•
a: computation energy for
transceiving a single bit
b: transmission cost factor
for a single bit
g: path-loss exponent (2..4)
e: overhead (in extra bits
needed for transmission of
a single bit)
Pstandby: standby power (eg.,
due to need to keep
receiver on)
 a: Choice of data rate, modulation, physical channel access
 e: Cost of framing, channel coding, synchronization, CRC
Opportunities: radio’s with fast acquisition (and probably less perfect channel)
The Mostly Digital Radio
Analog
Digital
cos[2(2GHz)t]
RF input
(fc = 2GHz)
I (50MS/s)
A/D
Digital
Baseband
Receiver
RF filter
LNA
A/D
Q (50MS/s)
chip boundary
sin[2(2GHz)t]
Integration/Power Tradeoff
• Example:
High carrier frequency allows small integrated
passive elements, but increases power consumption
Carrier Frequency Vs. Power Consumption
• Why?
10
– Increase Id to boost
device ft
– Increased power
consumption in PLLs
1
0.1
1
10
Power Consumption (mW)
100
0.1
1000
Carrier Frequency (GHz)
1.2V Receiver
Price Label Radio
WINS
Wireless Hearing Aid
Super-Regenerative
Pager
Philips Pager - UAA2080
Bluetooth
BWRC D.C. Radio
PicoRadio Implementation Issues
• Dynamic nature of PicoRadio networks requires
adaptive, flexible solution
• Traditional programmable platforms cannot meet
the stringent low-power requirements
– 3 orders of magnitude in energy efficiency between custom
and programmable solutions
• Configurable (parameterizable) architectures
combine energy efficiency with limited flexibility
• System-on-a-chip approach enables integration
of heterogeneous and mixed mode modules on
same die
• Predicted improvements: factor 10 each year!
The Energy-Flexibility Gap
Energy Efficiency
MOPS/mW (or MIPS/mW)
1000
Dedicated
HW
100
10
Reconfigurable
Processor/Logic
ASIPs
DSPs
Pleiades
10-80 MOPS/mW
2 V DSP: 3 MOPS/mW
1
Embedded Processors
SA110
0.4 MIPS/mW
0.1
Flexibility (Coverage)
(Re)configurable Computing:
Merging Efficiency and Versatility
Spatially programmed connection of processing elements.
“Hardware” customized to
specifics of problem.
Direct map of problem
specific dataflow, control.
Circuits “adapted” as
problem requirements
change.
Architecture Comparison
LMS Correlator at 1.67 MSymbols Data Rate
Complexity: 300 Mmult/sec and 357 Macc/sec
16 Mmacs/mW!
Note: TMS implementation requires 36 parallel processors to meet data rate validity questionable
Intercom TDMA MAC
Implementation alternatives
ASIC
FPGA
ARM8
Power 0.26mW
2.1mW
114mW
Energy 10.2pJ/op 81.4pJ/op n*457pJ/op
ASIC: 1V, 0.25 mm CMOS process
FPGA: 1.5 V 0.25 mm CMOS low-energy FPGA
ARM8: 1 V 25 MHz processor; n = 13,000
Ratio: 1 - 8 - >> 400

Idea: Exploit model of computation: concurrent finite state machines,
communicating through message passing
Envisioned PicoNode Platform
•
Reconfigurable
State Machines
Embedded uP
•
FPGA
Dedicated
DSP
Reconfigurable
DataPath
•
•
Small footprint directdownconversion R/F
frontend
Digital baseband
processing
implemented on
combination of fixed
and configurable
datapath structures
Protocol stack
implemented on
combination
FPGA/reconfigurable
state machines
Embedded
microprocessor running
at absolute minimal
rates
PicoNode I
•
Motorola StarTac
Cellular Battery (3.6V)
Serial Port
Window
•
•
•
Casing Cover
Connectors for
sensor boards
•
Pico Radio
Test Bed
Flexible platform for
experimentation on
networking and
protocol strategies
Size: 3”x4”x2”
Power dissipation <
1 W (peak)
Multiple radio
modules: Bluetooth,
Proxim, …
Collection of sensor
and monitor cards
PicoNode II (two-chip)
Custom
analog
circuitry
Mixed
analog/
digital
Fixed
logic
Programmable
logic
Embedded
Processor
(Xtensa)
Software
running on
processor
Memory
Protocol
Sub-system
Interconnect Network (Sonics Silicon Backplane)
ADC
Analog RF
DAC
Chip 1
Direct down-conversion front-end
(Yee et al)
Digital
Baseband
Baseband
processing
Processing
Fixed
Protocol Stack
Programmable
Protocol Stack
(FPGA)
Chip 2
The Holy Grail: Energy Scavenging
Power (Energy) Density
Batteries (Zinc-Air)
Batteries(Lithium ion)
Source of Estimates
3
1050 -1560 mWh/cm (1.4 V)
Published data from manufacturers
3
300 mWh/cm (3 - 4 V)
Published data from manufacturers
2
15 mW/cm - direct sun
Solar (Outdoors)
2
0.15mW/cm - cloudy day.
Published data and testing.
2
.006 mW/cm - my desk
Solar (Indoor)
Vibrations
2
0.57 mW/cm - 12 in. under a 60W bulb
Testing
3
0.001 - 0.1 mW/cm
Simulations and Testing
2
3E-6 mW/cm at 75 Db sound level
Acoustic Noise
Passive Human
Powered
9.6E-4 mW/cm2 at 100 Db sound level
1.8 mW (Shoe inserts >> 1 cm )
Published Study.
Thermal Conversion
0.0018 mW - 10 deg. C gradient
Published Study.
Direct Calculations from Acoustic Theory
2
3
80 mW/cm
3
Nuclear Reaction
1E6 mWh/cm
3
300 - 500 mW/cm
Fuel Cells
~4000 mWh/cm
3
SOURCE:
P. Wright & S. Randy
UC ME Dept.
Published Data.
Published Data.
Example: MEMS Variable Capacitor
Out of the plane, variable gap capacitor
springs
50m
500m
Proof mass
Integrated Manufacturing Lab
Up to 10 mW of power demonstrated
PicoRadio Design Challenges
6
7
PicoNode Architecture Design
10
18
17
11
19
20
12
16
13
15
14
Positioning
Network
Architecture
Power (Energy) Density
FPGA
Embedded uP
Batteries
(Zinc-Air) 1050 -1560 mWh/cm3
Batteries
(rechargeable Lithium) 300 mWh/cm3 (3 - 4 V)
15 mW/cm2 - direct sun
Solar
1mW/cm2 - ave. over 24 hrs.
Vibrations
0.05 - 0.5 mW/cm3
Inertial Human Power
Acoustic Noise
Non-Inertial Human
Power
Nuclear Reaction
One Time Chemical
Reaction
Dedicated FSM
3E-6 mW/cm2 at 75 Db
9.6E-4 mW/cm2 at 100 Db
Dedicated
DSP
1.8 mW (Shoe inserts)
80 mW/cm3
1E6m Wh/cm3
Reconfigurable
DataPath
Performance Analysis
Fluid Flow
Fuel Cells
300 - 500 mW/cm3
~4000 mWh/cm3
Energy Constraints
PicoNode

hopsoptimal  ceil dist Total 10g
Offices
Entrance
Exhibits
Use Cases
Cafe
Conceptual Modeling
