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Sensor Network Publication Trend
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Transcript of Sensor Network Publication Trend
17 May 2004 ICASSP Tutorial I-1
Sensor Networks, Aeroacoustics,and Signal Processing
ICASSP 2004 Tutorial
Brian M. SadlerRichard J. Kozick
17 May 2004
17 May 2004 ICASSP Tutorial I-2
Sensor Network Publication Trend
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2000 2001 2002 2003
Journal
Conference
Source: IEEE Xplore, “sensor networks” (IEEE only)
NSF Boost Phase
17 May 2004 ICASSP Tutorial I-3
Sensor Networks, Aeroacoustics,and Signal Processing
Intl. Conf. on Acoustics, Sensor-Nets, and Signal Proc.
Brian M. SadlerRichard J. Kozick
17 May 2004
17 May 2004 ICASSP Tutorial I-4
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
17 May 2004 ICASSP Tutorial I-5
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.
17 May 2004 ICASSP Tutorial I-6
Sensor Networks, Aeroacoustics,and Signal Processing
ICASSP 2004 Tutorial
Part I: Overview of Sensor Networks
Brian M. SadlerRichard J. Kozick
17 May 2004
17 May 2004 ICASSP Tutorial I-7
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, UAV’s
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!
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!
17 May 2004 ICASSP Tutorial I-8
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 communications1 or multi-hop to fixed infrastructure vs no fixed infrastructurehomogeneous vs non-homogeneous nodes (“base stations”)synchronization (beacons, message passing) & geolocationdegree of robustnesshighly variable RF propagation conditions
• and morerandom vs deterministic placementsensor density
17 May 2004 ICASSP Tutorial I-9
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
17 May 2004 ICASSP Tutorial I-10
Mobility and Overhead
Headers for each levelTimingStatusetc
From SUO SAS TIM, June 12 & 13 2001
512 byte packet, 32 mcps & FEC = 1/2 @ 4000 kbps maximum burst
Actual Application 1.8 Mbps Data 0.9 %
Ad Hoc Mobile Network Aggregate 200 Mbps Capability
• Does Not Include Initial Acquisition, Other Entry Requests, TCP, Routing Table, and Related Bandwidth Requirements
Bits (in K) Reduction % Payload Transmission Capacity of 50 Radios 200,000 Half-Duplex Operation 100,000 100,000 Channel Contention @ 5 Radio Density 40,000 60,000 UDP Header 39,385 615 34% IP Header 37,647 1,738 95% COMSEC Header 36,571 1,076 59% Radio Network Header 36,120 451 25% Radio Link Layer Header 35,679 441 24% Modem Framing & CRC 35,068 611 34% Forward Error Correction 17,534 17,534 Waveform Framing 17,491 43 2% Synchronization Probe Overhead 13,378 4,113 226% Slot Quantization @ 1.2 ms per Slot 11,378 2,000 110% Channel Acquisition (RTS/CTS) 6,827 4,551 250% Frame Acknowledge 5,689 1,138 62% Average Contention Interval (1.44 slots) 4,588 1,101 60% Average Number of Transmissions per packet 1,821 2,767 Candidate Packet Overhead 982%
• DoD ad hoc network experiment (mobile & high QoS)
• Network overhead dominates
• Fixed overhead increasingly less efficient as duty cycle decreases
Chip-scalesensor
Chip-scaleradio
The future?
17 May 2004 ICASSP Tutorial I-11
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
17 May 2004 ICASSP Tutorial I-12
Outline
• Intro & Energy Themes
• Architectures & Connectivity
• Some Fundamental Limits
• Clocks & Synchronization
• Hardware Trends
• Node Localization
• Medium Access Control & Routing
• Conclusions
17 May 2004 ICASSP Tutorial I-13
Architectures
• flat
• cluster, hierarchical
• mobile collectors
• mobile nodes / robotics / UAVs
• k-hop to fixed infrastructure (k=1)
the likely dominant commercial paradigm
17 May 2004 ICASSP Tutorial I-14
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
17 May 2004 ICASSP Tutorial I-15
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 multi-user interference
• Relation with sensor coverage?
