Data Fusion Improves the Coverage of Sensor Networks

Guoliang Xing

Assistant Professor Department of Computer Science and Engineering

Michigan State University http://www.cse.msu.edu/~glxing/

Outline

• Background• Problem definition

– Coverage of large-scale sensor networks• Scaling laws of coverage • Other projects

– Model-driven concurrent medium access control– Integrated coverage and connectivity

configuration

2

Mission-critical Sensing Applications

• Large-scale network deployments– OSU ExScal project: 1450 nodes deployed in a 1260X288 m2 region

• Resource-constrained sensor nodes– Limited sensing performance

• Stringent performance requirements– High sensing probability, e.g., 90%, low false alarm rate, e.g., 5%,

bounded delay, e.g., 20s3

• Fundamental requirement of critical apps– How well is a region monitored by sensors?

• Coverage of static targets– How likely is a target detected?

Sensing Coverage

4

Network Density for Achieving Coverage

• How many sensors are needed to achieve full or instant coverage of a geographic region?– Any static target can be detected at a high prob.

• Significance of reducing network density– Reduce deployment cost– Prolong lifetime by putting redundant sensors to sleep

5

State of the Art• K-coverage

– Any physical point in a large region must be detected by at least K sensors

• Coverage of mobile targets– Any target must be detected within certain delay

• Barrier coverage– All crossing paths through a belt region must be k-covered

• Most previous results are based on simplistic models– All 5 papers on the coverage problem published at

MobiCom since 2004 assumed the disc model

6

Single-Coverage under Disc Model• Deterministic deployment

– Optimal pattern is hexagon• Random deployment

– Sensors deployed by a Poisson point process of density ρ– The coverage (fraction of points covered by at least one sensor):

deterministic deployment random deployment 7

[Liu 2004]

8

Sensing Model

✘The (in)famous disc model✘Sensor can detect any target within range r

✔Real-world sensor detection• There is no cookie-cutter “sensing range”!

r

Acoustic Vehicle Tracking Data in DARPA SensIT Experiments [Duarte 04]

Contributions

• Introduce probabilistic and collaborative sensing models in the analysis of coverage– Data fusion: sensors combine data for better inferences

• Derive scaling laws of network density vs. coverage– Coverage of both static and moving targets

• Compare the performance of disc and fusion models– Data fusion can significantly improve coverage!

9

Outline

• Background• Problem definition

– Coverage of large-scale sensor networks• Scaling laws of coverage • Other projects

– Model-driven concurrent medium access control– Integrated coverage and connectivity

configuration• Personal perspectives on research

10

11

Sensor Measurement Model• Sensor reading yi = si + ni• Decayed target energy si = S · w(xi)

• Noise energy follows normal distribution ni ~ N(μ,σ2)

Acoustic Vehicle Tracking Data in DARPA SensIT Experiments [Duarte 04]

, 2≤ k ≤ 5

Single-sensor Detection Model

• Sensor reading yi

• H0 – target is absent

• H1 – target is present

sensor reading distribution

detection threshold

noise energy distribution

false alarm rate

detection probability

t

energy

Q(·) – complementary CDF of the std normal distribution

false alarm rate:

detection probability: pro

bab

ilit

y

12

χn – CDF of Chi-square distributionw(xi) – Energy reading of sensor xi from target

Data Fusion Model• Sensors within distance R from target fuse their readings

– R is the fusion range• The sum of readings is compared again a threshold η• False alarm rate

PF = 1-χn(n· η)• Detection probability PD = 1 –χn(n·η - Σw(xi)) R

13

Outline

• Background• Problem definition

– Coverage of large-scale sensor networks• Scaling laws of coverage

– Coverage of static targets• Other projects

– Model-driven concurrent medium access control– Integrated coverage and connectivity configuration

• Personal perspectives on research

14

(α,β)-coverage

• A physical point p is (α,β)-covered if – The system false alarm rate PF ≤ α – For target at p, the detection prob. PD ≥ β

• (α,β)-coverage is the fraction of points in a region that is (α,β)-covered– Full (0.01, 0.95)-coverage: system false alarm rate

is no greater than 1%, and the prob. of detecting any target in the region is no lower than 95%

