Paper:

# Auction-Based Consensus Mechanism for Cooperative Tracking in Multi-Sensor Surveillance Systems

## Ahmed M. Elmogy, Fakhreddine O. Karray, and Alaa M. Khamis

University of Waterloo 200 University Avenue, West, Waterloo, Ontario, Canada

This paper presents an auction-based consensusmechanism for cooperative targets tracking using minimum numbers of mobile sensors in order to reduce energy consumption due to sensor mobilization. After targets are detected, they are clustered using hybrid subtractive K-means clustering technique to reduce the number of trackers needed to track these detected targets. The proposed target tracking process is based on an Extended Kohonen neural network. In order to decrease the network sensitivity to initial conditions, a supervised learning technique is used to get the initial weights of unsupervised Extended Kohonen Map instead of random initialization. An auction-based consensus mechanism is used as a cooperation methodology between trackers during tracking. Monitoring sensors either remain stationary or begin following their targets is based on this mechanism. The simulation results confirms that the proposed approach outperforms other approaches in energy saving and achieves better coverage.

*J. Adv. Comput. Intell. Intell. Inform.*, Vol.14, No.1, pp. 13-20, 2010.

- [1] I. F. Akyildiz, W. Su, Y. Sankarasubramaniam, and E. Cayirci, “Wireless sensor networks: a survey,” Computer Networks 38, pp. 393-422, 2002.
- [2] T. Kohonen, “Self-organization and Associative Memory,” Springer, ISBN: 0-387-51387-6, 1989.
- [3] B. P. Gerkey and M. J. Mataric, “Sold!: Auction Methods for Multirobot Coordination,” IEEE Trans. on Robotics and Automation, Vol.18, No.5, pp. 785-768, Oct. 2002.
- [4] M. R. Endsley, “Toward a Theory of Situation Awareness in Dynamic Systems,” Human Factors, Vol.37, No.1, pp. 32-64, 1995.
- [5] J. O’Rourke, “Art Gallery Theorems and Algorithms,” London, U.K.: Oxford Univ. Press, 1987.
- [6] K. Hsiang, W. Leow, and M. Ang, “Autonomic Mobile Sensor Network With Self- Coordinated Task Allocation and Execution,” IEEE Trans. On SMC- Part C, Vol.36, No.3, pp. 315-327, May 2006.
- [7] N. Heo and P. Varsheny, “Energy Efficient Deployment of Intelligent Mobile sensor Networks,” IEEE Trans. on SMC- Part A, Vol.35, No.1, pp. 78-92, Jan. 2005.
- [8] H. Qi, S. S. Iyengar, and K. Chakrabarty, “Distributed sensor fusion-a review of recent research,” J. Franklin Inst., Vol.338, pp. 655-668, 2001.
- [9] A. Howard, M. J. Mataric, and G. S. Sukhatme, “Mobile sensor network deployment using potential fields: A distributed, scalable solution to the area coverage problem,” in Proc. 6th Int. Conf. Distributed Autonomous Robotic Systems, Fukuoka, Japan, pp. 299-308, 2002.
- [10] J. Cortes, S. Martnez, T. Karatas, and F. Bullo, “Coverage Control for Mobile Sensing Networks,” IEEE Trans. On Robotics and Automation, Vol.20, No.2, pp. 243-255, April 2004.
- [11] R. E. Kalman, “A New Approach to Linear Filtering and Prediction Problems,” J. of Basic Engineering, Vol.82, No.1, pp. 35-45, 1960.
- [12] J. K. Uhlmann, “General Data Fusion for Estimates with Unknown Cross Covariances,” Proc. of the SPIE AeroSense Conf., 1996.
- [13] A. R. Benaskeur and J. Roy, “A Consistent Filter for Robust Decentralized Data Fusion,” Defense Research & Development Canada — Valcartier, Technical Report, 2002.
- [14] L. D. Stone, C. A. Barlow, and T. L. Corwin, “Bayesian Multiple Target Tracking,” Artech House Books, ISBN: 0 58053 024 9, 2000.
- [15] D. Simon, “Optimal State Estimation: Kalman, H Infinity, and Nonlinear Approaches,” Wiley, 2006.
