Login Register Cart 0
Generic placeholder image

International Journal of Sensors, Wireless Communications and Control

Editor-in-Chief

ISSN (Print): 2210-3279
ISSN (Online): 2210-3287

Back
Journal Home About Journal Editorial Board Journal Insight Current Issue Volumes/Issues
Subscribe
Research Article

Signal Assessment Using ML for Evaluation of WSN Framework in Greenhouse Monitoring

Author(s): Aarti Kochhar*, Naresh Kumar and Utkarsh Arora

Volume 12, Issue 9, 2022

Published on: 02 January, 2023

Page: [669 - 679] Pages: 11

DOI: 10.2174/2210327913666221220154338

Price: $65

Abstract

Background and Objective: The deployment of a Wireless Sensor Network (WSN) provides a useful aid for monitoring greenhouse-like environments. WSN helps in achieving precision agriculture i.e. more yield can be produced with precise inputs. Before the deployment of a sensor network, it is necessary to explore the communication range of nodes. Communication signals are affected by losses due to stems, fruits, twigs, leaves, infrastructure material, etc. in a greenhouse. So as part of the deployment strategy, signal assessment is required in the greenhouse.

Methods: This research work proposes a Machine Learning (ML) based signal assessment for the evaluation of WSN deployment in different structures of a tomato greenhouse. Signal strength is measured for a naturally ventilated greenhouse and a fan-pad ventilated greenhouse. Measurements for the naturally ventilated greenhouse are considered with two case scenarios i.e. with transmitter and receiver in the same lane and with transmitter and receiver in different lanes. Models are developed for measured values and evaluated in terms of correlation and error between measured and model formulated values.

Results and Conclusion: For the naturally ventilated greenhouse case scenario 1, correlation increases from 91.83% to 95.42% as the degree increases from 2 to 7. Correlation for naturally ventilated greenhouse case scenario 2 rises from 72.51% at degree 2 to 90.09% at degree 10. For the fan-pad ventilated greenhouse, the model has a more complex fitting because of the spatial variability within the greenhouse. Correlation of the model increases from 79.39% to 84.06 % with an increase in degree from 2 to 11. For the naturally ventilated greenhouse, better correlation is achieved at lower degrees compared to the fan-pad ventilated greenhouse.

Keywords: Fan-pad ventilated greenhouse, greenhouse monitoring, machine learning, naturally ventilated greenhouse, regression, total path loss, wireless sensor networks.

