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Sensor Networks, Aeroacoustics,
and Signal Processing
ICASSP 2004 Tutorial
Brian M. Sadler
Richard J. Kozick
17 May 2004
17 May 2004
ICASSP Tutorial
I-1
Sensor Network Publication Trend
NSF
Boost Phase
250
200
150
Journal
Conference
100
50
0
2000
2001
2002
2003
Source: IEEE Xplore, “sensor networks” (IEEE only)
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Sensor Networks, Aeroacoustics,
and Signal Processing
Intl. Conf. on Acoustics, Sensor-Nets, and Signal Proc.
Brian M. Sadler
Richard J. Kozick
17 May 2004
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Caveats
SP & SP-Comms Perspective, Finite Citations, RMF*
Acknowledgements
S. Collier, M. Dong, P. Marshall, S. Misra, T. Moore, R. Moses, T. Pham, N. Shroff,
N. Srour, A. Swami, R. Tobin, L. Tong, D. K. Wilson, Q. Zhao, T. Zhou, etc!
*rapidly moving field
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Outline
•
Part 1: Overview of Sensor Networks
– Consider the rich interplay between sensing,
signal processing, and communications, with
a focus on energy preserving strategies.
•
Part 2: Aeroacoustic Sensor Networks
– Application of aeroacoustic sensing with
distributed nodes, including propagation
effects, and optimal signal processing, under
communication constraints.
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Sensor Networks, Aeroacoustics,
and Signal Processing
ICASSP 2004 Tutorial
Part I: Overview of Sensor Networks
Brian M. Sadler
Richard J. Kozick
17 May 2004
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Modalities and Applications
Application Domains
• Point sources
Sensing Modalities
Detection, estimation, geolocation,
tracking moving sources
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• Imaging: sampling a field
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Environment (e.g., temperature,
atmosphere)
• Monitoring: dedicated sensor / source
groupings (IEEE 802.15.4 / ZigBee)
Assembly lines, machines, hospital
patients, home intrusion
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Active sensing
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• Logistics: where is it?, what condition?
Warehouse, dock, container, on-ship
• Mobility & Control
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ICASSP Tutorial
radar, RF tags
A range of environments

