Predictive controller for heterogeneous sensor network operation in dynamic environments

We discuss a novel control methodology for power management in heterogeneous distributed sensor networks. Many algorithms for resource management in sensor networks require complete knowledge of the external environment and the sensor network system, are rule-based and cannot handle rapidly changing environments; this restricts their use in real-world environments. We present an event based control optimization formulation of the resource management problem and discuss a method to adaptively change desired system performance of the sensor network in response to events. This functionality is critical in field-deployable sensor networks where the available energy is extremely limited. This limitation disallows continuous operation as a very expensive option and necessitates system adaptation as a means to extend operational lifetime in the face of dynamic external events. We show results on synthetic sensor networks where only partially accurate information about the external world and the sensing system is available and illustrate the efficacy of the control algorithm in handling dynamic events with guaranteed minimum system lifespan via efficient usage of energy resources. We show that the control algorithm makes effective control decisions about the use of energy resources with varying sensor reliabilities.

[1]  Douglas C. Hittle,et al.  Robust reinforcement learning control with static and dynamic stability , 2001 .

[2]  Gavin Brown,et al.  The Use of the Ambiguity Decomposition in Neural Network Ensemble Learning Methods , 2003, ICML.

[3]  Elie Bienenstock,et al.  Neural Networks and the Bias/Variance Dilemma , 1992, Neural Computation.

[4]  Martin J. Oates,et al.  The Pareto Envelope-Based Selection Algorithm for Multi-objective Optimisation , 2000, PPSN.

[5]  Wei-Min Shen,et al.  Dynamic Distributed Resource Allocation: A Distributed Constraint Satisfaction Approach , 2001, ATAL.

[6]  Matt Welsh,et al.  CodeBlue: An Ad Hoc Sensor Network Infrastructure for Emergency Medical Care , 2004 .

[7]  Sandeep K. S. Gupta,et al.  Research challenges in wireless networks of biomedical sensors , 2001, MobiCom '01.

[8]  José Rodellar,et al.  Adaptive Predictive Control: From the Concepts to Plant Optimization , 1995 .

[9]  Jeffrey S. Vetter,et al.  Autopilot: adaptive control of distributed applications , 1998, Proceedings. The Seventh International Symposium on High Performance Distributed Computing (Cat. No.98TB100244).

[10]  L. Chandramouli,et al.  Real-time intelligent pattern recognition, resource management and control under constrained resources for distributed sensor networks , 2004, 2004 IEEE International Joint Conference on Neural Networks (IEEE Cat. No.04CH37541).

[11]  Geoffrey E. Hinton,et al.  Adaptive Mixtures of Local Experts , 1991, Neural Computation.