Distributed Data Fusion in
Sensor Networks
PAMI Research Group
ECE Department
Bahador Khaleghi
Outline
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Sensor Networks
Characteristics
Applications
Challenges
Distributed Data Fusion
Distributed Kalman Filtering
What’s Next
References
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Sensor Networks
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Definition
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Originally developed for military
applications
Multi-disciplinary field
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A network of large number of sensing,
computation, and communication
enabled devices performing distributed
data gathering collaboratively
EPIC Mote (UC Berkeley)
Wireless communication, computer
networks, MEMS, system and control,
computer science
Sensor node (mote) components
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Sensing, computing, communication,
and energy source units
Mote architecture
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Sample Architecture
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Requirements
WSN
Wireless
communication
media
Large number
& densely
deployed
motes
Size, cost,
computational
power,
bandwidth, and
energy
constrained
Prone to
failure (e.g.
obstruction,
loss of motes)
Distributed &
preferably
selforganized
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WSN Applications
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Distributed sensing and monitoring
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Military (reconnaissance and detection)
Environment (fire/flood detection, bio-complexity mapping)
Industry and business (process control and inventory
management)
Civilian (home automation)
Space exploration
Target tracking
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Military (surveillance, targeting)
Public (traffic control)
Healthcare and rescue (tracking elderly, drug administration)
Business (human tracking)
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PermaSense Project
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Long-lived deployment of WSN in
environmental monitoring (since 2006)
Goals
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Develop a set of wireless measurement units
for use in remote areas with harsh
environmental monitoring conditions
Gathering of environmental data that helps
to understand the processes that connect
climate change and rock fall in permafrost
area
Specs
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Two field sites in Swiss Alps
~25 sensor nodes
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Ultra low power (148 uA)
Ruggedized for durability (3 years unattended
lifetime)
Modular architecture (4 tiers)
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WSN Challenges
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Communication network
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Architecture and protocol stack (mostly network and DL layer)
Topology
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Sensor Management
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Realize low cost and tiny sensor nodes using MEMS and NEMS technologies
Evaluation framework
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Efficient resource allocation
Security (DOS attack and sink/black/worm/jamming holes)
Fault tolerance (wrt link or node failure)
Hardware platform design
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Positioning of the sensors (could be random)
Homogeneous vs. Heterogeneous
Dynamic or static
Clustering
Measure performance quantitatively (accuracy, latency, scalability, stability, fault
tolerance)
Sensing and Data Fusion
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How to fuse data from many sensors using local communication
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Distributed Data Fusion
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Solve detection and estimation problems using
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Centralized algorithms: data is relayed to a central sink
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Issues: data congestion, scalability, reliability
Distributed algorithms: data is used to compute local estimates
forwarded to nearby nodes; receiving nodes fuse data and
update local estimates
DDF design objectives
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Scalability: deployable in large networks
Efficiency (limited resources): less transmissions and
computing
Robustness and reliability: no centralized weak spot, handle
network imprecations (e.g. delayed information)
Autonomy (self-adaptability)
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Early Work
• Uncorrelated errors
across quantities to
be fused
• Time-invariant states
• Linear system
dynamics
• Linear sensor models
Rao et al. 1991 [11]:
fully decentralized
Kalman filtering
assuming perfect
instantaneous
communication
among all nodes
1970
Uhllmann 1996 [12]:
Covariance Intersection (CI)
permits the optimal fusion of
estimates that are correlated
to an unknown degree
2000
Shalom and Tse
1975 [9]:
tracking in a
cluttered
environment with
probabilistic data
association
Chong et al. 1983
[10]: how to
optimally account for
correlations due to
common information
(static states)
Mutambara 1998 [13]:
Distributed and Decentralized
Extended Information Filter
(DDEIF) estimates information
about nonlinear state
parameters, observations, and
system dynamics (time-varying
states)
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Recent Work
Li et al. 2003 [15]:
first general and
systematic approach
to development of
distributed fusion rules
(optimal fusion with
time-invariant states)
Boyd et al. 2005 [16]:
gossip-based methods for
distributed averaging
problem (each node
communicates with no more
than one neighbor in each
time slot)
2000
Present
Kumar et al 2003 [14]: DFuse architectural
framework for dynamic application-specified
data fusion in future sensor networks
• Fusion API facilitating fusion function
implementation
• Distributed dynamic fusion function
assignment and relocation (accommodating
dynamic nature of WSN)
Olfati-Saber et al. 2006 [4]:
Distributed Kalman Filter
(DKF)
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Distributed Kalman Filtering
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Distributed algorithm for Kalman filtering
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Applicable in large-scale sensor networks with limited capabilities
(e.g. local communication, routing)
Analyzable performance in terms of properties of the network
Excellent robustness properties regarding various network
imperfections, including delay, link loss, network fragmentation,
and asynchronous operation
Assumes identical sensing models across WSN
Discrete-time approach
Decomposes KF into n collaborative mirco-KFs with local
communication
Estimating inputs for each micro-KF involves two dynamic
consensus problems solved using two consensus filters
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Low-pass CF: fusion (average) of measurements
Band-pass CF: fusion (average) of inverse-covariance matrices
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Consensus Filters
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CFs are distributed algorithms that allow
calculation of average-consensus of timevarying signals
Sensing model
.