Robotics, UAV’s
acoustic, seismic
vibration, tilt
thermal, humidity, barometer
NBC (nuke / bio / chemical)
magnetic, RF
light
high bandwidth (video, IR)
etc!
home, office, factory
toxic, inhospitable, remote
etc!
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Rich Multi-Disciplinary Interplay
Ad hoc networking
Sensing / physics / propagation
Low power / adaptive hardware
Controls, robotics, avionics
Types of constraints
• Energy
battery vs continuous power supply
• Wireless communications
1 or multi-hop to fixed infrastructure vs no fixed infrastructure
homogeneous vs non-homogeneous nodes (“base stations”)
synchronization (beacons, message passing) & geolocation
degree of robustness
highly variable RF propagation conditions
• and more
random vs deterministic placement
sensor density
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What is a Sensor Network?
• Postulate (something for everyone)
Given any definition of a sensor network, there exists a counter-example.
Extremely varied requirements, environments, comms ranges and
propagation conditions, and power constraints.
• Our
focus
Energy constrained, battery driven, robust radio communications with
little or no fixed infrastructure
(other possible comms: acoustic, laser, UV)
• DSP / MEMS / Nano & Moore’s Law vs Shannon / Maxwell
Digital Processing Power Requirements Drop by Factor of 1.6/Year
Eb/No Required Remains Constant
Maximum lifetime implies minimal communications
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Mobility and Overhead
• DoD ad hoc network
experiment (mobile &
high QoS)
• Network overhead
dominates
• Fixed overhead
increasingly less
efficient as duty cycle
decreases
Headers for each level
Timing
Status
etc
Chip-scale
sensor
Chip-scale
radio
The future?
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Ad Hoc Mobile Network
Aggregate 200 Mbps Capability
512 byte packet, 32 mcps & FEC = 1/2 @ 4000 kbps maximum burst
Transmission Capacity of 50 Radios
Half-Duplex Operation
Channel Contention @ 5 Radio Density
UDP Header
IP Header
COMSEC Header
Radio Network Header
Radio Link Layer Header
Modem Framing & CRC
Forward Error Correction
Waveform Framing
Synchronization Probe Overhead
Slot Quantization @ 1.2 ms per Slot
Channel Acquisition (RTS/CTS)
Frame Acknowledge
Average Contention Interval (1.44 slots)
Average Number of Transmissions per packet
Candidate Packet Overhead
Actual Application
Data ICASSP Tutorial
Bits (in K) Reduction % Payload
200,000
100,000 100,000
40,000
60,000
39,385
615
34%
37,647
1,738
95%
36,571
1,076
59%
36,120
451
25%
35,679
441
24%
35,068
611
34%
17,534
17,534
17,491
43
2%
13,378
4,113
226%
11,378
2,000
110%
6,827
4,551
250%
5,689
1,138
62%
4,588
1,101
60%
1,821
2,767
982%
From
SUO
SAS
TIM,
June
12
&
13
2001
• Does Not Include Initial Acquisition,
Other Entry Requests, TCP,
Routing Table, and Related
Bandwidth Requirements
1.8 Mbps
 0.9 %
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Energy Themes
• Reduce communications to a minimum
Idle listening & duty cycling
Reduction of protocol overhead
• Common channel access limits communications
performance
Medium access control (MAC) a critical element
• Coordinated signal processing
Collaborative & distributed signal processing vs centralized
Optimality and performance under communications constraints
• Specialized low power hardware
DSP, clocks, radios
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Outline
• Intro & Energy Themes
• Architectures & Connectivity
• Some Fundamental Limits
• Clocks & Synchronization
• Hardware Trends
• Node Localization
• Medium Access Control & Routing
• Conclusions
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Architectures
• flat
• cluster, hierarchical
• mobile collectors
• mobile nodes / robotics / UAVs
• k-hop to fixed infrastructure (k=1)
 the likely dominant commercial paradigm
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Connectivity
• Connectivity: multi-hop path exists between all (or desired) nodes
• Connectivity is a function of:
Radio channels, power assignment (control), node locations (density),
traffic matrix
• Model
r
n total nodes, obey Poisson distribution
geometric path loss
radius r connectivity
• What density to ensure connectivity?
• Does this scale with area for fixed density?
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Connectivity
• [1970’s - 80’s] “Magic number” = 6
(2 to 8 perhaps)
Postulate: connecting with approx 6 neighbors ensures connectivity with
very high probability
Under Poisson model with fixed node density, as area grows then there is
a finite probability of disconnection
• Scaling
Each node should be connected to O(log n) nearest neighbors, so
prob(connected)  1. [Philips, et al 1989; Xue Kumar 2004]
Implies a connectivity – capacity tradeoff due to increased multiuser interference
• Relation with sensor coverage?
e.g., Nyquist sampling, detection coverage
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Ad Hoc Network Capacity
• Define new notion of network capacity [Gupta Kumar 2000]
(aggregate transport capacity, bit-meters / sec)
• Comms between random i-j node pairs (peer-to-peer, multi-hop,
random planar network)
• For n nodes, and W Hz shared channel, at best throughput (bits/sec)
for each node scales as
Assumptions

