Most Efficient Sensor Network Protocol for a Permanent Natural Disaster Monitoring System

Changmin Lee, Seong Lyun Kim

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)


To minimize enormous havoc from disasters, permanent environment monitoring is necessarily required. Thus, we propose a novel energy management protocol for energy harvesting wireless sensor networks, named the adaptive sensor node management protocol (ASMP). The proposed protocol makes system components to systematically control their performance to conserve the energy. Through this protocol, sensor nodes autonomously activate an additional energy conservation algorithm. ASMP embeds three sampling algorithms. For the optimized environment sampling, we proposed the adaptive sampling algorithm for monitoring (ASA-m). ASA-m estimates the expected time period to occur meaningful change. The meaningful change refers to the distance between two target data for the monitoring Quality of Service. Therefore, ASA-m merely gathers the data the system demands. The continuous adaptive sampling algorithm (CASA) solves the problem to be continuously decreasing energy despite of ASA-m. When the monitored environment shows a linear trend property, the sensor node in CASA rests a sampling process, and the server generates predicted data at the estimated time slot. For guaranteeing the self-sustainability, ASMP uses the recoverable adaptive sampling algorithm (RASA). RASA makes consumed energy smaller than harvested energy by utilizing the predicted data. RASA recharges the energy of the sensor node. Through this method, ASMP achieves both energy conservation and service quality.

Original languageEnglish
Article number9378785
Pages (from-to)11776-11792
Number of pages17
JournalIEEE Internet of Things Journal
Issue number15
Publication statusPublished - 2021 Aug 1

Bibliographical note

Funding Information:
Manuscript received July 20, 2020; revised December 20, 2020 and January 30, 2021; accepted February 28, 2021. Date of publication March 15, 2021; date of current version July 23, 2021. This work was supported in part by the Grant to Bio-Mimetic Robot Research Center Funded by Defense Acquisition Program Administration and in part by the Agency for Defense Development under Grant UD190018ID. This article was presented in part at the European Conference on Networks and Communications (EuCNC), Dubrovnik, Croatia, 2020, doi: 10.1109/EuCNC48522.2020.9200960. (Corresponding author: Seong-Lyun Kim.) The authors are with the Department of Electrical and Electronic Engineering, Yonsei University, Seoul 03722, South Korea (e-mail:; Digital Object Identifier 10.1109/JIOT.2021.3065928 1We define QoS as a spatial resolution of data collected on a server side, following definitions by Zhang et al. [1] and Al-Shammari et al. [2].

Publisher Copyright:
© 2014 IEEE.

All Science Journal Classification (ASJC) codes

  • Signal Processing
  • Information Systems
  • Hardware and Architecture
  • Computer Science Applications
  • Computer Networks and Communications


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