Collective dynamics
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ui (t )  s(t )  vi (i)
x  ( I n    L) x  ( I n  )u
Tracking uncertainty principle

1

where,  
(2 E  n)
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n
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Extensions to DKF
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Revised DKF (2007) [5]
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Recently proposed by R. Olfati-Saber
Three types of DKF
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Diffusion DKF (2008) [7]
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1st: Applicable to sensor networks with different observation matrices (sensing
models)
2nd and 3rd: Continuous-time distributed Kalman filters with different consensus
strategies
Proposed by Cattivelli et al.
Assumes linear system dynamics and sensing model
Replaces consensus with diffusion process and outperforms DKF
Multi-scale DKF (2008) [8]
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Proposed by Kim et al.
Based on newly introduced multi-scale consensus algorithm
Faster convergence and order-of-magnitude reduction of the communication
cost
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What Could Be Done Further
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Extension of Diffusion DKF to
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Heterogeneous networks
Nonlinear systems
Multi-scale diffusion scheme
An in-depth comparison between the DKF
and other existing decentralized fusion
algorithms
Deployment of DKF (and its variants) in
practical applications (e.g. surveillance,
monitoring, etc.)
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References
[1] I.F. Akyildiz, W. Su, Y. Sankarasubramaniam, E. Cayirci, “Wireless sensor
networks: a survey”, Computer Networks 38 (2002) 393–422
[2] C. F. García-Hernández, P. H. Ibargüengoytia-González, J. García-Hernández†,
and J. A. Pérez-Díaz, “Wireless Sensor Networks and Applications: a Survey”,
IJCSNS, VOL.7 No.3, March 2007
[3] C. CHONG, AND S. P. KUMAR, Sensor Networks: Evolution, Opportunities,
and Challenges”, PROCEEDINGS OF THE IEEE, VOL. 91, NO. 8, AUGUST 2003
[4] R Olfati-Saber, Distributed Kalman Filtering and Sensor Fusion in Sensor
Networks”, Lecture notes in control and information sciences, 2006 - Springer
[5] R. Olfati-Saber, “Distributed Kalman Filtering for Sensor Networks”, Proc. of the
46th IEEE Conference on Decision and Control, 2007
[6] R. Olfati-Saber, J. S. Shamma, “Consensus Filters for Sensor Networks and
Distributed Sensor Fusion”, Proceedings of IEEE Conference on Decision and
Control, 2005
[7] F. S. Cattivelli, C. G. Lopes, A. H. Sayed, “DIFFUSION STRATEGIES FOR
DISTRIBUTED KALMAN FILTERING: FORMULATION AND PERFORMANCE
ANALYSIS”, Proc. Cognitive Information Processing, Santorini, Greece, 2008
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References
[8] J. Kim, M. West, E. Scholte, and S. Narayanan, “Multiscale Consensus for
Decentralized Estimation and Its Application to Building Systems”, 2008
American Control Conference, 2008
[9] Y. Bar-Shalom and E. Tse, “Tracking in a cluttered environment with probabilistic
data association”, Automatica, 11(5):451–460, Sept. 1975.
[10] C. Y. Chong, E. Tse, and S. Mori, “Distributed estimation in networks”, In
Proceedings of the 1983 American Control Conference, volume 1, pages 294–
300, San Francisco, CA, Sept. 1983.
[11] B.S. Rao, and H.F. Durrant-Whyte, “Fully decentralized algorithm for
multisensor Kalman filtering”, IEE PROCEEDINGS-D, Vol. 138, NO. 5,
SEPTEMBER 1991
[12] J. K. Uhlmann, “General Data Fusion for Estimates With Unknown Cross
Covariances”, Proceedings of SPIE, 1996
[13] A. Mutambara, “Decentralized estimation and control for multisensor systems”,
CRC Press, 1998
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References
[14] R. Kumar, M. Wolenetz, B. Agarwalla, J. Shin, P. Hutto, A. Paul, and U.
Ramachandran, “DFuse: A Framework for Distributed Data Fusion”, Proceedings
of the 1st international conference on Embedded networked sensor systems, pp.
114-125, 2003
[15] X. R. Li, Y. Zhu, J. Wang, and C. Han, “Optimal Linear Estimation Fusion—Part
I: Unified Fusion Rules”, IEEE TRANSACTIONS ON INFORMATION THEORY, VOL.
49, NO. 9, SEPTEMBER 2003
[16] S. Boyd, A. Ghosh, S. Prabhakar, D. Shah, “Gossip Algorithms: Design, Analysis
and Applications”, Proceedings IEEE INFOCOM, 2005
[17] http://www.permasense.ch/
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Thank YOU!
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