• Fundamental limit due to common access
• Splitting channel does not change things
e.g., FDMA, base-stations
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• P-to-P traffic model for sensor nets
the right one?
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Fully connected
Geolocated nodes
Global routes known
Perfect slot timing &
scheduling
Power control
Interference = noise
(no multi-user det.)
Arbitrary delay
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Correlated Traffic
• Many (most?) sensor network traffic models are highly correlated
• Correlation can be exploited with distributed compression (coding)
when transmitting to a common destination [Slepian Wolf 1973]
fundamental limit on data reduction
requires known correlation model
• Many-to-One Transport Capacity
Even with optimal (Slepian-Wolf) compression assumed, flat
architecture with single collector does not scale [Marco, Duarte-Melo,
Liu, Neuhoff, 2003]
• Leads naturally to routing schemes, e.g., trees, data aggregation
[Scaglione, Servetto, 02, 04]
• Development of practical distributed coding schemes continues
e.g., [Pradhan, Kusuma, Ramchandran, 02]
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Mobility brings Diversity
• Dramatic gains in capacity limit if mobility is introduced, i.e., network
topology is time-varying [Grossglauser Tse 02]
store and forward paradigm, delay finite but arbitrary
throughput can now be
, i.e., not decreasing with n
• Delay – Capacity tradeoff in mobile ad hoc networks
e.g., mobile network capacity can exceed that of stationary network,
even with bounded delay [Lin Shroff 04]
“iid mobility” model
• Mobility (time / channel diversity) can greatly increase throughput in
random access schemes (e.g., ALOHA), when channel knowledge or
multi-packet reception is utilized,
e.g., [Tong Naware Venkitasubramaniam 04]
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Time Synchronization
• Levels of Timing
(carrier phase, symbol boundary)
data fusion, event detection, state update
MAC: scheduling / duty cycling, TDMA slots
• Message frequency vs timing accuracy
circa 1908
exploit piggy-backing, broadcasting
extrapolation possible (forward and
backward)
• Pairwise vs global synch
e.g., iterative global LS solution
several protocols devised in literature
comms update rates critical
micro-secs accuracies reported
experimentally
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Oscillator Accuracy
Accuracy
Power
Lifetime with
AA battery
AA = 10,800 J
(3 W-Hrs)
GPS
10-8 -- 10-11
180 mW
16.7 hrs
beacon,
outdoor, cost
DARPA chipscale atomic clk
10-11
30 mW
100 hrs
program goals
MCXO
3 x 10-8
75 mW
40 hrs
large, aging
drift
TCXO
6 x 10-6
6 mW
500 hrs
>1 PPM
(21 days)
Watch clock
200 x 10-6
1 micro W
342 yrs
Temp (98.6 o),
aging
• Increased network timing accuracy increases lifetime and throughput
• With high duty cycling, clock becomes dominant energy consumer
• Low power GPS clocks likely to be developed, but …
• Beacons must be robust for DoD application
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Clock Drift and Resync Times
Clock Resync Time for Differing Guard bands and Clock
Accuracies
Clock Resync Time Hr
1000.00
100.00
1ppm
0.1ppm
0.01ppm
0.001ppm
10.00
1.00
0.10
0.01
0
0.001
0.002
0.003
0.004
0.005
TDMA Slot Time Sec
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Hardware Trends
• Sensing, signal processing, radio
clock, PA, receiver complexity
• State transitions
duty cycling: off, idle, SP, listen, communicate
ARL “Blue” Radio
turn-on consumes energy, balance against length of off-time
• Performance – energy tradeoffs
dynamic voltage scaling yields variable latency
slow DSP clock to accommodate time allowed for the job
multiple DSP bit-widths, i.e., FLOPS at different quantizations
“domain-specific” DSP suite
• Energy harvesting
vibration, solar, thermal
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An Energy Model
• Coarse energy consumption
Total will incorporate
duty cycles
 receiver energy may dominate
idle listening vs duty cycling & synch on receive
scheduling: multiple listeners vs perfect scheduling
short range desirable, but node density high (application?)
• Definition of Network Lifetime? - application & node density dependent
(i) first (or j) node failures
(ii) first (or k) network partitions appear
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Power Amplifier & Efficiency
Power control vs PA efficiency
variable voltage supply to maximize PA use
PAPR an issue with non-constant modulus modulations (OFDM)
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Localization & Calibration
Where are my nodes? Location, orientation, & calibration.
• Employ internal / external beacons
Deploy beacons within network; GPS limitations & cost
• Self-localization – use radio or exploit sensor modality
RF requires sufficient TB product, acoustic / other possible
Mixed modality possible, e.g, rcvd signal strength (RSS) & AOA mix
Fundamental limits: CRB analysis [Garber Moses 2003]
desired sensor connectivity approx 5
always have residual uncertainty
• Relative vs absolute location
Anchored network (e.g., GPS)
• Sensor calibration
Temperature, aging
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Medium Access Control (MAC)
How do we efficiently share the common medium?
• Scheduling & duty cycling to eliminate idle listening (TDMA)
Deterministic (peer-to-peer), perhaps pseudo-random, in clusters
Issues:
scalability
latency vs energy (duty cycle rate)
time variation (new joins, drop outs, channel changes, mobility)
synchronization (clock drift)
broadcasting (mode switch)
• Random access (e.g., ALOHA)
Issues: collisions & energy loss, idle listening
Slotted employs scheduling (hybrid: random access & TDMA)
Optimal duty cycle possible
low – energy to find neighbor dominates
high – energy spent listening dominates
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Medium Access Control (MAC)
PHY / MAC cross-layer design
• Multi-user detection significantly enhances random access performance
(2 or 3 users, relatively simple SP), e.g., [Adireddy, Tong, 02]
• Dual-channel transceiver
e.g., busy-tones in random access (CSMA-MA)
• Further issues:
broadcasting
monitoring, “heartbeat” & synch, maintain connectivity
polling from clusterhead vs event driven
adaptive frame size & heavy-tailed (bursty) traffic
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Medium Access Control (MAC)
• MAC typically comes with large range of tunable parameters
Analysis challenging, reliant on simulations & small experiments
Optimality measures?
Scalability?
Markov model for energy consumption, e.g., [Zorzi, Rao, 03]
• Optimality depends on variable factors
Applications & traffic models
Node density (perhaps highly varying in same network)
QoS required? (may be time varying, e.g., how & when to ACK?)
Latency required? (see QoS above)
Solutions provide various tradeoffs. Provable performance elusive.
Adaptability and flexibility important if variety of service desired.
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Sampling & MAC - 1
Consider field reconstruction fidelity under 2 sampling schemes.
Random Access
Deterministic Scheduling
Processing Steps
1 sensor snapshot
2 information retrieval
3 field reconstruction
Performance a function of:
Poisson sensor distribution
sensor density & SNR
MAC throughput (finite collection time)
= probability no sensor in interval
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[Dong, Tong, Sadler, 02, 04]
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Sampling & MAC - 2
A Mobile Collection Architecture
• Move network functions away from
sensors to mobile APs
• Network via mobility
• Connect only when needed
• Design for fraction of packets, from
fraction of sensors (no one sensor is
critical)
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Sampling & MAC - 3
(1-D) Signal Field Reconstruction
S (x )
Y (x )
Sˆ ( x )
• The signal field S (x ) (Gaussian, Markov)
• Poisson sensor field with density 
• Signal reconstruction via MMSE smoothing
• Performance measure: average maximum distortion
of reconstruction (pair-wise sensor spacing critical)
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Sampling & MAC - 4
MAC Assumptions:
• Slotted transmission in a collision channel
• Fixed collection time: M slots
# of packets collection is a r.v.
(1) Random Access
(2) Deterministic Scheduling

