Energy Adaptive Sensor Scheduling for Noisy Sensor Measurements

摘要

In wireless sensor network applications, sensor measurements are corrupted by noises resulting from harsh environmental conditions, hardware and transmission errors. Minimising the impact of noise in an energy constrained sensor network is a challenging task. We study the problem of estimating environmental phenomena (e.g., temperature, humidity, pressure) based on noisy sensor measurements to minimise the estimation error. An environmental phenomenon is modeled using linear Gaussian dynamics and the Kalman filtering technique is used for the estimation. At each time step, a group of sensors is scheduled to transmit data to the base station to minimise the total estimated error for a given energy budget. The sensor scheduling problem is solved by dynamic programming and one-step-look-ahead methods. Simulation results are presented to evaluate the performance of both methods. The dynamic programming method produced better results with higher computational cost than the one-step-look-ahead method.