Data Fusion Improves the Coverage of Sensor Networks

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Data Fusion Improves the Coverage of Sensor
Networks

• Guoliang Xing
• Assistant Professor
• Department of Computer Science and
  EngineeringMichigan State University
• http//www.cse.msu.edu/glxing/

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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

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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., 20s

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Sensing Coverage

• Fundamental requirement of critical apps
• How well is a region monitored by sensors?
• Coverage of static targets
• How likely is a target detected?

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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

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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

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Single-Coverage under Disc Model

• Deterministic deployment
• Optimal pattern is hexagon
• Random deployment
• Sensors deployed by a Poisson point process of
  density ?
• The coverage (fraction of points covered by at
  least one sensor)
• 
  
  

Liu 2004
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deterministic deployment
random deployment
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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
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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!

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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

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Sensor Measurement Model

• Sensor reading yi si ni
• Decayed target energy si S w(xi)
• Noise energy follows normal distribution ni
  N(µ,s2)

, 2 k 5
Acoustic Vehicle Tracking Data in DARPA SensIT
Experiments Duarte 04
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Single-sensor Detection Model

• Sensor reading yi
• H0 target is absent
• H1 target is present

sensor reading distribution
noise energy distribution
false alarm rate
probability
detection probability
energy
t
Q() complementary CDF of the std normal
distribution
detection threshold
false alarm rate
detection probability
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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
  ?
• False alarm rate
• PF 1-?n(n ?)
• Detection probability
• PD 1 ?n(n? - Sw(xi))

R
?n CDF of Chi-square distribution w(xi)
Energy reading of sensor xi from target
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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

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(a,ß)-coverage

• A physical point p is (a,ß)-covered if
• The system false alarm rate PF a
• For target at p, the detection prob. PD ß
• (a,ß)-coverage is the fraction of points in a
  region that is (a,ß)-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

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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
  (a,ß)-covered
• Previous results based on disc model can be
  extended to (a,ß)-coverage

d-- signal to noise ratio S/s
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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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(a,ß)-coverage under Fusion Model

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

F(p) set of sensors within fusion range of
point p N(p) of sensors in F(P)
optimal fusion range
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Network Density for Full Coverage

• ?f and ?d are densities of random networks under
  fusion and disc models
• Sensing range is a constant
• Opt fusion range grows with network density
• ? ?f lt?d when high coverage is required

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Network Density w Opt Fusion Range

• When fusion range is optimized with respect to
  network density
• When k2 (acoustic signals)
• Data fusion significantly reduces network density

, 2 k 5
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Network Density vs. SNR

• For any fixed fusion range
• The advantage of fusion decreases with SNR

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Trace-driven Simulations

• Data traces collected from 75 acoustic nodes in
  vehicle detection experiments from DARPA SensIT
  project
• a0.5, ß0.95, deployment region 1000m x 1000m

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Simulation on Synthetic Data

• k2, 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

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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

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Improve Throughput by Concurrency
s1
s2
r1
r2

• Enable concurrency by controlling senders' power

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Received Signal Strength
Received Signal Strength (dBm)
Received Signal Strength (dBm)
Transmission Power Level
Transmission Power Level

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

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Packet Reception Ratio vs. SINR
03 dB is "gray region"
Packet Reception Ratio ()
office, no interferer
parking lot, no interferer
office, 1 interferer

• Classical model doesn't capture the gray region

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C-MAC Components
Power Control Model
Currency Check

Concurrent Transmission Engine
Handshaking
Online Model Estimation
Interference Model
Throughput Prediction
Throughput Prediction

• Implemented in TinyOS 1.x, evaluated on a 18-mote
  test-bed
• Performance gain over TinyOS default MAC is gt2X

Presented at IEEE Infocom 2009
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Performance Evaluation

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

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Experimental Results
Improve throughput linearly w num of senders
system throughput (Kbps)
system throughput (Kbps)
Number of Senders
Time (second)
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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

Active nodes
Sensing range
Sleeping node
Communicating nodes
A network with 1-coverage and 1-connectivity
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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 lt 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)
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Research Summary

• Data fusion in sensor networks
• Coverage MobiCom 09 deploymentRTSS 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

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Acknowledgement

• Students
• Rui Tan, Mo Sha
• Collaborators
• Benyuan Liu, Jianping Wang..

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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 Universitys
  Institute of Higher Education
• 71th  in US, U.S. News World Report

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Computer Science Engineering _at_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 Universitys
  Institute of Higher Education

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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)

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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

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Ranking of Conferences

• Tier 0 SIGCOMM, MobiCom (10)1
• Tier 1 MobiHoc (1015)3, SenSys1, MobiSys
  (1518),
• Tier 1.5 Infocom1, ICDCS (18) 3, RTSS4
  (2025), ICNP, PerCom (10-15)
• Tier 2IPSN3, IWQoS, 1 MSWiM (2025) 1,
  MASS2, DCOSS (25)
• Tier 4 Globecom, WCNC, ICC..(gt30)
• Partially borrowed from Prof. Dong Xuans talk
• The ranking is only applicable to Sensor Network
  research, and may significantly change for other
  fields