Abstract
Based on the clustering center of data, this paper optimizes the data transmission path, and proposes an improved Dijkstra algorithm, which is applied to the path optimization of soil moisture sensors in tea plantations. Firstly, the date of soil moisture in tea plantation is collected under the condition of full coverage of the sensor network. Then, the AP clustering algorithm is used to cluster collected data to obtain the cluster center. Secondly, the dissimilarity values of the soil moisture data and the weighted combination of distance between the sensor nodes are used to identify the edge weights and calculate the adjacency matrix of the Dijkstra algorithm. Finally, with the clustering center as the starting point and the convergence point of wireless sensor network as the end point, Dijkstra algorithm is used to search the path. In order to verify the superiority of the proposed algorithm, the algorithm is compared with the ant colony optimization algorithm. In this paper, the data dissimilarity on the path is 25.0652, the total cost of the path is 0.3613, and the difference between the average soil moisture of the tea plantation is 0.1872 and the number of sensors required is 6, The ant colony algorithm obtained the data dissimilarity on the path of 20.4538, the total cost of the path is 0.5483, and the difference between the average soil moisture of the tea plantation is 0.7321 and the number of sensors required is 9. The test results show that the date of path obtained by this method has the largest dissimilarity and the shortest path, and the data collected by this method is representative, which can accurately reflect the distribution of soil moisture in tea plantations. At the same time, the number of sensors is reduced from 25 to 6, reducing the cost of the system.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Zhang, X., Yin, C., Wu, H.: Energy-saving optimization strategy of wireless sensor networks for large-scale farmland habitat monitoring. Intell. Agric. 1(02), 55–63 (2019)
Wang, H., Zhang, X., Lu, H.: Sensor coverage optimization strategy based on geometric coverage algorithm. Comput. Appl. Res. (8) (2017)
Zhu, X., Li, Y., Li, N., et al.: Sensor layout optimization design based on improved discrete particle swarm optimization. J. Electron. 41(10), 2104–2108 (2013)
Liu, X., Zhang, X., Hu, T., et al.: Application of distributed cuckoo algorithm in layout optimization of wireless sensor networks. Comput. Appl. Res. 35(07), 149–151 (2018). No. 321
Lin, F.Y.S., Chiu, P.L.: A simulated annealing algorithm for energy-efficient sensor network design. In: Third International Symposium on Modeling and Optimization in Mobile, AdHoc, and Wireless Networks, pp. 183–189 (2005)
Wang, Y.C., Hu, C.C., Tseng, Y.C.: Efficient placement and dispatch of sensors in a wireless sensor network. Trans. Mob. Comput., 262–274 (2008)
Yin, H., Du, G., Peng, Z., et al.: Study on the optimal sensor placement method of the weedy monkey swarm algorithm. Comput. Eng. Sci. 40(04), 60–69 (2018). No. 280
Wu, Z., Sun, J., Wang, Y., et al.: Optimum layout strategy of soil moisture sensor based on genetic algorithm. J. Agric. Eng. 27(5), 219–223 (2011)
Zhang, W., Zhang, M., Jiang, C., Jiang, Y.: Layout optimization of soil moisture sensor in tea plantation based on affinity propagation clustering algorithm. J. Agric. Eng. 35(06), 107–113 (2019)
Yao, Y., Man, X.: Spatial heterogeneity of surface soil water seal of Salix psammophila with different forest ages in Maowusu sandy land. J. Soil Water Conserv. 21(1), 112–115 (2007)
Huang, Q., Chen, L., Fu, B., et al.: Spatial pattern of soil moisture and its influencing factors in small watershed of loess hilly region. J. Nat. Resour. 20(4), 483–492 (2005)
Pan, Y., Wang, X., Su, Y., et al.: Characteristics of soil moisture change in sandy surface layer of different vegetation types. J. Soil Water Conserv. 21(5), 107–109 (2007)
Chen, S., Liu, Z.: Path coverage algorithm based on minimizing sensor moving distance. Comput. Eng. 44(06), 106–109 (2018). No. 488
Fink, W., Baker, V.R., Brooks, A.J.W., Flammia, M., Dohm, J.M., Tarbell, M.A.: Globally optimal rover traverse planning in 3D using Dijkstra’s algorithm for multi-objective deployment scenarios. Planet. Space Sci. 179, 104707 (2019)
Yuanyihang, Z.Z.: Research on floor texture recognition based on AP clustering algorithm. Microprocessor 39(06), 44–46 (2018)
Huan, R.-H., et al.: Human action recognition based on HOIRM feature fusion and AP clustering BOW. PLoS ONE 14(7), e0219910 (2019)
Liu, Z., Zhang, B., Zhuning, T.H.: Self-learning application layer DDoS detection method based on improved AP clustering algorithm. Comput. Res. Dev. 55(06), 1236–1246 (2018)
Liang, H.W., Chen, W.M., Shuai, L.I., et al.: ACO-based routing algorithm for wireless sensor networks (ARAWSN). Chin. J. Sens. Actuators 20(11), 2450–2455 (2007)
Zheng, W., Liu, S., Kou, X.: A route restoration algorithm for sensor network via ant colony optimization. J. Xi’an Jiao Tong Univ. 44(1), 83–86 (2010)
Ma, X., Cao, Z., Han, J., et al.: Routing optimization and path recovery algorithm in wireless sensor network based on improved ant colony algorithm. J. Electron. Meas. Instrum. 29(9), 1320–1327 (2015)
Tong, M., Yu, L., Zheng, L.: A study on the energy-efficient ant-based routing algorithm for wireless sensor networks. Chin. J. Sens. Actuators 24(11) (2011)
Yang, N., Fu, Q., Li, R., et al.: Application of ant colony algorithm based continuous space in optimizing irrigation regime of rice. Trans. CSAE 26(Supp. 1), 134–138 (2010). (in Chinese with English abstract)
Omran, M.G.H., Al-Sharhan, S.: Improved continuous Ant Colony Optimization algorithms for real-world engineering optimization problems. Eng. Appl. Artif. Intell. 85, 818–829 (2019)
Funding
Key Research and Development Project of Anhui Province in 2018 (1804a0702010 8), Major Science and Technology Special Plan of Anhui Province in 2017 (1703070 1049).
2016 Ministry of Agriculture Agricultural Internet of Things Technology Integration and Application Key Laboratory Open Fund (2016KL05), Key Research and Development Projects of Anhui Province in 2019 (2 01904a06020056).
The Key Support Project of Outstanding Youth Talents in Anhui Provincial University (gxyqZD2017020).
Major Natural Science Research Projects in Colleges and Universities of Jiangsu Province (18KJA520008).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Zhang, M. et al. (2020). Optimized Layout of the Soil Moisture Sensor in Tea Plantations Based on Improved Dijkstra Algorithm. In: Shen, H., Sang, Y. (eds) Parallel Architectures, Algorithms and Programming. PAAP 2019. Communications in Computer and Information Science, vol 1163. Springer, Singapore. https://doi.org/10.1007/978-981-15-2767-8_24
Download citation
DOI: https://doi.org/10.1007/978-981-15-2767-8_24
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-15-2766-1
Online ISBN: 978-981-15-2767-8
eBook Packages: Computer ScienceComputer Science (R0)