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

TL;DR

This paper introduces ASMP, an energy-efficient protocol for wireless sensor networks in disaster monitoring, combining adaptive sampling, prediction, and energy harvesting to ensure sustainability and data quality.

Contribution

The paper presents a novel energy management protocol, ASMP, integrating three adaptive sampling algorithms and energy harvesting techniques for permanent disaster monitoring systems.

Findings

ASMP reduces energy consumption while maintaining monitoring quality.

The adaptive sampling algorithms improve data collection efficiency.

Energy harvesting sustains sensor network operation over long periods.

Abstract

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 (EH-WSNs), 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 QoS. 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.