18Jun 2017

THE IMPACT OF GREENHOUSE ENVIRONMENTAL CONDITIONS ON THE SIGNAL STRENGTH OF WI-FI BASED SENSOR NETWORK.

  • Kahramanmaras Sutcu Imam University, Turkoglu MYO, 46880, Kahramanmaras/Turkey.
  • Kahramanmaras Sutcu Imam University, Department of Informatics, 46060, Kahramanmaras/Turkey.
Crossref Cited-by Linking logo
  • Abstract
  • Keywords
  • References
  • Cite This Article as
  • Corresponding Author

Because greenhouses are controlled sites for agricultural production, environmental conditions such as temperature, relative humidity, carbon dioxide level and solar radiation must be kept at a certain level for plant growth. Therefore, indoor environmental parameters must be constantly monitored in order to take precautions when necessary. Thanks to the technological advancements in recent years, wireless sensor networks have been widely used in monitoring and controlling systems. In addition to monitoring environmental conditions, wireless sensors are used to irrigation, ventilation and heating equipment and in Internet of Things applications. Greenhouse environment is different from external climate, and sudden climatic changes such as high relative humidity and condensation of moisture may occur due to various cultural activities. As a result, some problems may be encountered in data transfer over wireless networks. Therefore, this study analyzes the impact of greenhouse environmental conditions on the data transfer performance of wireless network. The study was conducted in a greenhouse with a floor area of 150 m2 where side walls were covered with double layer polyethylene (PE) with a gap of 5 cm and roof was covered with single layer PE covering material. The main station was positioned outside the greenhouse. Sensor nodes were positioned 20 meters away from the main station within the greenhouse. The research lasted for 20 days between March 20 and April 10. Temperature, relative humidity and signal strength values were transferred to the main station every five minutes thanks to the micro-processor software. The obtained data were used to statistically assess the signal strength performance of sensor nodes. The findings demonstrated that high relative humidity influenced signal strength positively while high temperature influenced signal strength negatively.