15

Extending the Disc Model• Classical disc model is deterministic• Extends disc model to stochastic detection

– Choose sensing range r such that if any point is covered by at least one sensor, the region is (α,β)-covered

• Previous results based on disc model can be extended to (α,β)-coverage

δ-- signal to noise ratio S/σ

16

Disc and Fusion Coverage• Coverage under the disc model

– Sensors independently detect targets within sensing range r• Coverage under the fusion model

– Sensors collaborate to detect targets within fusion range R

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(α,β)-coverage under Fusion Model

• The (α,β)-coverage of a random network is given by

F(p) – set of sensors within fusion range of point pN(p) – # of sensors in F(P)

optimal fusion range

18

Network Density for Full Coverage

• ρf and ρd are densities of random networks under fusion and disc models

• Sensing range is a constant• Opt fusion range grows with network density ρf <ρd when high coverage is required

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20

Network Density w Opt Fusion Range

• When fusion range is optimized with respect to network density

• When k=2 (acoustic signals)

• Data fusion significantly reduces network density

, 2≤ k ≤ 5

Network Density vs. SNR

• For any fixed fusion range

• The advantage of fusion decreases with SNR

21

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Trace-driven Simulations

• Data traces collected from 75 acoustic nodes in vehicle detection experiments from DARPA SensIT project– α=0.5, β=0.95, deployment region: 1000m x 1000m

Simulation on Synthetic Data

• k=2, target position is localized as the geometric center of fusing nodes

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Conclusions

• Bridge the gap between data fusion theories and performance analysis of sensor networks

• Derive scaling laws of coverage vs. network density– Data fusion can significantly improve coverage!

• Help to understand the limitation of current analytical results based on ideal sensing models

• Provide guidelines for the design of data fusion algorithms for large-scale sensor networks

24

Outline

• Background• Problem definition

– Coverage of large-scale sensor networks• Scaling laws of coverage

– Coverage of static targets• Other projects

– Model-driven concurrent medium access control– Integrated coverage and connectivity configuration

• Personal perspectives on research

25

Improve Throughput by Concurrency

• Enable concurrency by controlling senders' power

s1

r1

s2

r2 +

Received Signal Strength

Transmission Power LevelTransmission Power Level

Re

ce

ive

d S

ign

al S

tren

gth

(dB

m)

Re

ce

ive

d S

ign

al S

tren

gth

(dB

m)

• 18 Tmotes with Chipcon 2420 radio• Near-linear RSSdBm vs. transmission power level• Non-linear RSSdBm vs. log(dist), different from the classical model! 27

Packet Reception Ratio vs. SINR

• Classical model doesn't capture the gray region

office, no interfererparking lot, no interferer office, 1 interferer

0~3 dB is

"gray region"

Packet R

eception Ratio (%

)

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C-MAC Components

ConcurrentTransmission

Engine

Power Control Model

InterferenceModel

Handshaking

On

line M

od

el Estim

ation

Currency Check

Throughput Prediction

Throughput Prediction

Presented at IEEE Infocom 2009

•Implemented in TinyOS 1.x, evaluated on a 18-mote test-bed

•Performance gain over TinyOS default MAC is >2X

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Performance Evaluation• Implemented in TinyOS 1.x• 16 Tmotes deployed in a 25x24 ft office• 8 senders and 8 receivers

Experimental Results

system th

rou

gh

pu

t (Kb

ps)

system th

rou

gh

pu

t (Kb

ps)

Time (second) Number of Senders

Improve throughput linearly w num of senders

Deterministic Coverage + Connectivity

• Select a set of nodes to achieve– K-coverage: every point is monitored by at least K sensors– N-connectivity: network is still connected if N-1 nodes fail

Sleeping node

Communicating nodes

Active nodes

Sensing range

A network with 1-coverage and 1-connectivity 32

Connectivity vs. Coverage: Analytical Results

• Network connectivity does not guarantee coverage– Connectivity only concerns with node locations– Coverage concerns with all locations in a region

