Information-Driven Collaborative Processing for Diffusive Source Estimation in Wireless Sensor Networks
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Abstract
This paper discusses an accurate distributed algorithm for diffusive source localization while maintaining the low energy consumption of sensor nodes in wireless sensor networks. In this algorithm, the sensor selection scheme based on the information utility measure is used. To update the estimation in each selected node, a neighborhood radius equal to the communication range of the sensor nodes is defined and all sensors located in the neighborhood circle, whose radius is equal to the neighborhood radius and the selected node is its centre, collaborate their information. To decrease the energy consumption, the neighborhood radius is reduced gradually based on the error covariance value of the estimation. In addition, this paper includes a new method for the initial point calculation which is important in the recursive methods used for distributed algorithms in wireless sensor networks. Numerical examples are used to study the performance of the algorithms. Simulation results show the accuracy of the new algorithm becomes better while its energy consumption is low enough.
- T. Zhao and A. Nehorai, “Detecting and Estimating Biochemical Dispersion of a Moving Source in a Semi-Infinite Medium,” IEEE Transactions on Signal Processing, Vol. 54, No. 6, June 2006, pp. 2213-2225.
- X. Sheng and Y. H. Hu, “Maximum Likelihood Multiple- Source Localization Using Acoustic Energy Measurements with Wireless Sensor Networks,” IEEE Transactions on Signal Processing, Vol. 53, No. 1, January 2005, pp. 44-53.
- A. Pardo, S. Marco and J. Samitier, “Nonlinear Inverse Dynamic Models of Gas Sensing Systems Based on Chemical Sensor Arrays for Quantitative Measurements,” IEEE Transactions on Instrumentation and Measurement, Vol. 47, No. 3, June 1998, pp. 644-651.
- H. Ishida, T. Nakamoto and T. Moriizumi, “Remote sen- sing and localization of gas/odor source and distribution using mobile sensing system,” Proceedings of the International Conference on Solid-State Sensors and Actuators, Chicago, Vol. 1, June 1997, pp. 16-19.
- A. Nehorai, B. Porat and E. Paldi, “Detection and Locali- zation of Vaporemitting Sources,” IEEE Transactions on Signal Processing, Vol. 43, No. 1, January 1995, pp. 243- 253.
- T. Zhao and A. Nehorai, “Information-Driven Distributed Maximum Likelihood Estimation Based on Gauss-Newton Method in Wireless Sensor Networks,” IEEE Transactions on Signal Processing, Vol. 55, No. 9, September 2007, pp. 4669-4682.
- A. T. Ihler, J. W. Fisher, R. L. Moses and A. S. Willsky, “Nonparametric Belief Propagation for Self-Localization of Sensor Networks,” IEEE Journal on Selected Areas in Communications, Vol. 23, No. 4, April 2005, pp. 809- 819.
- J. L. Williams, J. W. Fisher and A. S. Willsky, “Optimization Approaches to Dynamic Routing of Measurements and Models in a Sensor Network Object Tracking Problem,” IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), Philadelphia, Vol. 5, March 2005, pp. 1061-1064.
- J. Liu, J. Reich and F. Zhao, “Collaborative In-Network Processing for Target Tracking,” EURASIP Journal on Applied Signal Processing, Vol. 2003, No. 4, 2003, pp. 378-391.
- M. G. Rabbat and R. D. Nowak, “Decentralized Source Localization and Tracking,” IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), Montreal, May 2004, pp. 921-924.
- P. Stoica and A. Nehorai, “MUSIC, Maximum Likelihood, and Cramer-Rao Bound,” IEEE Transactions on Acoustics Speech and Signal Processing, Vol. 37, No. 5, 1989, pp. 720-741.