Autonomous Adaptive Resource Management in Sensor Network Systems for Environmental Monitoring

Abstract

The paper describes the use of model predictive control (MPC) as a framework for optimal resource management in environmental monitoring sensor networks. The MPC formulation adapts sensor and network parameters (such as sensor sampling rates, and routing of data) that impact the utilization of the system resources (such as energy reserves at off-shore in-situ sensors, and wireless bandwidth). The control parameters are optimized so as to maximize a measure of the total information extracted from the system. This information measure takes into account the spatio-temporal events of interests that are detected in the environment. The approach is illustrated on a coastal monitoring and forecast system that is in operation in the New York harbor and surrounding area. Offline results using actual modeled data from in-situ sensory measurements demonstrate how the sensor parameters can be adapted to maximize observability of a freshwater plume while ensuring that individual system components operate within their physical limitations.