Identyfikatory
Warianty tytułu
System IoT do zdalnego monitorowania lasów namorzynowych Sundarbans
Języki publikacji
Abstrakty
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 forests 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 forests.
Słowa kluczowe
Czasopismo
Rocznik
Tom
Strony
254--258
Opis fizyczny
Bibliogr.15 poz., rys., tab.
Twórcy
autor
- Department of Computer Science and Engineering, Jashore University of Science and Technology, Jashore-7408, Bangladesh
Bibliografia
- [1] 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.
- [2] 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.
- [3] 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.
- [4] 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.
- [5] 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.
- [6] 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.
- [7] 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).
- [8] 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).
- [9] 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.
- [10] 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.
- [11] 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.
- [12] A. Jesin, Packet Tracer Network Simulator, Packt Publishing Ltd, 2014.
- [13] Arduino UNO R3 board with DIP ATmega328P, https://www.walmart.com/ip/Arduino-UNO-R3-board-with-DIP-ATmega328P/133534784, [09.08.2021].
- [14] 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].
- [15] 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].
Uwagi
Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2021).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-1e5ace77-91dd-4c6c-9f30-feac1d6aa9a4