Optimal Radio-Mode Switching for Wireless Networked Control

Energy efficiency is a major issue in wireless networked control systems. In this chapter, we address this problem by considering more specifically the energy-consumption of the radio chip of sensor nodes. There exists an interesting and rich literature on the topics of intermittent and event-based control, allowing to turn off the sensor’s radio for longer time than what is possible with a classical periodic sampling. Such a literature only addresses policies using two radio-modes (Transmitting and Sleep), while this contribution aims at exploiting the use of various radio-modes, both transmitting modes having different transmission power, where increased power results in better transmission quality (fewer errors) but higher energy cost, and non-transmitting modes which correspond to switching off only some components of the chip, and result in having higher energy cost than Sleep but faster/less costly transition to transmission. We propose an event-based radio-mode switching policy to perform a trade-off between energy saving and performance of the control application. The switching policy is derived jointly with the feedback law using a switched model taking into account control and communication. We compute the optimal joint switching policy and feedback law using Dynamic Programming and we illustrate the results in simulations.

[1]  Bo Lincoln,et al.  Dynamic Programming and Time-Varying Delay Systems , 2003 .

[2]  Zuoyin Tang,et al.  An energy-efficient adaptive DSC scheme for wireless sensor networks , 2007, Signal Process..

[3]  Gregory J. Pottie,et al.  Wireless integrated network sensors , 2000, Commun. ACM.

[4]  Dimitri P. Bertsekas,et al.  Dynamic Programming and Optimal Control, Two Volume Set , 1995 .

[5]  Masato Ishikawa,et al.  43rd IEEE Conference on Decision and Control , 2005 .

[6]  JAMAL N. AL-KARAKI,et al.  Routing techniques in wireless sensor networks: a survey , 2004, IEEE Wireless Communications.

[7]  Antonio Alfredo Ferreira Loureiro,et al.  Transmission power control techniques for wireless sensor networks , 2007, Comput. Networks.

[8]  Carlo Fischione,et al.  TREnD: A Timely, Reliable, Energy-Efficient and Dynamic WSN Protocol for Control Applications , 2010, 2010 IEEE International Conference on Communications.

[9]  Tamer Basar,et al.  Optimal control of LTI systems over unreliable communication links , 2006, Autom..

[10]  van der Arjan Schaft,et al.  50th IEEE Conference on Decision and Control and European Control Conference, 2011 , 2011 .

[11]  João Pedro Hespanha,et al.  A Survey of Recent Results in Networked Control Systems , 2007, Proceedings of the IEEE.

[12]  Scott F. Midkiff,et al.  Wireless Sensor Network Radio Power Management and Simulation Models , 2010 .

[13]  Carlo Fischione,et al.  Distributed cooperative processing and control over wireless sensor networks , 2006, IWCMC '06.

[14]  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.

[15]  George W. Irwin,et al.  Wireless networked control systems with QoS-based sampling , 2007 .

[16]  M. Eisen,et al.  Probability and its applications , 1975 .

[17]  W. P. M. H. Heemels,et al.  Periodic event-triggered control based on state feedback , 2011, IEEE Conference on Decision and Control and European Control Conference.

[18]  Alberto Bemporad,et al.  Energy-aware robust Model Predictive Control based on wireless sensor feedback , 2008, 2008 47th IEEE Conference on Decision and Control.

[19]  A. Goldsmith,et al.  Wireless network design for distributed control , 2004, 2004 43rd IEEE Conference on Decision and Control (CDC) (IEEE Cat. No.04CH37601).

[20]  Arthur L. Liestman,et al.  A hierarchical energy-efficient framework for data aggregation in wireless sensor networks , 2006, IEEE Transactions on Vehicular Technology.

[21]  Robin J. Evans,et al.  Feedback Control Under Data Rate Constraints: An Overview , 2007, Proceedings of the IEEE.

[22]  Dimitri P. Bertsekas,et al.  Dynamic Programming and Suboptimal Control: A Survey from ADP to MPC , 2005, Eur. J. Control.

[23]  Nicolas Cardoso de Castro,et al.  Energy-aware control and communication co-design in wireless net-worked control systems , 2012 .

[24]  R. P. Marques,et al.  Discrete-Time Markov Jump Linear Systems , 2004, IEEE Transactions on Automatic Control.

[25]  Bruno Sinopoli,et al.  Kalman filtering with intermittent observations , 2004, IEEE Transactions on Automatic Control.

[26]  Peter Seiler,et al.  Estimation with lossy measurements: jump estimators for jump systems , 2003, IEEE Trans. Autom. Control..

[27]  Naixue Xiong,et al.  An Energy-Efficient Dynamic Power Management in Wireless Sensor Networks , 2006, 2006 Fifth International Symposium on Parallel and Distributed Computing.

[28]  Zabih Ghassemlooy,et al.  A MIMO-ANN system for increasing data rates in organic visible light communications systems , 2013, 2013 IEEE International Conference on Communications (ICC).

[29]  Steven Liu,et al.  Optimal Control and Scheduling of Switched Systems , 2011, IEEE Transactions on Automatic Control.

[30]  Carlo Fischione,et al.  Breath: A Self-Adapting Protocol for Wireless Sensor Networks in Control and Automation , 2008, 2008 5th Annual IEEE Communications Society Conference on Sensor, Mesh and Ad Hoc Communications and Networks.

[31]  Lino Guzzella,et al.  On Implementation of Dynamic Programming for Optimal Control Problems with Final State Constraints , 2010 .

[32]  Dimitri P. Bertsekas,et al.  Stochastic optimal control : the discrete time case , 2007 .

[33]  Karl Henrik Johansson,et al.  On energy-aware communication and control co-design in wireless networked control systems , 2010 .

[34]  R. Bellman Dynamic programming. , 1957, Science.