Data Fusion Improves the Coverage of Sensor Networks Guoliang Xing Assistant Professor Department of...
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Transcript of Data Fusion Improves the Coverage of Sensor Networks Guoliang Xing Assistant Professor Department of...
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
17
(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
19
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
22
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
23
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 (%
)
28
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
29
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