  1. Abbasi, A.Z., Islam, N., Shaikh, Z.A., 2014. A review of wireless sensors and networks applications in agriculture. Computer Standards & Interfaces 36, 263-270.
  2. Abouzar, P., Michelson, D.G., Hamdi, M., 2016. RSSI-Based Distributed Self-Localization for Wireless Sensor Networks Used in Precision Agriculture. Ieee T Wirel Commun 15, 6638-6650.
  3. Akyildiz, I.F., Su, W., Sankarasubramaniam, Y., Cayirci, E., 2002. Wireless sensor networks: a survey. Computer Networks 38, 393-422.
  4. Aqeel-Ur-Rehman, Abbasi, A.Z., Islam, N., Shaikh, Z.A., 2014. A review of wireless sensors and networks\' applications in agriculture. Computer Standards and Interfaces 36, 263-270.
  5. Bannister, K., Giorgetti, G., Gupta, S.K., 2008. Wireless Sensor Networking for ?Hot? Applications: Effects of Temperature on Signal Strength, Data Collection and Localization.
  6. Bezerra, T.D., de Sousa, J.A.R., Eleuterio, S.A.D., Rocha, J.S., 2015. Accuracy of Propagation Models to Power Prediction in WSN ZigBee Applied in Outdoor Environment. 2015 Sixth Argentine Conference on Embedded Systems (Case), 19-24.
  7. Boano, C.A., Wennerstrom, H., Zuniga, M., Brown, J., Keppitiyagama, C., Oppermann, F., Roedig, U., Norden, L.-A., Voigt, T., Römer, K., 2013. Hot Packets: A systematic evaluation of the effect of temperature on low power wireless transceivers.
  8. Capsuto, B., Frolik, J., 2006. A system to monitor signal fade due to weather phenomena for outdoor sensor systems, Fifth International Conference on Information Processing in Sensor Networks (IPSN 2006).
  9. Chen, Y., Shi, Y.L., Wang, Z.Y., Huang, L., 2016. Connectivity of wireless sensor networks for plant growth in greenhouse. Int J Agr Biol Eng 9, 89-98.
  10. Damas, M., Prados, A., G?mez, F., Olivares, G., 2001. HidroBus? system: fieldbus for integrated management of extensive areas of irrigated land. Microprocessors and Microsystems 25, 177-184.
  11. Ehlert, D., Schmerler, J., Voelker, U., 2004. Variable rate nitrogen fertilisation of winter wheat based on a crop density sensor. Precis Agric 5, 263-273.
  12. ESP8266, 2015. ESP8266EX Datasheet. Espressif Systems Datasheet, 1-31.
  13. Ferentinos, K., Katsoulas, N., Tzounis, A., Kittas, C., Bartzanas, T., 2014. A climate control methodology based on wireless sensor networks in greenhouses, XXIX International Horticultural Congress on Horticulture: Sustaining Lives, Livelihoods and Landscapes (IHC2014): 1107, pp. 75-82.
  14. Ferrandez-Pastor, F.J., Garcia-Chamizo, J.M., Nieto-Hidalgo, M., Mora-Pascual, J., Mora-Martinez, J., 2016. Developing Ubiquitous Sensor Network Platform Using Internet of Things: Application in Precision Agriculture. Sensors-Basel 16.
  15. Gang, L.L.L., 2006. Design of Greenhouse Environment Monitoring and Controlling System Based on Bluetooth Technology [J]. Transactions of the Chinese Society for Agricultural Machinery 10, 97-100.
  16. Garcia, M., Tomas, J., Boronat, F., Lloret, J., 2009. The Development of Two Systems for Indoor Wireless Sensors Self-location. Ad Hoc Sens Wirel Ne 8, 235-258.
  17. Gubbi, J., Buyya, R., Marusic, S., Palaniswami, M., 2013. Internet of Things (IoT): A vision, architectural elements, and future directions. Future Generation Computer Systems 29, 1645-1660.
  18. He, J., Wang, J., He, D., Dong, J., Wang, Y., 2011. The design and implementation of an integrated optimal fertilization decision support system. Mathematical and Computer Modelling 54, 1167-1174.
  19. Kolokotsa, D., Saridakis, G., Dalamagkidis, K., Dolianitis, S., Kaliakatsos, I., 2010. Development of an intelligent indoor environment and energy management system for greenhouses. Energy Conversion and Management 51, 155-168.
  20. Latal, J., Vitasek, J., Hajek, L., Vanderka, A., Koudelka, P., Kepak, S., Vasinek, V., 2016. Regresion Models Utilization for RSSI Prediction of Professional FSO Link with Regards to Atmosphere Phenomena. 2016 International Conference on Broadband Communications for Next Generation Networks and Multimedia Applications (Cobcom).
  21. Lea-Cox, J.D., Kantor, G., Anhalt, J., Ristvey, A., Ross, D.S., 2007. A wireless sensor network for the nursery and greenhouse industry, Southern Nursery Association Research Conference.
