Distributed Data Fusion in Sensor Networks

1. Distributed Data Fusion in Sensor Networks PAMI Research Group ECE 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 EPIC Mote (UC Berkeley) Mote architecture

4. Sample Architecture

5. Requirements 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

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 Rao et al. 1991 [11]: fully decentralized Kalman filtering assuming perfect instantaneous communication among all nodes • Uncorrelated errors across quantities to be fused • Time-invariant states • Linear system dynamics • Linear sensor models 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)

11. Recent 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)

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 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!