Author:
Lata Suman,Verma Harish Kumar
Abstract
One of the possible solutions for meeting the rising food demands is to opt for wireless sensor networks (WSN) monitored intelligent greenhouses. Such greenhouses require wireless sensor nodes rather than individual sensors to monitor and control the various parameters responsible for the growth of the plants. The appropriate selection of the number of wireless sensor nodes and their placement is crucial for optimizing the cost of the wireless sensor network by minimizing the number of sensor nodes as well as the measurement error. This paper extends the two techniques, namely, equal step (ES) and equal segment area (ESA) techniques, reported earlier for the selection of the number and locations of sensors to suit multi-sensor nodes inside a greenhouse. It also compares these techniques with the equal-spacing approach. The multi-sensor nodes considered here have temperature and luminosity sensors. Initial locations of the multi-sensor nodes have been fixed on the basis of temperature profile on the premise that temperature is the most important parameter for the growth of the plants. Evaluation of these techniques has been done on the basis of the root of the sum of square errors (RSSE) of the individual parameters. The ESA technique has been found to be better than the ES technique for the assumed temperature and luminosity profiles. In the future, this work may be extended to other situations where other than temperature is the most important parameter. The other direction in which the work can be extended may be considering the 2D or even 3D distribution of sensors.
Publisher
Universiti Putra Malaysia
Subject
General Earth and Planetary Sciences,General Environmental Science
Reference31 articles.
1. Ahonen, T., Virrankoski, R., & Elmusrati, M. (2008). Greenhouse monitoring with wireless sensor network. In Proceedings of International Conference on Mechatronic and Embedded Systems and Applications (pp. 403-408). IEEE Publishing. http://doi.org/10.1109/mesa.2008.4735744
2. Akkaş, M. A., & Sokullu, R. (2017). An IoT-based greenhouse monitoring system with Micaz motes. Procedia Computer Science, 113, 603-608. https://doi.org/10.1016/j.procs.2017.08.300
3. Balendonck, J., Van Os, E. A., Van der Schoor, R., Van Tuijl, B. A. J., & Keizer, L. C. P. (2010). Monitoring spatial and temporal distribution of temperature and relative humidity in greenhouses based on wireless sensor technology. In International Conference on Agricultural Engineering-AgEng (pp. 443-452). CABI Publishing.
4. Barker, J. C. (1990). Effects of day and night humidity on yield and fruit quality of glasshouse tomatoes (Lycopersicon esculentum Mill.). Journal of Horticultural Science, 65(3), 323-331. http://doi.org/10.1080/00221589.1990.11516061
5. Burrell, J., Brooke, T., & Beckwith, R. (2004). Vineyard computing: Sensor networks in agriculture production. IEEE Pervasive Computing, 3(1), 38-45. http://doi.org/10.1109/MPRV.2004.1269130
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