Distributed Data Fusion in Sensor Networks

PAMI Research GroupECE Department

Bahador Khaleghi

2

Outline Sensor Networks Characteristics Applications Challenges Distributed Data Fusion Distributed Kalman Filtering What’s Next References

3

Sensor 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

Mote architecture

EPIC Mote (UC Berkeley)

4

Sample Architecture

5

Requirements

WSN

Wireless communicatio

n media

Large number & densely

deployed motes

Size, cost, computationa

l power, bandwidth, and energy constrained

Prone to failure (e.g. obstruction,

loss of motes)

Distributed & preferably

self-organized

6

WSN 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)

7

PermaSense 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)

8

WSN 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

9

Distributed 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)

10

Early Work

1970 2000

Shalom and Tse 1975 [9]: tracking in a cluttered environment with probabilistic data association

• Uncorrelated errors across quantities to be fused• Time-invariant states• Linear system dynamics• Linear sensor models

Chong et al. 1983 [10]: how to optimally account for correlations due to common information (static states)

Uhllmann 1996 [12]: Covariance Intersection (CI) permits the optimal fusion of estimates that are correlated to an unknown degree

Mutambara 1998 [13]: Distributed and Decentralized Extended Information Filter (DDEIF) estimates information about nonlinear state parameters, observations, and system dynamics (time-varying states)

Rao et al. 1991 [11]: fully decentralized Kalman filtering assuming perfect instantaneous communicationamong all nodes

11

Recent Work

2000 Present

Li et al. 2003 [15]: first general and systematic approach to development of distributed fusion rules (optimal fusion with time-invariant states)

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)

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)

Olfati-Saber et al. 2006 [4]: Distributed Kalman Filter (DKF)

12

Distributed 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

13

Consensus Filters CFs are distributed algorithms that

allow calculation of average-consensus of time-varying signals

Tracking uncertainty principle

)()()( ivtstu ii

uIxLIx nn )()(.

5

)2(,

1

n

nEwhere

Sensing model

Collective dynamics

14

Extensions 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

15

What 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.)

16

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

17

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

18

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/

19

Thank YOU!