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Sensor Networks, Aeroacoustics, and Signal Processing ICASSP 2004 Tutorial Brian M. Sadler Richard J. Kozick 17 May 2004. Sensor Network Publication Trend. NSF Boost Phase. Source: IEEE Xplore, “sensor networks” (IEEE only).

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

ICASSP Tutorial


Sensor network publication trend
Sensor Network Publication Trend

NSF

Boost Phase

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

ICASSP Tutorial


Sensor Networks, Aeroacoustics,and Signal ProcessingIntl. Conf. on Acoustics,Sensor-Nets, and Signal Proc.Brian M. SadlerRichard J. Kozick17 May 2004

ICASSP Tutorial


CaveatsSP & SP-Comms Perspective, Finite Citations, RMF*AcknowledgementsS. 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

ICASSP Tutorial


Outline
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.

ICASSP Tutorial


Sensor Networks, Aeroacoustics,and Signal ProcessingICASSP 2004 TutorialPart I: Overview of Sensor NetworksBrian M. SadlerRichard J. Kozick17 May 2004

ICASSP Tutorial


Modalities and applications
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!

ICASSP Tutorial


Rich multi disciplinary interplay

Ad hoc networking

Sensing / physics / propagation

Low power / adaptive hardware

Controls, robotics, avionics

Rich Multi-Disciplinary Interplay

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
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

DIE

HARD

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 @ 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?

ICASSP Tutorial


Energy themes
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

ICASSP Tutorial


Outline1
Outline

  • Intro & Energy Themes

  • Architectures & Connectivity

  • Some Fundamental Limits

  • Clocks & Synchronization

  • Hardware Trends

  • Node Localization

  • Medium Access Control & Routing

  • Conclusions

ICASSP Tutorial


Architectures
Architectures

  • flat

  • cluster, hierarchical

  • mobile collectors

  • mobile nodes / robotics / UAVs

  • k-hop to fixed infrastructure (k=1)

    • the likely dominant commercial paradigm

ICASSP Tutorial


Connectivity
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

ICASSP Tutorial


Connectivity1
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

ICASSP Tutorial


Ad hoc network capacity
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
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]

ICASSP Tutorial


Mobility brings diversity
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]

ICASSP Tutorial


Time synchronization
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

ICASSP Tutorial


Oscillator accuracy
Oscillator Accuracy

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

ICASSP Tutorial



Hardware trends
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

ICASSP Tutorial


An energy model
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

ICASSP Tutorial


Power amplifier efficiency
Power Amplifier & Efficiency

  • Power control vs PA efficiency

    • variable voltage supply to maximize PA use

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

ICASSP Tutorial


Localization calibration
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

ICASSP Tutorial


Medium access control mac
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 mac1
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

ICASSP Tutorial


Medium access control mac2
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.

ICASSP Tutorial


Sampling mac 1
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

ICASSP Tutorial

[Dong, Tong, Sadler, 02, 04]


Sampling mac 2
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)

ICASSP Tutorial


1 d signal field reconstruction

Sampling & MAC - 3

(1-D) Signal Field Reconstruction

  • 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)

ICASSP Tutorial


Sampling mac 4
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)

Schedule one packet per resolution interval of length

ICASSP Tutorial


Sampling mac 5
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

ICASSP Tutorial


Sampling mac 6
Sampling & MAC - 6

Deterministic scheduling

random access

  • If expect # of sensors in interval > , then

  • scheduled collection is preferred

  • Or, given sensor density , choice of dictates

  • appropriate collection regime

ICASSP Tutorial


Routing
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

ICASSP Tutorial


Odds and ends
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
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

ICASSP Tutorial


Conclusions cross layer design
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

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

ICASSP Tutorial


Sensor Networks, Aeroacoustics,and Signal ProcessingICASSP 2004 TutorialEnd of Part I: Overview of Sensor NetworksBrian M. SadlerRichard J. Kozick17 May 2004

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