- [16] F. Amoozegar, A. Notash, and H. Y. Pang, “Survey of fuzzy logic and neural network technology for Multi-Target Tracking,” In Proc. of 8th Conf. on Automatic Target Recognition, Vol.3371, pp. 160-171, ISBN 0-8194-2820-5, 1998.
- [17] Y. Dong, “Energy Efficiency and Surveillance Applications in Mobile Sensor Networks,” Purdue University, Ph.D. Thesis, 2006.
- [18] Y. B. Shalom and T. E. Fortmann, “Tracking and data Association,” Academic Press, Inc., Orlando, Florida, 1988.
- [19] P. L. Bogler, “Radar principles with applications to tracking systems,” John Wiley & Sons, New York 1990.
- [20] F. Gustafsson, F. Gunnarsson, K. Bergman, U. Forssell, J. Jansson, R. Karlsson, and P. J. Nordlund, “Particle filters for positioning, navigation, and tracking,” IEEE Trans. on Signal Processing, Vol.50, No.2, pp. 425-437, Feb. 2002.
- [21] M. Isard and A. Blake, “Condensation- conditional propagation for visual tracking,” Int. J. of Computer Vision, Vol.29, No.1, pp. 5-28, 1998.
- [22] D. Liu and L. C. Fu, “Target tracking in an environment of nearly stationary and biased clutter,” In Proc. of the 2001 IEEE/RSJ Int. Conf. on Intelligent Robots and Systems, pp. 1358-1363, Maui, Hawaii, Oct. 2001.
- [23] W. Zhang and G. Cao, “Optimizing free reconfiguration for mobile target tracking in sensor network,” In Proc. of the IEEE Int. Conf. on Computer Communication, pp. 2434-2445, March 2004.
- [24] R. Murrieta, H. G. Banos, and B. Tavar, “A reactive motion planner to maintain visibility of unpredictable targets,” In Proc. of IEEE Int. Conf. on Robotics and Automation, pp. 4242-4247, Washington DC, May 2002.
- [25] L. E. Parker, “Distributed algorithms for multi-robot observation of multiple moving targets,” Int. J. of Autonomous Robots, Vol.12, No.3, pp. 231-255, 2002.
- [26] L. E. Parker, “Cooperative robotics for multi-target Observation,” Intelligent Automation and Soft Computing, special issue on Robotics Research at Oak Ridge National Laboratory, Vol.5, No.1, pp. 5-19, 1999.
- [27] A. Kolling and S. Carpin, “Multi-robot cooperation for surveillance of multiple moving targets — a new behavioral approach,” In Proc. of the IEEE Int. Conf. on Robotics and automation, pp. 1311-1316, 2006.
- [28] A. Kolling and S. Carpin, “Cooperative observation of multiple moving targets: an algorithm and its formalization,” Int. J. of Robotics Research, Vol.26, No.9, pp. 935-953, Nov. 2007.
- [29] B. Jung and G. S. Sukhatme, “Tracking targets using multiple robots: The effect of environment occlusion,” Autonomous Robots, Vol.13, No.3, pp. 191-205, 2002.
- [30] W. W. Ltu, C. H. Jing, B. W. Wang, Y. Shi, and H. U. Fen, “Study on Combining Subtractive Clustering with Fuzzy C-Means Clustering,” Proc. of the Second Int. Conf. on Machine Learning and Cybernetics, Xian, 2-5 November 2003.
- [31] J. B. MacQueen, “Some Methods for classification and Analysis of Multivariate Observations,” Proc. of 5-th Berkeley Symposium on Mathematical Statistics and Probability, Berkeley, University of California Press, 1, pp. 281-297, 1967.
- [32] T. Kanungo, D. M. Mount, N. S. Netanyahu, C. D. Piatko, R. Silverman, and A. Y. Wu, “An Efficient k-Means Clustering Algorithm: Analysis and Implementation,” IEEE Trans. on Pattern Analysis and Machine Intelligence, Vol.24, No.7, pp. 881-891, July 2002.
- [33] L. M. Gambardella and C. Versino, “Learning The Visuomotor Coordination Of A Mobile Robot By Using The Invertible Kohonen Map,” From Natural to Artificial Neural Computation, Int. Workshop on Artificial Neural Networks Proc., pp. 1084-1091, Berlin, 1995.
- [34] G. R. Mir: Mobot Simulator, Available at: http://www.mobotsoft.com/mobotsim.htm, Oct. 2008.

This article is published under a Creative Commons Attribution-NoDerivatives 4.0 Internationa License.