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.
|Title of host publication||2020 European Conference on Networks and Communications, EuCNC 2020|
|Publisher||Institute of Electrical and Electronics Engineers Inc.|
|Number of pages||5|
|Publication status||Published - 2020 Jun|
|Event||29th European Conference on Networks and Communications, EuCNC 2020 - Virtual, Dubrovnik, Croatia|
Duration: 2020 Jun 15 → 2020 Jun 18
|Name||2020 European Conference on Networks and Communications, EuCNC 2020|
|Conference||29th European Conference on Networks and Communications, EuCNC 2020|
|Period||20/6/15 → 20/6/18|
Bibliographical noteFunding Information:
This research was supported by a grant to Bio-Mimetic Robot Research Center Funded by Defense Acquisition Program Administration, and by Agency for Defense Development (UD190018ID).
© 2020 IEEE.
All Science Journal Classification (ASJC) codes
- Computer Networks and Communications
- Hardware and Architecture
- Signal Processing