1

Self-Configuring Beacon Systems for Localizing Networked Sensors

Nirupama Bulusu

Laboratory for Embedded Collaborative SystemsDepartment of Computer ScienceUniversity of California at Los Angeles

2

Wireless Sensor Networks

Sensing

Computation

Networking

New technologies have reduced the cost, size and power of micro-sensors and wireless interfaces

Systems can Embedded into environment Sense phenomena at close range

Systems will revolutionize Environmental monitoring Disaster scenarios Structure Response

Environmental Monitoring

Circulatory Net

3

New Challenges

Building Blocks to enable efficient coordination among Building Blocks to enable efficient coordination among sensor nodes; bridge technology-application gapsensor nodes; bridge technology-application gap

Nodes Small form factor Battery operated

System Large #s Ad hoc deployment Unattended

Energy constraints imposed by unattended systems

Scaling challenges due to very large numbers of sensors

Level of dynamics: Environmental – obstacles, weather,

terrain System – large number of nodes, failures

4

What is Localization?

A mechanism for discovering spatial relationships between objects

5

Why is Localization Important? Large scale embedded systems coupled to the

physical world Localization measures that coupling, giving raw

sensor readings a physical context Temperature readings temperature map Asset tagging asset tracking Smart spaces context dependent behavior Sensor time series coherent beam-forming

Enables data-centric network design

Goal: Scalable, ad hoc deployable, energy-efficientGoal: Scalable, ad hoc deployable, energy-efficientlocalization for small sensor deviceslocalization for small sensor devices

6

Problem Statement

Consider a collection of sensors Si, with coordinate Xi . Given a subset of Si, are “reference points (beacons)”,

with defined values for Xi , Given a set of measurements that relate the positions of

Si,

Estimate Xi. Design of position estimation algorithm depends on nature

of constraints; Nature of constraints depends on types of ranging. Ranging sensitive to environment.

7

Goal: Robust, Unattended operation Approach: Self-configuration Thesis

Many aspects of localization are highly environment dependent and may require configuration.

In order to be ad hoc deployed and operate unattended in any environment, the localization system must self-configure.

Many dimensions to Self-configuration System – Adapting to node density, failures etc. Multiple sensor modalities for robust measurements Environment - Adapting to fixed characteristics

Dynamically deriving wireless channel parameters

8

Methodology

Simulation and Analysis

Design solutions

Evaluate solutions in simulation

Collect data with real networks

Identify and analyze problems

Implement best solutions on real networks

Evaluate performance

9

Talk Structure

Motivation Localization Background Networked Sensors: Localization Challenges Self-Configuring Beacon Systems Conclusions

10

Variety of Applications Two applications:

Passive habitat monitoring:Where is the bird?

What kind of bird is it?

Asset tracking:Where is the projector?

Why is it leaving the room?

11

Variety of Application Requirements

Outdoor operation Weather problems

Bird is not tagged Birdcall is characteristic

but not exactly known Accurate enough to

photograph bird Infrastructure:

Several acoustic sensors, with known relative locations; coordination with imaging systems

Indoor operation Multipath problems

Projector is tagged Signals from projector tag

can be engineered Accurate enough to track

through building Infrastructure:

Room-granularity tag identification and localization; coordination with security infrastructure

Very different requirements!

12

Axes of Application Requirements

Cost, Power, Form factor Scaling (Number of devices) Communications Requirements Environmental conditions Is the target known? Is it cooperating? Distance scales Accuracy scales Relation to established coordinate system

Wireless

Sensor

Networks

13

Variety of MechanismsACTIVEe.g. radar and reflective sonar systems

System emits signal, deduces target location from distortions in signal returns

CO-OPERATIVEORL Active Bat, GALORE Panel, AHLoS, GPS, MIT Cricket, UNC HighBall

Target cooperates with the system

BLINDAcoustic “blind beamforming” (Yao)

System deduces location of target without a priori knowledge of its characteristics

TargetSynchronization channelRanging channel

?

PASSIVEMicrosoft RADAR

System deduces location from observation of signals that are “already present”

Definitely no “one size fits all” solution

14

What’s Wrong with What’s There? Approaches that scale (e.g. GPS) cannot always

accommodate device constraints, be ubiquitously available, responsive or accurate enough.

