IoT system for remote monitoring of mangrove forest the Sundarbans
Asif Rahman Rumee
arrumee@gmail.comDept. of Computer Science and Engineering, Jashore University of Science and Technology (Bangladesh)
Abstract
In-situ monitoring of mangrove forests is expensive, cumbersome, time consuming and error-prone, hence remote approaches are being used widely nowadays. Remote sensing using satellites, UAVs and other devices is incapable of collecting many important types of data required for processing, therefore a prototype of an IoT device is designed and built for monitoring environmental parameters of the largest mangrove forest in the world, the Sundarbans in Bangladesh. The prototype is tested for a few hours in a simulated environment where the readings are updated every 2 seconds and alert notifications are received if an emergency event occurs. The simulation results prove the effectiveness of the proposed device and the feasibility of it for low cost remote monitoring of the mangrove forest.
Keywords:
remote monitoring, IoT, mangrove forests, SundarbansReferences
C. Giri, Recent Advancement in Mangrove Forests Mapping and Monitoring of the World Using Earth Observation Satellite Data, Remote Sensing 13(4). (2021) 563, https://doi.org/10.3390/rs13040563.
DOI: https://doi.org/10.3390/rs13040563
Google Scholar
Y. Jiang, L. Zhang, M. Yan, J. Qi, T. Fu, S. Fan, B. Chen, High-Resolution Mangrove Forests Classification with Machine Learning Using Worldview and UAV Hyperspectral Data, Remote Sensing 13(8) (2021) 1529.
DOI: https://doi.org/10.3390/rs13081529
Google Scholar
K. Mirakhorlou, S. Teimouri, M. Abadeh, Mapping potential of mangrove forests based on site demands (Geomorphological factors and physico-chemical characteristics of soil and water), Environ. Conserv 23 (2017) 90-97.
Google Scholar
M. Ruwaimana, B. Satyanarayana, V. Otero, A. M. Muslim, M. A. Syafiq, S. Ibrahim, D. Raymaekers, N. Koedam, F. Dahdouh-Guebas, The advantages of using drones over space-borne imagery in the mapping of mangrove forests, PloS one 13(7) (2018) e0200288.
DOI: https://doi.org/10.1371/journal.pone.0200288
Google Scholar
T. D. Pham, N. Yokoya, D. T. Bui, K. Yoshino, D. A. Friess, Remote sensing approaches for monitoring mangrove species, structure, and biomass: Opportunities and challenges, Remote Sensing 11(3) (2019) 230.
DOI: https://doi.org/10.3390/rs11030230
Google Scholar
K. D. Purkayastha, R. K. Mishra, A. Shil, and S. N. Pradhan, IoT Based Design of Air Quality Monitoring System Web Server for Android Platform, Wireless Personal Communications 118(4) (2021) 2921-2940.
DOI: https://doi.org/10.1007/s11277-021-08162-3
Google Scholar
M. Anachkova, S. Domazetovska, Z. Petreski, V. Gavriloski, Design of low-cost wireless noise monitoring sensor unit based on IoT concept, Journal of Vibroengineering 23(4) (2021).
DOI: https://doi.org/10.21595/jve.2021.21709
Google Scholar
F. Akhter, H. R. Siddiquei, M. E. E. Alahi, K. Jayasundera, S. C. Mukhopadhyay, An IoT-enabled Portable Water Quality Monitoring System with MWCNT/PDMS Multifunctional Sensor for Agricultural Applications, IEEE Internet of Things Journal (2021).
DOI: https://doi.org/10.1109/JIOT.2021.3069894
Google Scholar
P. Sumathi, R. Subramanian, V. V. Karthikeyan, S. Karthik, Soil monitoring and evaluation system using EDL‐ASQE: Enhanced deep learning model for IoI smart agriculture network, International Journal of Communication Systems (2021) e4859.
DOI: https://doi.org/10.1002/dac.4859
Google Scholar
M. S. Uddin, E. R. V. Steveninck, M. Stuip, M. A. R. Shah, Economic valuation of provisioning and cultural services of a protected mangrove ecosystem: A case study on Sundarbans Reserve Forest, Bangladesh, Ecosystem Services 5 (2013) 88-93.
DOI: https://doi.org/10.1016/j.ecoser.2013.07.002
Google Scholar
A. N. M. Abdullah, N. Stacey, S. T. Garnett, B. Myers, Economic dependence on mangrove forest resources for livelihoods in the Sundarbans, Bangladesh, Forest Policy and Economics 64 (2016) 15-24.
DOI: https://doi.org/10.1016/j.forpol.2015.12.009
Google Scholar
A. Jesin, Packet Tracer Network Simulator, Packt Publishing Ltd, 2014.
Google Scholar
Arduino UNO R3 board with DIP ATmega328P, https://www.walmart.com/ip/Arduino-UNO-R3-board-with-DIP-ATmega328P/133534784, [09.08.2021].
Google Scholar
SIM808 Module GSM GPRS GPS Development Board IPX SMA with GPS Antenna for Arduino Raspberry Pi Support 2G 3G 4G SIM Card, https://www.aliexpress.com/item/1005001967026161.html?spm=a2g0o.productlist.0.0.5173b0dclMbfH9&algo_pvid=807751fc-8db4-4387-af70-84777f58bd1c&algo_exp_id=807751fc-8db4-4387-af70-84777f58bd1c-2, [09.08.2021].
Google Scholar
KOOKYE 16 in 1 Smart Home Sensor Modules Kit for Arduino Raspberry Pi DIY Professional, https://www.walmart.com/ip/KOOKYE-16-in-1-Smart-Home-Sensor-Modules-Kit-for-Arduino-Raspberry-Pi-DIY-Professional/392581400, [09.08.2021].
Google Scholar
Authors
Asif Rahman Rumeearrumee@gmail.com
Dept. of Computer Science and Engineering, Jashore University of Science and Technology Bangladesh
Statistics
Abstract views: 481PDF downloads: 267
License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