e.g., Nyquist sampling, detection coverage
17 May 2004 ICASSP Tutorial I-16
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
Assumptions Fully connected Geolocated nodes Global routes known Perfect slot timing &
scheduling Power control Interference = noise
(no multi-user det.) Arbitrary delay
17 May 2004 ICASSP Tutorial I-17
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]
17 May 2004 ICASSP Tutorial I-18
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]
17 May 2004 ICASSP Tutorial I-19
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
17 May 2004 ICASSP Tutorial I-20
Oscillator Accuracy
• 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
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)
>1 PPM
Watch clock 200 x 10-6 1 micro W 342 yrs Temp (98.6 ), aging
o
17 May 2004 ICASSP Tutorial I-21
Clock Drift and Resync Times
Clock Resync Time for Differing Guard bands and Clock Accuracies
0.01
0.10
1.00
10.00
100.00
1000.00
0 0.001 0.002 0.003 0.004 0.005
TDMA Slot Time Sec
Clo
ck R
esyn
c T
ime
Hr
1ppm
0.1ppm
0.01ppm
0.001ppm
17 May 2004 ICASSP Tutorial I-22
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
17 May 2004 ICASSP Tutorial I-23
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
17 May 2004 ICASSP Tutorial I-24
Power Amplifier & Efficiency
Power control vs PA efficiency
variable voltage supply to maximize PA use
PAPR an issue with non-constant modulus modulations (OFDM)
17 May 2004 ICASSP Tutorial I-25
Localization & 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
Where are my nodes? Location, orientation, & calibration.
17 May 2004 ICASSP Tutorial I-26
Medium Access Control (MAC)
• Scheduling & duty cycling to eliminate idle listening (TDMA)
Deterministic (peer-to-peer), perhaps pseudo-random, in clusters
Issues:
scalabilitylatency 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 dominateshigh – energy spent listening dominates
How do we efficiently share the common medium?
17 May 2004 ICASSP Tutorial I-27
Medium Access Control (MAC)
• 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
PHY / MAC cross-layer design
17 May 2004 ICASSP Tutorial I-28
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.
17 May 2004 ICASSP Tutorial I-29
Sampling & MAC - 1
Random Access Deterministic Scheduling
Processing Steps1 sensor snapshot2 information retrieval3 field reconstruction
Performance a function of:Poisson sensor distribution sensor density & SNR MAC throughput (finite collection time) = probability no sensor in interval
Consider field reconstruction fidelity under 2 sampling schemes.
[Dong, Tong, Sadler, 02, 04]
17 May 2004 ICASSP Tutorial I-30
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)
17 May 2004 ICASSP Tutorial I-31
(1-D) Signal Field Reconstruction
)(xS
)(xY
• 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)
)(ˆ xS
)(xS
Sampling & MAC - 3
17 May 2004 ICASSP Tutorial I-32
Sampling & MAC - 4
Sensor Outage Probability (no sensor in interval)
ePout
MAC Throughput
packets/slot
MAC Assumptions:
• Slotted transmission in a collision channel
• Fixed collection time: M slots
# of packets collection is a r.v.
Schedule one packet per resolution interval of length
(1) Random Access (2) Deterministic Scheduling
17 May 2004 ICASSP Tutorial I-33
Sampling & MAC - 5
r = distortion ratio of random access to scheduling
)ln
1(
1ln)
lnlnln
(1
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(1ln
out
o(1))(1
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MO
PMM
O
MM
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•
•
1:))1(1(out reP o
1:))1(1(out reP o
• Relative performance depends critically on (scheduling less robust)
• Random access may be easier to implement
outP
17 May 2004 ICASSP Tutorial I-34
Sampling & MAC - 6
random access
Deterministic scheduling
))1(1( o
• If expect # of sensors in interval > , then scheduled collection is preferred
• Or, given sensor density , choice of dictates appropriate collection regime
17 May 2004 ICASSP Tutorial I-35
Routing
• 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)
Some rough classes of algorithms
Issues: route discovery, scalability [Santivanez et al 02], global vs local,
provably good performance, comms load (energy), mobility
17 May 2004 ICASSP Tutorial I-36
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
17 May 2004 ICASSP Tutorial I-37
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
17 May 2004 ICASSP Tutorial I-38
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
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
17 May 2004 ICASSP Tutorial I-39
Sensor Networks, Aeroacoustics,and Signal Processing
ICASSP 2004 Tutorial
End of Part I: Overview of Sensor Networks
Brian M. SadlerRichard J. Kozick
17 May 2004