Approaches that accommodate device constraints (eg. Microsoft RADAR) require extensive pre-configuration and may not be suitable for ad hoc, unattended deployment.

No existing localization system can No existing localization system can self-configure to its environmental conditions.self-configure to its environmental conditions.

15

Talk Structure

Motivation Localization Background Networked Sensors: Localization Challenges Self-Configuring Beacon Systems Conclusions

16

Networked Sensors: Localization Challenges #1: Scale #2: Device Constraints #3: Deployment and Dynamics

17

#1: Scale

Problem Need to localize large numbers of devices Communications and computation cost of

centralized localization approach based on global system state prohibitively expensive

Our Solution Localized location computation

18

#2: Device Constraints

Problem Small devices have limited hardware and energy Low-energy localization approaches leverage inherent

communications capabilities (eg. RF amplitude) But RF amplitude not fine-grained enough to converge to

consistent global coordinate system….. Our Solution

Tiered architectures Exploit heterogeneity; use beacons

19

What are Beacons? Reference Nodes that

know their position How? Less-constrained

devices based on the principle of tiered architectures; can form accurate coordinate system independently GPS-enabled (outdoors) Special ranging

hardware; multiple sensor modalities etc. (recent work at UCLA)

More memory to run sophisticated position

estimation algorithms

Tiered Architecture

(trade form factor vs. functionality)

Sensor Mote

UCB, 2000

RFM radio, PIC

WINS NG 2.0Sensoria, 2001Node developmentplatform; multi-sensor, dual radio,Linux on SH4,Preprocessor, GPS

Lower TierLower Tier

Upper TierUpper Tier

20

Example: An RF-based Localization System Si - Set of Beacons Beacons broadcast advertisements

Randomly with periodic offsets with (X, Y, Z) coordinates Beacon Identifier Sequence number of the advertisement

Each client node computes its position based on the beacons it is connected to.

21

Single Beacon Idealized RF-propagation model

Connectivity implies client within some maximum communication radius R

R

BeaconClient Node

Possible position for client node

22

Multiple Beacons

More connections result in smaller regions of overlap

Smaller area feasible position is close to real position

23

Position Estimation Weighted centroid approach

Reference: The centroid of points with approximate weights [ M. Bern, D. Eppstein, L. Guibas, J. Hershberger, S. Suri, J. Wolter et.al.]

Set of i beacons, position Xi , Range Ri

Xe – estimated position Wi = 1/(Ri )2

Xe

n

i1

wW

n

1Wi Xi

W

24

Inferring RF Connectivity and Range

CM(i,t1,t2) = Nsent(i, t1,t2)Nrecv(i, t1, t2)

Connectivity if CM > CMthresh

Nrecv(i, t1, t2)Packets received from Bi in time [t1, t2] Nsent(i, t1, t2) – # Packets sent by Bi in time [t1, t2] Connectivity Metric for Beacon Bi

Range Ri of Beacon Bi

median range over all gradients for which CM > CMthresh

25

Characterizing Localization Quality X - real position Xe - estimated position Localization Error Metric

LE(X) = ||X – Xe ||

Localization Quality Cumulative Error Distribution Function

26

Sources of Localization Error Beacon Placement Environment

Signal Propagation vagaries Miscalibration

27

Impact of Beacon Placement

Beacons uniformly placed:

SMALLER mean granularity

Beacons randomly placed:

LARGER mean granularity

28

Radio Propagation Basics Why do RF propagation

vagaries occur? Path loss characteristics

depend on environment (1/rn) Shadowing depends on

environment Short-scale fading due to

multipath adds random high frequency component with huge amplitude (30-60dB) – very bad indoors Mobile nodes might average out

fading.. But static nodes can be stuck in a deep fade forever

DistanceR

ecei

ved

Sig

nal

Stre

ngth

(RS

SI)

Path lossShadowingFading

Ref. Rappaport, T, Wireless Communications Principle and Practice, Prentice Hall, 1996.

