Real-time intelligent pattern recognition, resource management and control under constrained resources for distributed sensor networks

We propose a new machine learning architecture with integrated system control and resource management capability for use in autonomous sensing applications with limited resources. Novel neural network dimensionality reduction with a mixture-of-experts classifier ensures that only relevant information is processed while handling missing sensor data. A genetic optimisation algorithm is used to control the system in the presence of dynamic events, while ensuring that system constraints are met. This tight integration of control optimisation and machine learning algorithms results in a highly efficient intelligent sensor network. The applicability of our technology in remote health monitoring and environmental monitoring is shown.