Abstract
Nowadays, the agriculture domain faces a lot of challenges for a better usage of its natural resources. For this purpose, and for the increasing danger of climate change, there is a need to locally monitor meteorological data and soil conditions to help make quicker and more adapted decision for the culture. Wireless Sensor Networks (WSN) can serve as a monitoring system for those types of features. However, WSN suffer from the limited energy resources of the motes which shorten the lifetime of the overall network. Every mote periodically captures the monitored feature and sends the data to the sink for further analysis depending on a certain sampling rate. This process of sending big amount of data causes a high energy consumption of the sensor node and an important bandwidth usage on the network. In this paper, a Machine Learning based Data Reduction Algorithm (MLDR) is introduced. MLDR focuses on environmental data for the benefits of agriculture. MLDR is a data reduction approach which reduces the amount of transmitted data to the sink by adding some machine learning techniques at the sensor node level by keeping data availability and accuracy at the sink. This data reduction helps reduce the energy consumption and the bandwidth usage and it enhances the use of the medium while maintaining the accuracy of the information. This approach is validated through simulations on MATLAB using real temperature data-sets from Weather-Underground sensor network. Results show that the amount of sent data is reduced by more than \(70\%\) while maintaining a very good accuracy with a variance that did not surpass 2\(^\circ \).
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
References
Azaza, M., Tanougast, C., Fabrizio, E., Mami, A.: Smart greenhouse fuzzy logic based control system enhanced with wireless data monitoring. ISA Trans. 61, 297–307 (2016)
Balducci, F., Impedovo, D., Pirlo, G.: Machine learning applications on agricultural datasets for smart farm enhancement. Machines 6(3), 38 (2018)
Díaz, S.E., Pérez, J.C., Mateos, A.C., Marinescu, M.C., Guerra, B.B.: A novel methodology for the monitoring of the agricultural production process based on wireless sensor networks. Comput. Electron. Agric. 76(2), 252–265 (2011)
Ghaddar, A., Razafindralambo, T., Simplot-Ryl, I., Tawbi, S., Hijazi, A.: Algorithm for data similarity measurements to reduce data redundancy in wireless sensor networks. In: International Symposium on “A World of Wireless, Mobile and Multimedia Networks” (WoWMoM), June 2010
Ghaddar, A., Razafindralambo, T., Simplot-Ryl, I., Simplot-Ryl, D., Tawbi, S., Hijazi, A.: Investigating data similarity and estimation through spatio-temporal correlation to enhance energy efficiency in WSNs. Ad Hoc Sens. Wirel. Netw. 16(4), 273–295 (2012)
Habib, C., Makhoul, A., Darazi, R., Salim, C.: Self-adaptive data collection and fusion for health monitoring based on body sensor networks. IEEE Trans. Industr. Inf. 12(6), 2342–2352 (2016)
Monteiro, L.C., Delicato, F.C., Pirmez, L., Pires, P.F., Miceli, C.: DPCAS: data prediction with cubic adaptive sampling for wireless sensor networks. In: International Conference on Green, Pervasive, and Cloud Computing, pp. 353–368. Springer (2017)
Musaazi, K.P., Bulega, T., Lubega, S.M.: Energy efficient data caching in wireless sensor networks: a case of precision agriculture. In: Nungu, A., Pehrson, B., Sansa-Otim, J. (eds.) e-Infrastructure and e-Services for Developing Countries (2015)
Ojha, T., Misra, S., Raghuwanshi, N.S.: Wireless sensor networks for agriculture: the state-of-the-art in practice and future challenges. Comput. Electron. Agric. 118, 66–84 (2015)
Patil, S.S., Thorat, S.A.: Early detection of grapes diseases using machine learning and IoT. In: International Conference on Cognitive Computing and Information Processing (CCIP), August 2016
Radhika, S., Rangarajan, P.: On improving the lifespan of wireless sensor networks with fuzzy based clustering and machine learning based data reduction. Appl. Soft Comput. 83, 105610 (2019)
Razafimandimby, C., Loscri, V., Vegni, A.M., Neri, A.: Efficient Bayesian communication approach for smart agriculture applications. In: IEEE Vehicular Technology Conference (VTC-Fall), September 2017
Razafimandimby, C., Loscri, V., Vegni, A.M., Aourir, D., Neri, A.: A Bayesian approach for an efficient data reduction in IoT. In: International Conference on Interoperability in IoT (InterIoT), November 2017
Salim, C., Makhoul, A., Darazi, R., Couturier, R.: Similarity based image selection with frame rate adaptation and local event detection in wireless video sensor networks. Multimed. Tools Appl. 78(5), 5941–5967 (2019)
Tayeh, G.B., Makhoul, A., Laiymani, D., Demerjian, J.: A distributed real-time data prediction and adaptive sensing approach for wireless sensor networks. Pervasive Mob. Comput. 49, 62–75 (2018)
Tayeh, G.B., Makhoul, A., Perera, C., Demerjian, J.: A spatial-temporal correlation approach for data reduction in cluster-based sensor networks (2019)
Toldov, V., Clavier, L., Loscrí, V., Mitton, N.: A Thompson sampling approach to channel exploration-exploitation problem in multihop cognitive radio networks. In: IEEE International Symposium on Personal, Indoor, and Mobile Radio Communications (PIMRC), September 2016
Wu, M., Tan, L., Xiong, N.: Data prediction, compression, and recovery in clustered wireless sensor networks for environmental monitoring applications. Inform. Sci. 329, 800–818 (2016). Special issue on Discovery Science
Acknowledgments
This work was partially supported by a grant from CPER DATA and by LIRIMA Agrinet project.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Salim, C., Mitton, N. (2020). Machine Learning Based Data Reduction in WSN for Smart Agriculture. In: Barolli, L., Amato, F., Moscato, F., Enokido, T., Takizawa, M. (eds) Advanced Information Networking and Applications. AINA 2020. Advances in Intelligent Systems and Computing, vol 1151. Springer, Cham. https://doi.org/10.1007/978-3-030-44041-1_12
Download citation
DOI: https://doi.org/10.1007/978-3-030-44041-1_12
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-44040-4
Online ISBN: 978-3-030-44041-1
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)