29

Impact of Propagation Vagaries

Gap in Beacon Coverage Proximity inferred to Distant Beacon

30

Summary: RF-based Localization Problem

Localization of many small devices Solution

Self-Localization from RF-proximity beacons General Lessons

Localized algorithms Tiered architectures that leverage heterogeneity

Status Implementations: Radiometrix RPC radios, UCB motes Experiments both indoors and outdoors Used for proximity-based tracking, geo-routing, localization for

energy harvesting etc. Papers: IEEE Personal Communications

31

#3: Deployment and Dynamics Problem

Localization quality governed by beacon placement and environmental conditions…..

…..But careful manual pre-configuration of beacon systems

impedes ad hoc deployment manual re-configuration to dynamics impedes

unattended operation Our solution

Self-configuring beacon systems

32

Talk Structure

Motivation Localization Background Networked Sensors: Localization Challenges Self-Configuring Beacon Systems Conclusions

33

Self-configuring Beacon Systems Idea:

Measure and adapt to unpredictable environment Exploit spatial diversity and density of

sensor/actuator nodes Assuming large solution space, not seeking

global optimal Questions:

What to measure? How to adapt?

34

Characterizing Beacon Density N - Number of Beacons A - Area R - Transmission Range of each beacon Beacon Deployment Density, = N/A Beacons per nominal radio coverage area, R2

R

35

Impact of Beacon Density

saturation density

~6 bpnrca

Beacons per nominal radio coverage area

Density should influence approach to self-configurationDensity should influence approach to self-configuration

00.10.20.30.40.50.60.70.80.9

1

0 5 10 15 20Mea

n Lo

caliz

atio

n Er

ror

(frac

tion

of R

)

36

Impact of Beacon Density

saturation density

~6 bpnrca

Beacons per nominal radio coverage area

Low Density: HEAP

High Density: STROBE

Mea

n Lo

caliz

atio

n Er

ror

(frac

tion

of R

)

00.10.20.30.40.50.60.70.80.9

1

0 5 10 15 20

37

Talk Structure

Motivation Localization Background Networked Sensors: Localization Challenges Self-Configuring Beacon Systems

Beacon Density Low densities: HEAP High densities: STROBE

Conclusions

38

HEAP Introduction

Problem Beacons deployed may not ensure localization

quality due to environment vagaries Traditional Approaches

Eg. Facility location, gap-finding Offline, centralized optimization based on beacon

positions only; ignore environmental effects Our Solution: HEAP

Adaptive, incremental beacon placement

39

HEAP - Incremental Beacon Placement Goal

Add new beacons to an already deployed beacon field where most needed

Design Goals Measurement-based adaptation to environmental

conditions Localized algorithms to minimize communications

Caveats Completely self-configuring only if new beacons

can be added without manual intervention

40

HEAP Illustration

BeaconNode

Placer

candidate point, utility

41

Local Candidate Point Selection Given

S – set of all beacons reachable in grid

E - An error estimation model

Determine C - (x , y) Such that cumulative

localization error in the grid is minimized by adding

beacon at C Analytically intractable Estimate by sampling the

grid. candidate point

2R

42

HEAP Evaluations

Goals Impact of density How does it compare to a centralized scheme, or

a purely random one? Metrics

Improvement in mean localization error Methodology

Simulations for repeatable experiments Experiments to validate with real data

43

Performance: Mean Error Improvement

Beacons Per Nominal Radio Coverage Area Localized algorithms gains comparable to Localized algorithms gains comparable to

centralized algorithmscentralized algorithms

Mea

n Er

ror

Impr

ovem

ent

(fra

ctio

n of

R )

44

Experimental Validation

Limited Computation 4 MHz, 8-bit CPU

Limited memory 512 bytes

Limited code size 8 KB 3.5 K Base code (TinyOS

+ radio encoder) Only 4.5K for apps

Limited communication 30 byte packets

Platform:

Berkeley RENE Motes

45

Indoor Beacon Deployment35 ft

42 ft

24 ft

46

Software Infrastructure Beacon Client Placer

Transceiver Send control packets to beacons Receive reports from client

BeaconRemoteController User control of beacons

BeaconInterpretor User input of actual client coordinates Localization error report

