Sensor Networks, Aeroacoustics, and Signal Processing ICASSP 2004 Tutorial Brian M. Sadler Richard J. Kozick 17 May 2004

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Sensor Networks, Aeroacoustics,and Signal
ProcessingICASSP 2004 TutorialBrian M.
SadlerRichard J. Kozick17 May 2004

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Sensor Network Publication Trend
NSF Boost Phase

Source IEEE Xplore, sensor networks (IEEE only)
3
Sensor Networks, Aeroacoustics,and Signal
ProcessingIntl. Conf. on Acoustics,
Sensor-Nets, and Signal Proc.Brian M.
SadlerRichard J. Kozick17 May 2004

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CaveatsSP 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
ProcessingICASSP 2004 TutorialPart I
Overview of Sensor NetworksBrian M.
SadlerRichard J. Kozick17 May 2004

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Modalities and Applications
Application Domains

• Point sources
• Detection, estimation, geolocation, tracking
  moving sources
• Imaging sampling a field
• Environment (e.g., temperature, atmosphere)
• Monitoring dedicated sensor / source groupings
  (IEEE 802.15.4 / ZigBee)
• Assembly lines, machines, hospital patients, home
  intrusion
• Logistics where is it?, what condition?
• Warehouse, dock, container, on-ship
• Mobility Control
• Robotics, UAVs

• Sensing Modalities
• acoustic, seismic
• vibration, tilt
• thermal, humidity, barometer
• NBC (nuke / bio / chemical)
• magnetic, RF
• light
• high bandwidth (video, IR)
• etc!
• Active sensing
• radar, RF tags
• A range of environments
• 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 Moores 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
Ad Hoc Mobile Network Aggregate 200 Mbps
Capability

• DoD ad hoc network experiment (mobile high
  QoS)
• Network overhead dominates
• Fixed overhead increasingly less efficient as
  duty cycle decreases

512 byte packet, 32 mcps FEC 1/2 _at_ 4000 kbps
maximum burst

• Headers for each level
• Timing
• Status
• etc

From SUO SAS TIM, June 12 13 2001

• Does Not Include Initial Acquisition, Other Entry
  Requests, TCP, Routing Table, and Related
  Bandwidth Requirements

Chip-scale sensor
Chip-scale radio
Actual Application 1.8 Mbps Data ? 0.9
The future?
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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 (k1)
• 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
• 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?

r
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Connectivity

• 1970s - 80s 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 multi-user 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
• Fundamental limit due to common access
• Splitting channel does not change things
• e.g., FDMA, base-stations
• P-to-P traffic model for sensor nets
• the right one?

• Assumptions
• 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
• 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

circa 1908
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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 chip-scale 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 (21 days) gt1 PPM
Watch clock 200 x 10-6 1 micro W 342 yrs Temp (98.6 ), aging
o

• 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
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Hardware Trends

• Sensing, signal processing, radio
• clock, PA, receiver complexity
• State transitions
• duty cycling off, idle, SP, listen, communicate
• 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

ARL Blue Radio
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An Energy Model

• Coarse energy consumption
• 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

Total will incorporate duty cycles
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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
Performance a function of Poisson sensor
distribution sensor density SNR MAC
throughput (finite collection time)
probability no sensor in interval
Processing Steps 1 sensor snapshot 2
information retrieval 3 field reconstruction
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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(1-D) Signal Field Reconstruction
Sampling MAC - 3

• The signal field (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
Sensor Outage Probability (no sensor in interval)
MAC Throughput packets/slot
Schedule one packet per resolution interval of
length
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Sampling MAC - 5

r distortion ratio of random access to
scheduling

• Relative performance depends critically on
• (scheduling less robust)
• Random access may be easier to implement

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Sampling MAC - 6
Deterministic scheduling
random access

• If expect of sensors in interval gt ,
  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 Moores 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
• takes long term view
• facilitates parallel engineering, ensures
  interoperability
• lowers cost, leads to wide implementation
• Tension between performance and architecture
  Kawadia Kumar 2003
• cross-layer tangled spaghetti ?
• What architecture for low-energy sensor nets?
• limits on performance
• optimal layer interaction feedback
• what information is passed?
• provable stability needed
• widely varying application space

Transport
Network
Link
Physical
OSI Wired World

• Wireless Sensor-Net 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
ProcessingICASSP 2004 TutorialEnd of Part I
Overview of Sensor NetworksBrian M.
SadlerRichard J. Kozick17 May 2004