Title: Distributed Data Fusion in Sensor Networks
1Distributed Data Fusion in Sensor Networks
- PAMI Research Group
- ECE Department
- Bahador Khaleghi
2Outline
- Sensor Networks
- Characteristics
- Applications
- Challenges
- Distributed Data Fusion
- Distributed Kalman Filtering
- Whats Next
- References
3Sensor Networks
- Definition
- A network of large number of sensing,
computation, and communication enabled devices
performing distributed data gathering
collaboratively - Originally developed for military applications
- Multi-disciplinary field
- Wireless communication, computer networks, MEMS,
system and control, computer science - Sensor node (mote) components
- Sensing, computing, communication, and energy
source units
EPIC Mote (UC Berkeley)
Mote architecture
4Sample Architecture
5Requirements
WSN
Size, cost, computational power, bandwidth, and
energy constrained
Prone to failure (e.g. obstruction, loss of motes)
Distributed preferably self-organized
Large number densely deployed motes
Wireless communication media
6WSN Applications
- Distributed sensing and monitoring
- 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
- Military (surveillance, targeting)
- Public (traffic control)
- Healthcare and rescue (tracking elderly, drug
administration) - Business (human tracking)
7PermaSense Project
- Long-lived deployment of WSN in environmental
monitoring (since 2006) - Goals
- 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
- Two field sites in Swiss Alps
- 25 sensor nodes
- Ultra low power (148 uA)
- Ruggedized for durability (3 years unattended
lifetime) - Modular architecture (4 tiers)
8WSN Challenges
- Communication network
- Architecture and protocol stack (mostly network
and DL layer) - Topology
- Positioning of the sensors (could be random)
- Homogeneous vs. Heterogeneous
- Dynamic or static
- Clustering
- Sensor Management
- Efficient resource allocation
- Security (DOS attack and sink/black/worm/jamming
holes) - Fault tolerance (wrt link or node failure)
- Hardware platform design
- Realize low cost and tiny sensor nodes using MEMS
and NEMS technologies - Evaluation framework
- Measure performance quantitatively (accuracy,
latency, scalability, stability, fault tolerance) - Sensing and Data Fusion
- How to fuse data from many sensors using local
communication
9Distributed Data Fusion
- Solve detection and estimation problems using
- Centralized algorithms data is relayed to a
central sink - 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
- 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)
10Early 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
Uhllmann 1996 12 Covariance Intersection (CI)
permits the optimal fusion of estimates that are
correlated to an unknown degree
1970
2000
Shalom and Tse 1975 9 tracking in a cluttered
environment with probabilistic data association
Mutambara 1998 13 Distributed and
Decentralized Extended Information Filter (DDEIF)
estimates information about nonlinear state
parameters, observations, and system dynamics
(time-varying states)
Chong et al. 1983 10 how to optimally account
for correlations due to common information
(static states)
11Recent Work
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)
- Li et al. 2003 15 first general and systematic
approach to development of distributed fusion
rules (optimal fusion with time-invariant states)
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)
12Distributed Kalman Filtering
- Distributed algorithm for Kalman filtering
- 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 - Low-pass CF fusion (average) of measurements
- Band-pass CF fusion (average) of
inverse-covariance matrices
13Consensus Filters
- CFs are distributed algorithms that allow
calculation of average-consensus of time-varying
signals - Tracking uncertainty principle
Sensing model
Collective dynamics
14Extensions to DKF
- Revised DKF (2007) 5
- Recently proposed by R. Olfati-Saber
- Three types of DKF
- 1st Applicable to sensor networks with different
observation matrices (sensing models) - 2nd and 3rd Continuous-time distributed Kalman
filters with different consensus strategies - Diffusion DKF (2008) 7
- 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
- Proposed by Kim et al.
- Based on newly introduced multi-scale consensus
algorithm - Faster convergence and order-of-magnitude
reduction of the communication cost
15What Could Be Done Further
- Extension of Diffusion DKF to
- 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.)
16References
- 1 I.F. Akyildiz, W. Su, Y. Sankarasubramaniam,
E. Cayirci, Wireless sensor networks a survey,
Computer Networks 38 (2002) 393422 - 2 C. F. GarcÃa-Hernández, P. H.
Ibargüengoytia-González, J. GarcÃa-Hernández,
and J. A. Pérez-DÃaz, 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
17References
- 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)451460, 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 294300, 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
18References
- 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 FusionPart 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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