Visualization – AirPacketAnalyzer Displays all transmitting devices in the lab Useful for checking RF interference Uses lab snoopers

OperationalOperationalTestbedTestbed

ExperimentalExperimentalTestbedTestbed

47

Control Message- Activate/Stop- Transmit Power Setting- Beaconing Interval

48

Beacon Connectivity

Failed beacon

49

Beacon Connectivity

Failed beacon

Missing

LinkLong

Asymmetric links

50

Candidate Point Selected by HEAP

Missing

LinkLong

Asymmetric links

Failed beacon

Candidate point

51

Performance: Localization Error

X (ft)Y (ft)

Localization

Error (ft)

52

Cumulative Frequency Distribution

Cum

ulat

ive

Freq

uenc

y

Dis

trib

utio

n (%

)

Localization Error (ft)

5 feet decrease in 90%ile localization error

53

Summary: HEAP Problem

Set of beacons deployed may not ensure localization quality Solution

Adaptive beacon placement at empirically determined candidate points

General Lessons Localized algorithms effective Empirical adaptation necessary Measurements always difficult but necessary

Status Experiments over indoor and outdoor mote test-bed Papers: Journal submission, IEEE ICDCS 2001

54

Talk Structure

Motivation Localization Background Networked Sensors: Localization Challenges Self-Configuring Beacon Systems

Beacon Density Low Densities: HEAP High Densities: STROBE

Conclusions

55

STROBE Introduction Problem

threshhreshold Beacon Density

Densely deployed beacons (>> thresh ) can provide robustness through redundancy

But will self-interfere and also waste energy if operational simultaneously

Conventional Approaches Rotate functionality amongst beacons using pre-assigned (static)

schedules Our Solution: STROBE

Adapt operational density based on measured environmental conditions, redundant neighbors, application dynamics

56

STROBE – Selectively TuRning Off BEacons Goals

Conserve energy to extend system lifetime without diminishing localization granularity

Design Goals Localized algorithms Responsive but low adaptation overhead

STROBE – Adaptive Density

57

SLEEP state

VOTING state

DESIGNATED state

STROBE Illustration

58

STROBE Duty Cycle

Voting

SleepDesignatedShould Beacon

Should

Slee

p

Time e

lapse

d >

Sleep T

ime

Time elapsed >

Beacon Time

59

STROBE Decision Making

We have n=actual beacons in some area, only k=thresh need to perform a given task, the rest can go to sleep

Randomized decentralized approach: They each decide to

participate independently with probability p.

Let X be the random variable that indicates how many beacons actually participate.

Probability that task is accomplished = Probability that X >= k.

Binomial Distribution:

This equation gives a phase

transition at p=k/n

60

STROBE Evaluations

Goals Can it maintain localization granularity? How much does it improve lifetime?

Metrics Median localization error vs. time Improvements in overall system lifetime

Methodology Experimental Trace-driven simulations

61

Performance - System Lifetime

Time (in 105 seconds) Med

ian

Loc

aliz

atio

n Er

ror

(frac

tion

of R

)

1.5x lifetime increase without localization 1.5x lifetime increase without localization degradationdegradation

0 2.5 5

0.2

0

0.8

0.6

0.4

2x threshold density => Max. Lifetime Increase (Theoretically) 2x

62

Summary: STROBE Problem

Dense deployment not enough as redundant beacons can self-interfere, waste energy

Solution Randomized, decentralized algorithm (STROBE) to rotate

functionality amongst redundant beacons General Lessons

Exploit redundancy to extend system lifetime Characterize threshold density; utilize density to tune sleep

probabilities Utilize energy analysis to tune adaptation frequency

Status Papers: ISCTA 2001, journal submission, GHC 2002

63

Talk Structure

Motivation Localization Background Networked Sensors: Localization Challenges Self-Configuring Beacon Systems Conclusions

64

Summary Localizing networked sensors: New Challenges

Scale, Device Constraints: Self-Localization from RF-Beacons Deployment and Dynamics: Self-configuring Beacon Systems

Lessons for Systems Design Tiered architectures that leverage heterogeneity Self-configuration for unattended systems Measurement-based adaptation of placement

