The Sampling Period Estimation based Adaptive Sampling Algorithm for a Self-sustainable Disaster Monitoring System
Changmin Lee, Seong‐Lyun Kim · 2020
To introduce the most energy-efficient adaptive sampling algorithm for the disaster monitoring system, this study proposes a novel algorithm based on sampling period estimation for gathering only valuable information. It is called an adaptive sampling algorithm for monitoring (ASA-m). This method estimates the next sampling period to get the information that is required by the monitoring system. In order to estimate this time period, the proposed algorithm uses an advanced trend estimation method considering an energy transfer mechanism, i.e heat, or wave. The sampling period prediction is based on estimating changes in energy sources from the trend of prior environmental change. Through this method, sensor nodes can predict the environmental changing velocity by using an estimated energy source. Based on this property, each sensor node estimates the sampling period for collecting the next semantic information. It has some advantages to minimize the consumed energy of sensor nodes and the network traffic by collecting meaningless data. As a result, the proposed algorithm can reduce 65.4% of the energy consumption and 50% of the sampling count.