  22. Liu, D., Cao, X., Huang, C.W., Ji, L.L., 2016. Intelligent Agriculture Greenhouse Environment Monitoring System Based on IOT Technology. 2015 International Conference on Intelligent Transportation, Big Data and Smart City (Icitbs), 487-490.
  23. Markham, A., Trigoni, N., Ellwood, S., 2010. Effect of rainfall on link quality in an outdoor forest deployment, Wireless Information Networks and Systems (WINSYS), Proceedings of the 2010 International Conference on. IEEE, pp. 1-6.
  24. Ortega-Corral, C., Palafox, L.E., Garcia-Macias, J.A., Garcia, J.S., Aguilar, L., Hipolito, J.I.N., 2014. Transmission Power Control based on Temperature and Relative Humidity. 2014 Ieee Ninth International Conference on Intelligent Sensors, Sensor Networks and Information Processing (Ieee Issnip 2014).
  25. Rao, Y., Jiang, Z.H., Lazarovitch, N., 2016. Investigating signal propagation and strength distribution characteristics of wireless sensor networks in date palm orchards. Comput Electron Agr 124, 107-120.
  26. Rose, K., Eldridge, S., Chapin, L., 2015. The Internet of Things (IoT): An Overview, In: Marsan, C. (Ed.), Understanding the Issues and Challenges of a More Connected World. Internet Society, Geneva, Switzerland, p. 53.
  27. Sensirion, 2008. Humidity at a Glance Most Relevant Equations with Sample Code SENSIRION AG, https://www.sensirion.com/fileadmin/user_upload/customers/sensirion/Dokumente/2_Humidity_Sensors/Sensirion_Humidity_Sensors_at_a_Glance_V1.pdf.
  28. Shi, Y.B., Shi, Y.P., Xiu, D.B., Wang, X., Wang, M.M., Wang, R.X., 2013. Design of wireless sensor system for agricultural Micro environment based on WiFi. Sensors, Measurement and Intelligent Materials, Pts 1-4 303-306, 215-222.
  29. SI7021, 2015. Si7021 Datasheet A20 I2C Humidity and Temperature Sensor. Silicon Laboratories, 1-36.
  30. Sui, R., Baggard, J., 2015. Wireless Sensor Network for Monitoring Soil Moisture and Weather Conditions. Appl Eng Agric 31, 193-201.
  31. Thelen, J., Goense, D., Langendoen, K., 2005. Radio wave propagation in potato fields, 1st workshop on wireless network measurement, Riva del Garda, Italy, April 2005, pp. np-np.
  32. Wang, C., Zhao, C., Qiao, X., Zhang, X., Zhang, Y., 2007. The design of wireless sensor networks node for measuring the greenhouse\'s environment parameters, International Conference on Computer and Computing Technologies in Agriculture. Springer, pp. 1037-1046.
  33. Wang, J.N., Niu, X.T., Zheng, L.J., Zheng, C.T., Wang, Y.D., 2016. Wireless Mid-Infrared Spectroscopy Sensor Network for Automatic Carbon Dioxide Fertilization in a Greenhouse Environment. Sensors-Basel 16.
  34. Wu, S.F., Wang, G.W., Xiao, Y.Y., Xue, J.P., 2014. Vegetable Monitoring and Control Based on Internet of Things. International Symposium on Signal Processing Biomedical Engineering, and Informatics (Spbei 2013), 619-628.
  35. Xie, Y.Q., Wang, Y., Nallanathan, A., Wang, L.N., 2016. An Improved K-Nearest-Neighbor Indoor Localization Method Based on Spearman Distance. Ieee Signal Proc Let 23, 351-355.
  36. Yang, I.-C., Chen, S., Huang, Y.-I., Hsieh, K.-W., Chen, C.-T., Lu, H.-C., Chang, C.-L., Lin, H.-M., Chen, Y.-L., Chen, C.-C., 2008. RFID-integrated multi-functional remote sensing system for seedling production management, Proceedings of 2008 ASABE annual International meeting.
  37. Zhang, W., Kantor, G., Singh, S., 2004. Integrated wireless sensor/actuator networks in an agricultural application, Proceedings of the 2nd international conference on Embedded networked sensor systems. ACM, pp. 317-317.
  38. Zhou, B., Yang, Q.L., Liu, K.N., Li, P.Q., Zhang, J., Wang, Q.J., 2013. Greenhouse Irrigation Control System Design based on ZigBee and Fuzzy PID Technology. Proc Spie 8762.

[Ali Cayli and Ali Selcuk Mercanli. (2017); THE IMPACT OF GREENHOUSE ENVIRONMENTAL CONDITIONS ON THE SIGNAL STRENGTH OF WI-FI BASED SENSOR NETWORK. Int. J. of Adv. Res. 5 (6). 774-781] (ISSN 2320-5407). www.journalijar.com


Ali Cayli
Kahramanmaras Sutcu Imam University

DOI:


Article DOI: 10.21474/IJAR01/4475       DOI URL: http://dx.doi.org/10.21474/IJAR01/4475


Share this article