« Previous Next »
Graphical Abstract

[1]
Ajayan J, Nirmal D, Tayal S, et al. Nanosheet field effect transistors-A next generation device to keep Moore’s law alive: An intensive study. Microelectronics 2021; 114105141.
[http://dx.doi.org/10.1016/j.mejo.2021.105141]
[2]
Liu Y, Qian K, Wang K, He L. Effective scaling of blockchain beyond consensus innovations and Moore’s Law: challenges and opportunities. IEEE Syst J 2022; 16(1): 1424-35.
[http://dx.doi.org/10.1109/JSYST.2021.3087798]
[3]
Gulati K, Kumar Boddu RS, Kapila D, Bangare SL, Chandnani N, Saravanan G. A review paper on wireless sensor network techniques in Internet of Things (IoT). Mater Today Proc 2022; 51(1): 161-5.
[http://dx.doi.org/10.1016/j.matpr.2021.05.067]
[4]
Lata S, Mehfuz S, Urooj S. Secure and reliable WSN for internet of things: challenges and enabling technologies. IEEE Access 2021; 9: 161103-28.
[5]
Majid M, Habib S, Javed AR, et al. Applications of wireless sensor networks and internet of things frameworks in the industry revolution 4.0: A systematic literature review. Sensors (Basel) 2022; 22(6): 2087.
[http://dx.doi.org/10.3390/s22062087] [PMID: 35336261]
[6]
Ghayvat H, Mukhopadhyay S, Gui X, Suryadevara N. WSN-and IOT-based smart homes and their extension to smart buildings. Sensors (Basel) 2015; 15(5): 10350-79.
[http://dx.doi.org/10.3390/s150510350] [PMID: 25946630]
[7]
Begum K, Dixit S. Industrial WSN using IoT: A survey. 2016 International Conference on Electrical, Electronics, and Optimization Techniques (ICEEOT) 2016. 499-504.
[http://dx.doi.org/10.1109/ICEEOT.2016.7755660]
[8]
Singh RP, Javaid M, Haleem A, Suman R. Internet of things (IoT) applications to fight against COVID-19 pandemic. Diabetes Metab Syndr 2020; 14(4): 521-4.
[http://dx.doi.org/10.1016/j.dsx.2020.04.041] [PMID: 32388333]
[9]
Ali A, Ming Y, Chakraborty S, Iram S. A comprehensive survey on real-time applications of WSN. Future Internet 2017; 9(4): 77.
[http://dx.doi.org/10.3390/fi9040077]
[10]
Zahhad MA, Farrag M, Ali A. A comparative study of energy consumption sources for wireless sensor networks. Int J Grid Distrib Comput 2015; 8(3): 65-76.
[http://dx.doi.org/10.14257/ijgdc.2015.8.3.07]
[11]
Kandris D, Nakas C, Vomvas D, Koulouras G. Applications of wireless sensor networks: an up-to-date survey. Appl Syst Innov 2020; 3(1): 14.
[http://dx.doi.org/10.3390/asi3010014]
[12]
Kabalci E, Kabalci Y. From smart grid to internet of energy. (1st ed.), Sen Diego, C.A.: Academic Press 2019.
[13]
Hamami L, Nassereddine B. Application of wireless sensor networks in the field of irrigation: A review. Comput Electron Agric 2020; 179: 105782.
[http://dx.doi.org/10.1016/j.compag.2020.105782]
[14]
Khairunnni S, Ramli N, Muharam FM. Wireless sensor network (WSN) applications in plantation canopy areas: A review. Asian J Sci Res 2018; 11(2): 151-61.
[http://dx.doi.org/10.3923/ajsr.2018.151.161]
[15]
Shafi U, Mumtaz R, García-Nieto J, Hassan SA, Zaidi SAR, Iqbal N. Precision agriculture techniques and practices: From considerations to applications. Sensors (Basel) 2019; 19(17): 3796.
[http://dx.doi.org/10.3390/s19173796] [PMID: 31480709]
[16]
Kochhar A, Kumar N. Wireless sensor networks for greenhouses: An end-to-end review. Comput Electron Agric 2019; 163: 104877.
[http://dx.doi.org/10.1016/j.compag.2019.104877]
[17]
Messaoud S, Bradai A, Bukhari SHR, Quang PTA, Ahmed OB, Atri M. A survey on machine learning in Internet of Things: Algorithms, strategies, and applications. Internet Things 2020; 12: 100314.
[http://dx.doi.org/10.1016/j.iot.2020.100314]
[18]
Praveen Kumar D, Amgoth T, Annavarapu CSR. Machine learning algorithms for wireless sensor networks: A survey. Inf Fusion 2019; 49: 1-25.
[http://dx.doi.org/10.1016/j.inffus.2018.09.013]
[19]
Akhter R, Sofi SA. Precision agriculture using IoT data analytics and machine learning. J King Saud Univ - Comput. Inf Sci 2022; 34(8) (8 Pt B): 5602-18.
[http://dx.doi.org/10.1016/j.jksuci.2021.05.013]
[20]
Ali A, Hassanein HS. A fungus detection system for greenhouses using wireless visual sensor networks and machine learning. In IEEE Globecom Workshops (GC Wkshps), Waikoloa, HI, USA IEEE. December 2019; pp. 1-6.
[http://dx.doi.org/10.1109/GCWkshps45667.2019.9024412]
[21]
Joaquim MM, Kamble AW, Misra S, Badejo J, Agrawal A. IoT and machine learning based anomaly detection in WSN for a smart greenhouse data, engineering and applications. Singapore: Springer 2022; pp. 421-31.
[22]
Liu Q, Jin D, Shen J, Fu Z, Linge N. A WSN-based prediction model of microclimate in a greenhouse using extreme learning approaches. 18th International Conference on Advanced Communication Technology (ICACT) PyeongChang, Korea (South) IEEE. 2016; pp. 730-5.
[23]
Codeluppi G, Cilfone A, Davoli L, Ferrari G. AI at the edge: A smart gateway for greenhouse air temperature forecasting. In IEEE International Workshop on Metrology for Agriculture and Forestry (MetroAgriFor). 04-06 November 2020; Trento, Italy: IEEE. 2020; pp. 348-53.
[http://dx.doi.org/10.1109/MetroAgriFor50201.2020.9277553]
[24]
Yang S, Zhang J, Zhang J. Impact of foliage on urban mm wave wireless propagation channel: A ray-tracing based analysis. In 2019 International Symposium on Antennas and Propagation (ISAP) 27-30 October 2019: Xi'an, China, IEEE. 2019; pp. 1-3.
