Data Acquisition Filtering Focused on Optimizing Transmission in a LoRaWAN Network Applied to the WSN Forest Monitoring System
Author:
Brito Thadeu1234ORCID, Azevedo Beatriz Flamia125ORCID, Mendes João125ORCID, Zorawski Matheus12ORCID, Fernandes Florbela P.12ORCID, Pereira Ana I.125ORCID, Rufino José12ORCID, Lima José123ORCID, Costa Paulo34ORCID
Affiliation:
1. Research Centre in Digitalization and Intelligent Robotics CeDRI, Instituto Politécnico de Bragança, 5300-252 Bragança, Portugal 2. Laboratório para a Sustentabilidade e Tecnologia em Regiões de Montanha (SusTEC), Instituto Politécnico de Bragança, 5300-252 Bragança, Portugal 3. INESC TEC—INESC Technology and Science, 4200-465 Porto, Portugal 4. Faculty of Engineering, University of Porto, 4200-465 Porto, Portugal 5. Algoritmi Research Centre/LASI, Campus Azurém, University of Minho, 4800-058 Guimarães, Portugal
Abstract
Developing innovative systems and operations to monitor forests and send alerts in dangerous situations, such as fires, has become, over the years, a necessary task to protect forests. In this work, a Wireless Sensor Network (WSN) is employed for forest data acquisition to identify abrupt anomalies when a fire ignition starts. Even though a low-power LoRaWAN network is used, each module still needs to save power as much as possible to avoid periodic maintenance since a current consumption peak happens while sending messages. Moreover, considering the LoRaWAN characteristics, each module should use the bandwidth only when essential. Therefore, four algorithms were tested and calibrated along real and monitored events of a wildfire. The first algorithm is based on the Exponential Smoothing method, Moving Averages techniques are used to define the other two algorithms, and the fourth uses the Least Mean Square. When properly combined, the algorithms can perform a pre-filtering data acquisition before each module uses the LoRaWAN network and, consequently, save energy if there is no necessity to send data. After the validations, using Wildfire Simulation Events (WSE), the developed filter achieves an accuracy rate of 0.73 with 0.5 possible false alerts. These rates do not represent a final warning to firefighters, and a possible improvement can be achieved through cloud-based server algorithms. By comparing the current consumption before and after the proposed implementation, the modules can save almost 53% of their batteries when is no demand to send data. At the same time, the modules can maintain the server informed with a minimum interval of 15 min and recognize abrupt changes in 60 s when fire ignition appears.
Funder
Fundação para a Ciência e Tecnologia
Subject
Electrical and Electronic Engineering,Biochemistry,Instrumentation,Atomic and Molecular Physics, and Optics,Analytical Chemistry
Reference54 articles.
1. Hernández, L. (2019). The Mediterranean Burns: WWF’s Mediterrenean Proposal for the Prevention of Rural Fires, WWF. 2. A review on forest fire detection techniques;Alkhatib;Int. J. Distrib. Sens. Netw.,2014 3. Wildfires and global change;Pausas;Front. Ecol. Environ.,2021 4. Drivers of forest fire occurrence in the cultural landscape of Central Europe;Kula;Landsc. Ecol.,2018 5. San-Miguel-Ayanz, J., Durrant, T., Boca, R., Maianti, P., Libertà, G., Artés Vivancos, T., Oom, D., Branco, A., De Rigo, D., and Ferrari, D. (2022). Forest Fires in Europe, Middle East and North Africa 2021, European Union.
Cited by
3 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献
|
|