Dynamic power management in a wireless sensor network using predictive control

Technological advances have made wireless sensor nodes cheap and reliable enough to be brought into various application domains. These nodes are powered by battery, thus they have a limited lifespan which is a major drawback for their acceptance. This paper addresses a power consumption control problem of wireless nodes equipped with batteries. Dynamic power management is used to dynamically re-configure the set of sensor nodes in order to provide given services and performance levels with a minimum number of active nodes and/or a minimum load on such components. The power control formulation is based on model predictive control with constraints and binary optimization variables, leading to a mixed integer quadratic programming problem. Simulations are performed to demonstrate the efficiency of the proposed control method.

[1]  Ian F. Akyildiz,et al.  Wireless sensor networks: a survey , 2002, Comput. Networks.

[2]  Jiming Chen,et al.  Utility-based asynchronous flow control algorithm for wireless sensor networks , 2010, IEEE Journal on Selected Areas in Communications.

[3]  Purushottam Kulkarni,et al.  Energy Harvesting Sensor Nodes: Survey and Implications , 2011, IEEE Communications Surveys & Tutorials.

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

[5]  Kameshwar Poolla,et al.  Energy management in wireless sensor networks , 2012 .

[6]  Mario Di Francesco,et al.  Energy conservation in wireless sensor networks: A survey , 2009, Ad Hoc Networks.

[7]  Mircea Lazar,et al.  Model predictive control of hybrid systems : stability and robustness , 2006 .

[8]  Marko Bacic,et al.  Model predictive control , 2003 .

[9]  Thierry Val,et al.  An advanced study of energy consumption in an IEEE 802.15.4 based network: Everything but the truth on 802.15.4 node lifetime , 2012, Comput. Commun..

[10]  Catherine Rosenberg,et al.  Homogeneous vs heterogeneous clustered sensor networks: a comparative study , 2004, 2004 IEEE International Conference on Communications (IEEE Cat. No.04CH37577).

[11]  Graham C. Goodwin,et al.  Characterisation Of Receding Horizon Control For Constrained Linear Systems , 2003 .

[12]  Daniel E. Quevedo,et al.  Power Control and Coding Formulation for State Estimation With Wireless Sensors , 2013, IEEE Transactions on Control Systems Technology.

[13]  Eduardo F. Camacho,et al.  Fault-tolerant model predictive control , 2010, 2010 IEEE 15th Conference on Emerging Technologies & Factory Automation (ETFA 2010).

[14]  Frank Allgöwer,et al.  Nonlinear Model Predictive Control , 2007 .

[15]  Eduardo Fernandez-Camacho,et al.  Introduction to Model Based Predictive Control , 1995 .

[16]  Stephen J. Wright,et al.  Nonlinear Predictive Control and Moving Horizon Estimation — An Introductory Overview , 1999 .

[17]  Jasmeer. Ramlal.  Model predictive control of hybrid systems. , 2002 .

[18]  Alberto Bemporad,et al.  Control of systems integrating logic, dynamics, and constraints , 1999, Autom..

[19]  M. Morari,et al.  Predictive Control of Constrained Hybrid Systems , 2000 .

[20]  Robert L. Williams,et al.  Linear State-Space Control Systems , 2007 .

[21]  Johan Löfberg,et al.  YALMIP : a toolbox for modeling and optimization in MATLAB , 2004 .

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