[25]
Sabri N, Fouad S, Mohammed SS, Syed AA, Al-Dhief FT, Raheemah A. Investigation of empirical wave propagation models in precision agriculture. MATEC Web of Conferences EDP Sciences. 2018; 150. 06020
[26]
Picallo I, Klaina H, López-Iturri P, et al. A radio channel model for D2D communications blocked by single trees in forest environments. Sensors (Basel) 2019; 19(21): 4606.
[http://dx.doi.org/10.3390/s19214606] [PMID: 31652740]
[27]
Zhang KQ. Wireless communications: principles, theory and methodology. Hoboken: John Wiley & Sons 2015.
[http://dx.doi.org/10.1002/9781119113263]
[28]
Barrios-Ulloa A, Ariza-Colpas P, Sánchez-Moreno H, Quintero-Linero A, De la Hoz-Franco E. Modeling radio wave propagation for wireless sensor networks in vegetated environments: a systematic literature review. Sensors (Basel) 2022; 22(14): 5285.
[http://dx.doi.org/10.3390/s22145285] [PMID: 35890965]
[29]
Gay-Fernandez JA, Cuinas I. Peer to peer wireless propagation measurements and path-loss modeling in vegetated environments. IEEE Trans Antenn Propag 2013; 61(6): 3302-11.
[http://dx.doi.org/10.1109/TAP.2013.2254452]
[30]
Sulyman AI, Alwarafy A, MacCartney GR, Rappaport TS, Alsanie A. Directional radio propagation path loss models for millimeter-wave wireless networks in the 28-, 60-, and 73-GHz bands. IEEE Trans Wirel Commun 2016; 15(10): 6939-47.
[http://dx.doi.org/10.1109/TWC.2016.2594067]
[31]
Saakian A. Radio wave propagation fundamentals. (2nd ed.), Norwood, MA: Artech House 2020.
[32]
Sabri N, Aljunid SA, Salim MS, Fouad S, Kamaruddin R. Wireless sensor network wave propagation in vegetation Recent trends in physics of material science and technology. Singapore: Springer 2015; pp. 283-98.
[http://dx.doi.org/10.1007/978-981-287-128-2_18]
[33]
Rahim HM, Leow CY, Abd Rahman T, Arsad A, Malek MA. Foliage attenuation measurement at millimeter wave frequencies in tropical vegetation. In 2017 IEEE 13th Malaysia International Conference on Communications (MICC). 28-30 November 2017: Johor Bahru, Malaysia: IEEE 2017; pp. 241-6.
[http://dx.doi.org/10.1109/MICC.2017.8311766]
[34]
Shutimarrungson N, Wuttidittachotti P. Realistic propagation effects on wireless sensor networks for landslide management. EURASIP J Wirel Commun Netw 2019; 2019(1): 94.
[http://dx.doi.org/10.1186/s13638-019-1412-6]
[35]
Al Salameh MS. Lateral ITU-R foliage and maximum attenuation models combined with relevant propagation models for forest at the VHF and UHF bands. Inter J Netw Commun 2014; 1(2): 55-63.
[36]
Cama-Pinto D, Damas M, Holgado-Terriza JA, et al. Empirical model of radio wave propagation in the presence of vegetation inside greenhouses using regularized regressions. Sensors (Basel) 2020; 20(22): 6621.
[http://dx.doi.org/10.3390/s20226621] [PMID: 33228055]
[37]
Hejselbæk J, Nielsen JØ, Fan W, Pedersen GF. Empirical study of near ground propagation in forest terrain for Internet-of-Things type device-to-device communication. IEEE Access 2018; 6: 54052-63.
[http://dx.doi.org/10.1109/ACCESS.2018.2871368]
[38]
Vougioukas S, Anastassiu HT, Regen C, Zude M. Influence of foliage on radio path losses (PLs) for wireless sensor network (WSN) planning in orchards. Biosyst Eng 2013; 114(4): 454-65.
[http://dx.doi.org/10.1016/j.biosystemseng.2012.08.011]
[39]
Digi XBee RF Modules. RF modules in multiple form factors for embedded IoT designs Available from: https://www.digi.com/products/embedded-systems/digi-xbee/rf-modules (Accessed on: 18-10-2021).
[40]
Olasupo T, Otero CE, Olasupo KO, Kostanic I. Empirical path loss models for wireless sensor network deployments in short and tall natural grass environments. IEEE Trans Antenn Propag 2016; 64(9): 1.
[http://dx.doi.org/10.1109/TAP.2016.2583507]
[41]
Salam A, Vuran MC, Irmak S. Di-Sense: In situ real-time permittivity estimation and soil moisture sensing using wireless underground communications. Comput Netw 2019; 151: 31-41.
[http://dx.doi.org/10.1016/j.comnet.2019.01.001]
[42]
Jawad HM, Jawad AM, Nordin R, et al. Accurate empirical path-loss model based on particle swarm optimization for wireless sensor networks in smart agriculture. IEEE Sens J 2020; 20(1): 552-61.
[http://dx.doi.org/10.1109/JSEN.2019.2940186]
[43]
Pal P, Sharma RP, Tripathi S, Kumar C, Ramesh D. 2.4 GHz RF received signal strength based node separation in WSN monitoring infrastructure for millet and rice vegetation. IEEE Sens J 2021; 21(16): 18298-306.
[http://dx.doi.org/10.1109/JSEN.2021.3083552]
[44]
Adafruit. DHT11, DHT22 and AM2302 sensors. Available from: https://learn.adafruit.com/dht (Accessed on: 18-10-2021).
[45]
DIGI. XBee/XBee-PRO S2C 802.15.4 Radio Frequency (RF) Module. Available from: https://www.digi.com/resources/documentation/digidocs/pdfs/90001500.pdf (Accessed on: 28-03-2022).
[46]
Chicco D, Warrens MJ, Jurman G. The coefficient of determination R-squared is more informative than SMAPE, MAE, MAPE, MSE and RMSE in regression analysis evaluation. Peer J Comput Sci 2021; 7e623
[http://dx.doi.org/10.7717/peerj-cs.623] [PMID: 34307865]
[47]
Linear Models. Available from: https://scikitlearn.org/stable/modules/linear_model.html (Accessed on: 17-10-2022).

Rights & Permissions Print Cite
Article Metrics
18
4
© 2024 Bentham Science Publishers | Privacy Policy