MAC Throughput
 packets/slot
Sensor Outage Probability
(no sensor in interval)
Pout  e  
Schedule one packet per resolution
interval of length 
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Sampling & MAC - 5
  (1 o (1))
: r 1
• Pout  e
• Pout  e   (1 o (1)) : r  1
-1
 (1 o(1))
out
r  ln P
ln ln M
)
ln M
ln ln M
1
1
1  O(
)  ln
O(
)
ln M
Pout ln M
1  O(
r = distortion ratio of random
access to scheduling
• Relative performance depends critically on Pout
(scheduling less robust)
• Random access may be easier to implement
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Sampling & MAC - 6

Deterministic scheduling
   (1  o(1))
random access
• If expect # of sensors in  interval >  , then
scheduled collection is preferred
• Or, given sensor density , choice of  dictates
appropriate collection regime
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Routing
Some rough classes of algorithms
• Energy-aware cost
parameters: delay, range, hop count, battery level, etc
heterogeneous nodes with highly variable energy resources
• Directed Diffusion:
Query-based, data-dependent routes, controlled flooding (establish
“gradients”), e.g., tracking
• Clustering algorithms
Supports hierarchical signal processing
• Geographically-based (e.g., geographic forwarding)
Issues:
route discovery, scalability [Santivanez et al 02], global vs local,
provably good performance, comms load (energy), mobility
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Odds and Ends
• Security, authentication, encryption
• Broadcasting
• Node management & maintenance
• Collaborative transmission
• Relay
regenerative and non-regenerative
analog vs digital
• Antennas, propagation
• Iterative distributed detection & estimation
• Tracking
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Conclusions
• Its all about energy
Reduce idle listening, new adaptive hardware, accurate & low
power clocks
• SP, MAC, and Routing are fundamentally interrelated
application dependent, cross-layer design
• Large scaling is problematic
Common channel = interference, correlated traffic flows, leads
naturally to clustering
Exploit mobility, heterogeneous nodes
• No Moore’s Law for batteries (ever?)
Energy harvesting
• Local vs global SP tradeoffs
Maximum performance with minimal communications
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Conclusions – Cross-Layer Design
• Layered architecture
Transport
takes long term view
Network
facilitates parallel engineering, ensures
interoperability
Link
Physical
lowers cost, leads to wide implementation
• “Tension between performance and
architecture” [Kawadia Kumar 2003]
cross-layer = tangled spaghetti ?
Wireless Sensor-Net World

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• What architecture for low-energy
sensor nets?

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limits on performance

optimal layer interaction & feedback
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what information is passed?

provable stability needed
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widely varying application space
17 May 2004
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ICASSP Tutorial
OSI Wired
World
Multi-antenna
Multi-user detection
Synchronization
Beacons & robust comm
Adapt. modulation & coding
Geolocation
Hierarchical & distr. SP
Mobility
Variable QoS
Routing metric
Non peer-to-peer
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Sensor Networks, Aeroacoustics,
and Signal Processing
ICASSP 2004 Tutorial
End of Part I: Overview of Sensor Networks
Brian M. Sadler
Richard J. Kozick
17 May 2004
17 May 2004
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