Optimization framework and clustering-based algorithm for energy-aware adaptive sensing

摘要

The problem of controlling the operation of the multiple sensors in a monitoring network is one of jointly optimizing for both information requirements and energy constraints. These goals conflict with each other - increasing the sampling rate collects more data but this consumes energy and hence reduces system lifetime. On the other hand, sampling at a higher rate when the environment is not changing does not generate more useful information. This work presents a general data-centric framework for adaptive sensing that brings together both information requirements and energy constraints in an optimization problem for generating sensor control actions. Clustering of correlated sensors is proposed as a solution for this framework where the collected data is periodically used to learn the relationship between sensors and energy parameters. Control is divided into periods of full sensing and periods of approximation. During periods of full sensing, measurements collected from all sensors are used to compute pair-wise correlations which are then used as the distance metric for clustering. A regression function is learned to approximate the measurement of a sensor given its past sensor measurements and the measurements of another sensor in the cluster. The control framework is evaluated using a publicly-available dataset of approximately 2.4 million sensor readings collected from 54 sensors over a period of approximately 35 days. The parameter space of the clustering-based adaptive sensing policy is systematically explored using this dataset. The framework enables a point on the energy-information trade-off curve to be quantified for different choices of policy parameters.