I-1 17 May 2004 ICASSP Tutorial 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 Networks, Aeroacoustics,and Signal Processing

ICASSP 2004 Tutorial

Brian M. SadlerRichard J. Kozick

17 May 2004


Sensor Network Publication Trend

0

50

100

150

200

250

2000 2001 2002 2003

Journal

Conference

Source: IEEE Xplore, “sensor networks” (IEEE only)

NSF Boost Phase



Intl. Conf. on Acoustics, Sensor-Nets, and Signal Proc.


17 May 2004


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


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.




Part I: Overview of Sensor Networks


17 May 2004


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!


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


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


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?


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


Outline

• Intro & Energy Themes

• Architectures & Connectivity

• Some Fundamental Limits

• Clocks & Synchronization

• Hardware Trends

• Node Localization

• Medium Access Control & Routing

• Conclusions


Architectures

• flat

• cluster, hierarchical

• mobile collectors

• mobile nodes / robotics / UAVs

• k-hop to fixed infrastructure (k=1)

the likely dominant commercial paradigm


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


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


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


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]


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]


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


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


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


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


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


Power Amplifier & Efficiency

Power control vs PA efficiency

variable voltage supply to maximize PA use

PAPR an issue with non-constant modulus modulations (OFDM)


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.


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?



• 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



• 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.


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]


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)


(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


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


Sampling & MAC - 5

r = distortion ratio of random access to scheduling

)ln

1(

1ln)

lnlnln

(1

)lnlnln

(1ln

out

o(1))(1

1-

out

MO

PMM

O

MM

OPr

•

•

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


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


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


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


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


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




End of Part I: Overview of Sensor Networks


17 May 2004

Documents

I-1 17 May 2004 ICASSP Tutorial Sensor Networks, Aeroacoustics, and Signal Processing ICASSP 2004 Tutorial Brian M. Sadler Richard J. Kozick 17 May 2004