Abstract
The monitoring of various interest parameters in a culture was proven as a useful tool, which improve the agricultural production. The monitoring of crops in precision farming can be achieved through a multiplicity of technologies; however, using Wireless Sensor Networks results in low-power deployments, thus becoming a dominant option. Our research proposes the development of a new agricultural field monitoring system based on atmospheric sensors capable of measuring the different parameters of the air and soil sensors measuring the soil parameters. In this chapter, we propose a periodic hybrid routing algorithm sensitive to the threshold for the collection of environmental data. The proposed algorithm uses region-based cluster approaches for the deployment of sensor nodes, which provide effective coverage to the entire agricultural area. In addition, a proposed clustering protocol based on the combination of residual energy and distance between neighboring nodes, to obtain optimal Cluster-head and improve energy efficiency in the WSN. The results of the simulation show that the proposed routing algorithm exceeds other well-known algorithms based on packet delivery, energy consumption and network lifetime as a performance measure.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Cheng-Jun, Z. (2014). Research and implementation of agricultural environment monitoring based on internet of things. In 2014 fifth international conference on intelligent systems design and engineering applications. IEEE, pp. 748–752.
Fang, Q., Zhao, F., & Guibas, L. (2003). Lightweight sensing and communication protocols for target enumeration and aggregation. In Proceedings of the 4th ACM international symposium on mobile ad hoc networking & computing. ACM, pp. 165–176.
Garcia-Sanchez, A.-J., Garcia-Sanchez, F., & Garcia-Haro, J. (2011). Wireless sensor network deployment for integrating video-surveillance and data-monitoring in precision agriculture over distributed crops. Computers and Electronics in Agriculture, 75(2), 288–303.
Gubbi, J., Buyya, R., Marusic, S., et al. (2013). Internet of things (IoT): A vision, architectural elements, and future directions. Future Generation Computer Systems, 29(7), 1645–1660.
Hao, Z., Zhang, Z., & Chao, H.-C. (2015). A cluster-based fuzzy fusion algorithm for event detection in heterogeneous wireless sensor networks. Journal of Sensors, 2015, 1.
Heinzelman, W. B., Chandrakasan, A. P., Balakrishnan, H., et al. (2002). An application-specific protocol architecture for wireless microsensor networks. IEEE Transactions on Wireless Communications, 1(4), 660–670.
Hwang, J., Shin, C., & Yoe, H. (2010). Study on an agricultural environment monitoring server system using wireless sensor networks. Sensors, 10(12), 11189–11211.
Jiang, J.-A., Wang, C.-H., Liao, M.-S., et al. (2016). A wireless sensor network-based monitoring system with dynamic convergecast tree algorithm for precision cultivation management in orchid greenhouses. Precision Agriculture, 17(6), 766–785.
Khedo, K. K., Perseedoss, R., Mungur, A., et al. (2010). A wireless sensor network air pollution monitoring system. International Journal of Wireless & Mobile Networks, 2(2), 31–45.
Khelifi, F., Kaddachi, M. L., Bouallegue, B., et al. (2014). Fuzzy logic-based hardware architecture for event detection in wireless sensor networks. In 2014 world symposium on computer applications & research (WSCAR). IEEE, pp. 1–4.
Khelifi, F., Bradai, A., Kaddachi, M. L., et al. (2017). A novel intelligent mechanism for monitoring in wireless sensor networks. In: 2017 IEEE international conference on consumer electronics (ICCE). IEEE, pp. 170–171.
Kuila, P., & Jana, P. K. (2014). Energy efficient clustering and routing algorithms for wireless sensor networks: Particle swarm optimization approach. Engineering Applications of Artificial Intelligence, 33, 127–140.
Liao, M.-S., Chen, S.-F., Chou, C.-Y., et al. (2017). On precisely relating the growth of phalaenopsis leaves to greenhouse environmental factors by using an IoT-based monitoring system. Computers and Electronics in Agriculture, 136, 125–139.
Manjunatha, P., Verma, A. K., & Srividya, A. (2008). Multi-sensor data fusion in cluster based wireless sensor networks using fuzzy logic method. In 2008 IEEE region 10 and the third international conference on industrial and information systems. IEEE, pp. 1–6.
Maurya, S., & Jain, V. K. (2017). Energy-efficient network protocol for precision agriculture: Using threshold sensitive sensors for optimal performance. IEEE Consumer Electronics Magazine, 6(3), 42–51.
Nugroho, A. P., Okayasu, T., Hoshi, T., et al. (2016). Development of a remote environmental monitoring and control framework for tropical horticulture and verification of its validity under unstable network connection in rural area. Computers and Electronics in Agriculture, 124, 325–339.
Ojha, T., Misra, S., & Raghuwanshi, N. S. (2015). Wireless sensor networks for agriculture: The state-of-the-art in practice and future challenges. Computers and Electronics in Agriculture, 118, 66–84.
Sánchez, V., Gil, S., Flores, J. M., et al. (2015). Implementation of an electronic system to monitor the thermoregulatory capacity of honeybee colonies in hives with open-screened bottom boards. Computers and Electronics in Agriculture, 119, 209–216.
Tayeb, S., Latifi, S., & Kim, Y. (2017). A survey on IoT communication and computation frameworks: An industrial perspective. In 2017 IEEE 7th annual Computing and Communication Workshop and Conference (CCWC). IEEE, pp. 1–6.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this chapter
Cite this chapter
Khelifi, F. (2020). Monitoring System Based in Wireless Sensor Network for Precision Agriculture. In: Alam, M., Shakil, K., Khan, S. (eds) Internet of Things (IoT). Springer, Cham. https://doi.org/10.1007/978-3-030-37468-6_24
Download citation
DOI: https://doi.org/10.1007/978-3-030-37468-6_24
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-37467-9
Online ISBN: 978-3-030-37468-6
eBook Packages: Computer ScienceComputer Science (R0)