Location and Time Synchronization Beacons Sensing and communications coverage

Density-adaptive schemes Energy-conserving ad hoc routing

Lessons for Evaluation Measurements always difficult, but always necessary Fully controllable system parameters Experimental vs. operational test-bed

65

Future Directions

Adaptive, self-configuring networks Large scale, ad hoc sensor networks Internet measurement and analysis

Self-organizing peer-to-peer overlay networks Pervasive Location-aware computing

Location Modeling Federated Spatial Coordinate Systems

New sensor network applications

66

More Information

http://www.cs.ucla.edu/~bulusu Laboratory for Embedded Collaborative Systems

http://lecs.cs.ucla.edu

67

BACKUP SLIDES

68

Technology Trends

Sensor node energy requirements

Energy supply

Communications and signal processing energy

Heterogeneous Sensors

Sensor node development

69

1. Sensor Node Energy Roadmap

20002000 20022002 20042004

10,0010,0000

1,0001,000

100100

1010

11

.1.1

Ave

rage

Pow

er

(mW

)

• Deployed (5W)• PAC/C Baseline (.5W)

• (50 mW)

(1mW)

Re-hosting to Re-hosting to Low Power Low Power COTSCOTS (10x)(10x)

-System-On-Chip-System-On-Chip-Adv Power -Adv Power ManagementManagementAlgorithms (50x)Algorithms (50x)

Source: ISI & DARPA PAC/C Program

70

2. Comparison of Energy Sources

Power (Energy) Density Source of EstimatesBatteries (Zinc-Air) 1050 -1560 mWh/cm3 (1.4 V) Published data from manufacturers

Batteries(Lithium ion) 300 mWh/cm3 (3 - 4 V) Published data from manufacturers

Solar (Outdoors)15 mW/cm2 - direct sun

0.15mW/cm2 - cloudy day. Published data and testing.

Solar (Indoor).006 mW/cm2 - my desk

0.57 mW/cm2 - 12 in. under a 60W bulb Testing

Vibrations 0.001 - 0.1 mW/cm3 Simulations and Testing

Acoustic Noise3E-6 mW/cm2 at 75 Db sound level

9.6E-4 mW/cm2 at 100 Db sound level Direct Calculations from Acoustic TheoryPassive Human

Powered 1.8 mW (Shoe inserts >> 1 cm2) Published Study.

Thermal Conversion 0.0018 mW - 10 deg. C gradient Published Study.

Nuclear Reaction80 mW/cm3

1E6 mWh/cm3 Published Data.

Fuel Cells300 - 500 mW/cm3

~4000 mWh/cm3 Published Data.

With aggressive energy management, ENS With aggressive energy management, ENS mightmightlive off the environment.live off the environment.

Source: UC Berkeley

71

3. Communication/Computation Technology Projection

Assume: 10kbit/sec. Radio, 10 m range.Assume: 10kbit/sec. Radio, 10 m range.

Large cost of communications relative to computation Large cost of communications relative to computation continuescontinues

1999 (Bluetooth

Technology)2004

(150nJ/bit) (5nJ/bit)1.5mW* 50uW

~ 190 MOPS(5pJ/OP)

Computation

Communication

Source: ISI & DARPA PAC/C Program

72

4. Sensors• Passive elements: seismic, acoustic, infrared, strain,

salinity, humidity, temperature, etc.• Passive Arrays: imagers (visible, IR), biochemical• Active sensors: radar, sonar

– High energy, in contrast to passive elements• Technology trend: use of IC technology for increased

robustness, lower cost, smaller size– COTS adequate in many of these domains; work

remains to be done in biochemical

73

Scaling and Robustness: Lessons from Internet Protocol

Design Soft state protocol design Localized algorithms Adaptability

74

Some Networked Sensor NodeDevelopments

LWIM III

UCLA, 1996

Geophone, RFM

radio, PIC, star

network

AWAIRS I

UCLA/RSC 1998

Geophone, DS/SS

Radio, strongARM,

Multi-hop networks

Processor

Sensor Mote

UCB, 2000

RFM radio,

PIC

WINS NG 2.0Sensoria, 2001Node developmentplatform; multi-sensor, dual radio,Linux on SH4,Preprocessor, GPS