Predictive power control for dynamic state estimation over wireless sensor networks with relays

We present a predictive power controller for state estimation of a stationary ARMA process over a wireless sensor network (WSN), consisting of sensor nodes, relays, and a single gateway (GW). The state estimate is formed centrally at the GW by using packets received from sensors and relays. The latter perform network coding of sensor measurements. Communication from sensors and relays to the GW is over a fading channel. Packet loss probabilities depend upon the time-varying channel gains and the transmission powers used. To achieve an optimal trade-off between state estimation quality and energy expenditure, in our approach the GW decides upon the in general time-varying transmission powers of sensors and relays. This decision process is carried out on-line and adapts to changing channel conditions by using elements of stochastic model predictive control. Simulations on measured channel data illustrate the performance achieved by the proposed controller.

[1]  Jörg Widmer,et al.  Network coding: an instant primer , 2006, CCRV.

[2]  Shuguang Cui,et al.  Linear Coherent Decentralized Estimation , 2008, IEEE Trans. Signal Process..

[3]  M. Sternad,et al.  Unbiased power prediction of Rayleigh fading channels , 2002, Proceedings IEEE 56th Vehicular Technology Conference.

[4]  Rudolf Ahlswede,et al.  Network information flow , 2000, IEEE Trans. Inf. Theory.

[5]  Torsten Söderström,et al.  Discrete-time Stochastic Systems , 2002 .

[6]  Xuemin Shen,et al.  Wireless sensor networking [Guest Editorial] , 2007 .

[7]  Jan M. Maciejowski,et al.  Predictive control : with constraints , 2002 .

[8]  Hamid Gharavi,et al.  Special issue on sensor networks and applications , 2003 .

[9]  G. Goodwin,et al.  Finite constraint set receding horizon quadratic control , 2004 .

[10]  Anders Ahlén,et al.  Fixed Link Margins Outperform Power Control in Energy-Limited Wireless Sensor Networks , 2007, 2007 IEEE International Conference on Acoustics, Speech and Signal Processing - ICASSP '07.

[11]  Allen Gersho,et al.  Vector quantization and signal compression , 1991, The Kluwer international series in engineering and computer science.

[12]  Daniel E. Quevedo,et al.  Predictive power control and multiple-description coding for wireless sensor networks , 2009, 2009 IEEE International Conference on Acoustics, Speech and Signal Processing.

[13]  David Q. Mayne,et al.  Constrained model predictive control: Stability and optimality , 2000, Autom..

[14]  John G. Proakis,et al.  Digital Communications , 1983 .

[15]  Daniel E. Quevedo,et al.  Energy Efficient State Estimation With Wireless Sensors Through the Use of Predictive Power Control and Coding , 2010, IEEE Transactions on Signal Processing.

[16]  Daniel E. Quevedo,et al.  A predictive power control scheme for energy efficient state estimation via wireless sensor networks , 2008, 2008 47th IEEE Conference on Decision and Control.

[17]  R. Yeung,et al.  Network coding theory , 2006 .