• If Rc/Rs 2– K-coverage K-connectivity– Implication: given requirements of K-coverage and N-

connectivity, only needs to satisfy max(K, N)-coverage– Solution: Coverage Configuration Protocol (CCP)

• If Rc/Rs < 2– CCP + connectivity mountainous protocols

ACM Conference on Embedded Networked Sensor Systems (SenSys), 2003 ACM Transactions on Sensor Networks, Vol. 1 (1), 2005 (~600 citations on Google Scholar) 33

Research Summary• Data fusion in sensor networks

– Coverage [MobiCom 09]; deployment[RTSS 08]; mobility [ICDCS 08]• MAC protocol design and architecture

– C-MAC: concurrent model-driven MAC [Infocom08]– UPMA: unified power management architecture [IPSN 07]

• Sensornet/real-time middleware– MobiQuery: spatiotemporal query service for mobile users [ICDCS 05, IPSN 05]– nORB: light-weight real-time middleware for networked embedded systems [RTAS 04]

• Controlled mobility– Mobility-assisted spatiotemporal detection [ICDCS 08,IWQoS 08]– Rendezvous-based data transport [MobiHoc 08, RTSS 07]

• Power management– Minimum power configuration [MSWiM 07, MobiHoc 05, TOSN 3(2)]– Integrated coverage and connectivity configuration [TOSN 1(1), SenSys 03]– Impact of sensing coverage on geographic routing [TPDS 17(4), MobiHoc 04]– Real-time power-aware routing in sensor networks [IWQoS 06]– Data fusion for target detection [IPSN 04]

Acknowledgement

• Students– Rui Tan, Mo Sha

• Collaborators– Benyuan Liu, Jianping Wang…..

Michigan State University• First land-grant institution

– Founded in 1855, prototype for 69 land-grant institutions established under the Morrill Act of 1862

• One of America's Public Ivy universities • Big ten conferences

– University of Illinois, Indiana University, University of Iowa, University of Michigan, University of Minnesota, Northwestern University, Ohio State University, Pennsylvania State University, Purdue University, University of Wisconsin

• Single largest campus, 8th largest university in the US with 46,648 students and 2,954 faculty members

• Rankings of 2008– 80th worldwide, Shanghai Jiao Tong University’s Institute of Higher

Education – 71th in US, U.S. News & World Report

Computer Science & Engineering @MSU

• People– 27 tenure-stream faculty– Each year awards approximately 100 BS, 40 MS, and 10

PhD degrees in Computer Science• Research

– 9 research laboratories, with annual research expenditures exceeding $3.5 million

• Rankings– 15th graduate program in US, a recent article of Comm. of

ACM– Top 100, Shanghai Jiao Tong University’s Institute of

Higher Education

My Group

• Research– Sensor networks

• Data fusion, power management, voice streaming, controlled mobility

– Low-power wireless networks• MAC, Interference management

– Cyber-physical systems• Students

– Supervise 6 PhDs (CityU and MSU), 2 MS– Co-supervise 4 PhDs (CAS, CWM, UTK, MSU)

Ranking of Journals*• Tier 1

– IEEE/ACM Trans. on Networking (TON)• Tier 1.5

– ACM Trans. on Sensor Networks (TOSN)– IEEE Trans. on Mobile Computing (TMC)– IEEE Trans. on Computers (TC)– IEEE Trans. on Parallel and Distributed Systems

(TPDS)

*The ranking is only applicable to Sensor Network research

Ranking of Conferences*• Tier 0: SIGCOMM, MobiCom (~10%)[1]• Tier 1: MobiHoc (10~15%)[3], SenSys[1], MobiSys

(15~18%), • Tier 1.5: Infocom[1], ICDCS (~18%) [3], RTSS[4]

(20~25%), ICNP, PerCom (10-15%)• Tier 2:IPSN[3], IWQoS, [1] MSWiM (20~25%) [1],

MASS[2], DCOSS (~25%)• Tier 4: Globecom, WCNC, ICC…..(>30%)

*Partially borrowed from Prof. Dong Xuan’s talk*The ranking is only applicable to Sensor Network research, and may

significantly change for other fields